International Journal of Modelling, Identification and Control (46 papers in press)
Designing Route Guidance Strategy with Travellers Stochastic Compliance: A Bi-level Optimal Control Procedure
by Wei-li Sun, Ling-long Hu, Ping Li, Hui Wang
Identification scheme for switched linear systems in presence of bounded noise
by Abdelhak Goudjil, Mathieu Pouliquen, Eric Pigeon, Olivier Gehan
Abstract: This paper discusses an online switched linear systems identification algorithm based on a modified Outer Bounding Ellipsoid (OBE) algorithm. It alternates between estimating discrete states and system parameters updating. Theoretical analysis show the convergence result given that certain persistent excitation condition are met. This algorithm can be adapted to solve offline identification as well as MIMO system identification. Simulation results show the algorithm has improvement in both estimation error and computation time over existing methods.
Keywords: identification; switched linear systems; bounded noise.
Over-parameterisation and optimisation approaches for identification of nonlinear stochastic systems described by Hammerstein-Wiener models
by Saif Eddine Abouda, Mourad Elloumi, Yassine Koubaa, Abdessattar Chaari
Abstract: This paper proposes two iterative procedures based on over-parameterisation and optimisation approaches for identification of nonlinear systems that can be described by Hammerstein-Wiener stochastic models. In this case, the dynamic linear part of the considered system is described by the ARMAX mathematical model. The static nonlinear block is approximated by polynomial functions. The first procedure is based on a combination of the prediction error method by using the Recursive Approximated Maximum Likelihood (RAML) estimator, the Singular Value Decomposition (SVD) approach and the fuzzy techniques, in order to estimate the parameters of the considered process. As for the second procedure, it includes an appropriate representation named as Generalized Orthonormal Basis Filters (GOBF) in order to reduce the complexity of the considered system. The parametric estimation problem is formulated using the Recursive Extended Least Squares (RELS) algorithm incorporated with the SVD and fuzzy techniques, in order to segregate the coupled parameters and improve the estimation quality. The validity of the developed approaches is proved by considering a nonlinear hydraulic process simulation.
Keywords: nonlinear stochastic systems; Hammerstein-Wiener models; ARMAX model;
GOBF representation; parametric estimation; prediction error method,
SVD approach; fuzzy technique; hydraulic process simulation.
Intelligent detection method for tapping omission of internal thread based on computer vision
by Wei Ding, Qingguo Wang, Yanfang Zhao
Keywords: computer vision; tapping omission; interior thread; semantic recognition.
Thick restarted adaptive weighted block simpler GMRES algorithm
by Hongxiu Zhong, Guoliang Chen
Abstract: In this paper, an adaptive weighted block simpler GMRES algorithm is proposed for linear system with multiple right-hand sides, which can overcome the ill-conditioning difficulty of the basis generated by weighted block simpler GMRES algorithm. Moreover, a thick restarted adaptive weighted block simpler GMRES algorithm is presented to significantly reduce the products number of the matrix-vector and the consumed CPU time. Numerical examples illustrates the performance of our new algorithms.
Keywords: linear system; Block GMRES; adaptive simpler GMRES; thick restarting.
A target classification method for unmanned surface vehicle based on extreme learning machines
by Defeng Wu, Kexin Yuan, Jiadong Gu, Honggui Lin
Abstract: In the process of autonomous navigation and obstacle avoidance of unmanned surface vehicles (USV), it is important for USVs to classify maritime targets correctly and effectively. In this paper, aiming at the recognition of surface targets for autonomous navigation of USVs, three kinds of targets are mainly considered, namely ships, buoys and islands. Visual sensors are installed on the USV to acquire visual images of maritime targets, and then the images are sent to the computer for automatic recognition. The invariant moments of three kinds of target images are extracted firstly, and target feature library will be built through image invariant moments, then an Extreme Learning Machine (ELM) based neural network is trained and then used to classify and recognize the sea targets. In addition, the sea targets are classified and analyzed by Adaboost-BP. The simulation results show that the ELM based classification method proposed in this paper has a better performance for maritime targets.
Keywords: unmanned surface vehicles; visual system; extreme learning machine; target classification.
Using Self-constructing Recurrent Fuzzy Neural Networks for Identification of Nonlinear Dynamic Systems
by Qinghai Li, Ye Lin, Rui-Chang Lin
Abstract: In this paper, the self-constructing recurrent fuzzy neural network (SCRFNN) is applied for nonlinear dynamical system identification (NDSI). The SCRFNN is a novel fuzzy neural network (FNN) by adding a recurrent path in each node of the hidden layer of self-constructing fuzzy neural network, which contains two learning phases. Specifically, the structure learning is based on partition of the input space and the parameter learning is based on the supervised gradient descent method using a delta adaptation law. This simple and efficient FNN can decreases the minimum firing strength in each learning cycle and the number of hidden neurons and is able to generate a FNN with high accuracy and compact structure compared with several other neural network. The performance of SCRFNN in NDSI is further verified in simulation.
Keywords: Self-Constructing Recurrent Fuzzy Neural Network; Nonlinear Dynamic System Identification; Supervised Gradient Descent Method
An analytical approach for generating balanced gaits of a biped robot on stairs and sloping surfaces
by Ravi Kumar Mandava, Pandu R. Vundavilli
Abstract: The present paper deals with the generation of dynamically balanced walking gaits of a biped robot while moving on different terrain conditions, such as staircase and sloping surface. Here, an attempt is made to eliminate the complex inverse dynamics approach used to generate the gaits for the upper body by defining the trajectory for the movement of the hands and restricting the motion of the trunk. However, the restricted movement of the trunk is compensated by the movement of hands in the sagittal plane. The lower limbs gait has been generated by using the concept of inverse kinematics in both the sagittal and frontal planes. A provision is given in the algorithm to accommodate the variations in the dimension of the staircase and angle of slope of the terrain. Further, a study has also been conducted to investigate the influence of swing foot trajectory (that is, quadratic, cubic and fifth-order polynomial) on the balance of the robot. Once the model is developed, its performance has been benchmarked while ascending and descending the staircase and sloping surface after considering both the single and double support phases. Finally, the developed algorithms are tested on a real two-legged robot.
Keywords: biped robot; staircase; sloping surface; zero moment point.
Vision-based leader follower approach for uncertain quadrotor dynamics using feedback linearisation sliding mode control
by Walid Kh. Alqaisi, Brahim Brahimi, Jawhar Ghommam, Maarouf Saad, Vahe Nerguizian
Abstract: In this paper, a leader-follower quadrotor based on a visual system is
presented. It is assumed that the follower quadrotor is equipped with a single onboard camera for determining the position of the leader. In the following quadrotor, feedback linearisation based on sliding mode control is designed. The latter reduces complex nonlinear control solutions and highly coupled dynamic behaviour of the quadrotor. Uncertain dynamics and unexpected disturbances, such as the change of payload and wind variation, are overcome by designing a time delay estimation which helps in reducing chattering. The proposed controller uses a second order sliding mode exact differentiator to estimate the leader's velocity and acceleration. The effectiveness of the proposed system is analysed by Lyapunov function and studied by Matlab simulation.
Keywords: leader-follower; feedback linearisation sliding mode control; quadrotor;
second order sliding mode estimator; time delay estimation.
A biotech measurement scheme and software application for the level determination of persons functional reserve based fuzzy logic rules
by Riad Taha Al-kasasbeh, Nikolay Korenevskiy, Adnan Mukattash, Altin Aikeeva, Dmitry Titov, Maksim Jurievich Ilyash
Abstract: The level of apersons functional reserve is determined by a number of different indicators, such as power imbalance of meridian structures, psycho-emotional tension, intellectual and physical exhaustion, parameters of pulse and arterial pressure at the impact of the dosed intellectual and physical activities on the basis of heterogeneous fuzzy model usage. The functional reserve helps to diagnose a lot of diseases. The theoretical results obtained in the form of fuzzy models are recommended in medical practice with cofficient of confidence at least 95%.
Keywords: level of functional reserve; psycho-emotional pressure; intellectual exhaustion; physical exhaustion; heterogeneous fuzzy models.
Front vehicle detection based on parallel BLOB detection using quad-pipeline on FPGAs
by Lin Meng
Abstract: Binary large object (BLOB) detection technology is widely used for front vehicle detection in intelligent transport systems, so achieving real-time detection is very important. This paper describes real-time BLOB and front vehicle detection using quad-pipeline image processing on FPGAs. The image processing consists of reading image data from BRAM, Gaussian filtering, binarization, labelling, and BLOB analysis. We propose simplified labeling to detect BLOBs in parallel. The image is divided into four parts, and two lines are temporarily labelled at first in parallel. Then, the four pipelines temporarily label each corresponding line in parallel. Connected information of temporarily labels between two neighbouring lines is kept in four look up tables (LUTs), and they are integrated into one. In simplified labelling, temporary labels are not revised, and connected labels are kept in a BLOB table. A system for front vehicle detection is implemented based on parallel BLOB detection using quad-pipeline. Tail lights are detected using two pipelines, and white lines are detected using the other two pipelines. If two red BLOBs of tail lights are detected between the two white line BLOBs, we can determine a vehicle is up ahead. The experimental results showed that the quad-pipeline system can detect BLOBs in 31.6 us on Virtex-5, which is 3.55 times and 1.83 times faster than the single-pipeline system and the dual-pipeline one, respectively. The quad-pipeline system can detect a vehicle up ahead in 124.8 us on Artix7, which is 1.91 times faster than the dual-pipeline one. Thus, parallel BLOB detection and front vehicle detection have a nearly linear speedup in the quad-pipeline system.
Keywords: BLOB detection; front vehicle detection; quad-pipeline; simplified labeling; Scalability.
Identification of nonlinear systems having discontinuous nonlinearity
by Adil Brouri
Abstract: This paper deals with the identification of nonlinear systems. Presently, the nonlinear system is described by Wiener model. The linear element is nonparametric and may be of unknown structure. The nonlinear part is allowed to be discontinuous or of hard type and, noninvertible. A two-stage identification method is developed to get a set of points of the nonlinear part and estimates of the linear dynamic element. This method is based on simple geometric analysis and involves easily generated excitation signals.
Keywords: nonlinear systems; nonlinear systems identification; discontinuous nonlinearity; nonparametric linear block; hard nonlinearity; Wiener systems.
U-model enhanced MIMO decoupling control of thickness and plate type of cold rolling temper mill
by Pengwei Li, Weicun Zhang
Abstract: This paper presents a new design procedure for control of thickness and plate type of cold rolling temper mill. A novel concept for control system design, U-model-based design methodology with expanded Multi-Input and Multi-Output (MIMO) formulation for this application, makes separation of specifying closed loop performance-controller from process models. In contrast to classical and methodologies, this takes two parallel formations: 1) designs an invariant virtual controller with specified closed loop transfer function in a feedback control loop; 2) determines real controller output by resolving the inverse of the plant U-model. Further, this study analyses some of the associated properties for assurance and guidance for ongoing theoretical development and applications. To validate the design procedure, it takes several computational experiments to generate simulation results, the bench tests show the efficiency and effectiveness of the proposed MIMO U-model-based control system design.
Keywords: cold rolling temper mill; plate type; plate thickness; U-model; U-control; model independent control system design.
A new chaotic dynamical system with a hyperbolic curve of rest points, its complete synchronisation via integral sliding mode control and circuit design
by Sundarapandian Vaidyanathan, Aceng Sambas, Chang-Hua Lien, Babatunde A. Idowu
Abstract: A new 3-D dynamical system exhibiting chaos is introduced in this work. The proposed nonlinear plant with chaos has a hyperbola of rest points. Thus, the new nonlinear plant exhibits hidden attractors. A detailed dynamic analysis of the new nonlinear plant using bifurcation diagrams is described. The new nonlinear plant shows multi-stability and coexisting attractors. The synchronisation of the new nonlinear plant with itself is achieved using Integral Sliding Mode Control (ISMC). Finally, a circuit model using MultiSim of the new 3-D nonlinear plant with chaos is carried out for practical use.
Keywords: chaos; chaotic systems; circuit design; synchronisation.
Towards improving constrained robust model predictive control with free control moves
by Xianghua Ma, Shuang Fang
Abstract: An existing robust model predictive control (RMPC) algorithm parameterises the infinite horizon control moves into a set of free control moves over a fixed horizon and a state feedback law in the terminal region. This paper is towards further improving the feasibility and optimality of this kind of RMPC. The improvement is by introducing an extended parameter-dependent Lyapunov function to substitute the original parameter-dependent Lyapunov function. Correspondingly, the terminal-weighting matrix is extended parameter-dependent, the local control law is a non-parallel distributed compensation, and the terminal constraint set is an intersection of more ellipsoids. The new technique is demonstrated by a simulation example.
Keywords: model predictive control; polytopic description; parameter-dependent Lyapunov function; non-parallel distributed compensation law; linear matrix inequality.
Modelling of multi-timescale demand response for power markets
by Dan Zhou, Huiwen Dai
Abstract: Demand response as an important part of smart grid makes the consumers participate in market operation that can lead to the benefit of both the utility and the consumers. In this paper, a multi-timescale demand response model that accounts for short-term and long-term demand characteristics is established. The short-term demand response and long-term demand response are combined by the electricity price elasticity matrix. The objective of optimisation is to minimise the peak-valley load difference in the total load，and particle swarm optimisation is adopt as the optimisation algorithm. The simulation results of IEEE-30 node distribution network show the feasibility and effectiveness of the proposed demand response model and pricing strategy. The research of this paper will provide technical support for the application of demand response mechanism after the new electric power reform.
Keywords: demand response; power demand elasticity matrix; multi-timescale model; peak-valley difference optimisation; pricing strategy.
Application of multivariate adaptive regression in soft-sensing and control of UCG
by Jan Kacur, Marek Laciak, Milan Durdan, Patrik Flegner
Abstract: The technology of underground coal gasification (UCG) is still under development and provides an alternative to conventional coal mining. Process monitoring is the necessary part of a complex control system because it provides essential information for control level. Monitoring and control improve the behaviour and effectiveness of the technological process. This paper introduces a novel approach to soft-sensing in UCG based on multivariate adaptive regression splines (MARS). This technique can support monitoring the process variable that is inaccessible for standard measuring hardware. The MARS method was applied for modelling of underground temperature from the syngas composition. The paper also presents advanced approaches to control based on an adaptive regression model. Proposed control can increase or maintain the syngas calorific value during UCG operation. Proposed methods have shown interesting results and can be applied to industrial automation devices or implemented as support algorithms for the monitoring system. Methods were verified in experimental coal gasification on an ex-situ reactor.
Keywords: UCG; monitoring; control; soft-sensing; regression; adaptation; gasification; coal.
A welding manipulator path planning method combining reinforcement learning and intelligent optimisation algorithm
by Junhua Zhang, Lianglun Cheng, Tao Wang, Wenya Xia, Dejun Yan
Abstract: We present DDPG-AACO, a hierarchical method for welding manipulator path planning of complex components welding tasks that combines reinforcement learning (RL) with the intelligent optimisation algorithm. The RL agent, trained with the deep deterministic policy gradient (DDPG), learns local path planning policies that control the welding manipulator to safely move between two welding seam endpoints. Next, on a distance matrix constructed by the lengths of local paths between every two welding seam endpoints,the adaptive ant colony optimisation (AACO) algorithm with artificially changed value of pheromones is adopted to realise the global path planning that the welding manipulator traverses all welding seams under a welding direction constraint and has the shortest path length. The simulation results show the effectiveness of the method. The DDPG is better than the deep Q-learning based methods when performing local path planning. Moreover, the length of the global path with direction constraint can converge to the minimum.
Keywords: complex component; welding manipulator; path planning; direction constraint; DDPG; AACO.
Active disturbance rejection control of three-phase grid-connected photovoltaic systems
by Hezheng Li, Jing Bai, Shunshoku Kanae, Yanxi Li, Lin Yue
Abstract: In this paper, a three-phase grid-connected photovoltaic system based on active disturbance rejection controller (ARDC) is proposed to solve the problem of voltage instability that is caused by many internal uncertainties and external disturbances. A tracking differentiator is used to arrange the transient process to reduce the initial error, which solves the problem of large overshoot. The extended state observer is used to estimate the internal and external total disturbances in real-time, and adopts a linearisation technique via a dynamic compensation process, which can reject internal and external disturbances effectively. The simulation experiments show that ADRC has better performance than PI controller in reducing power loss and rejecting disturbances.
Keywords: three-phase grid-connected photovoltaic system; tracking differentiator; extended state observer; internal and external disturbances.
Incentive Stackelberg game based path planning for payload transportation with two quadrotors
by Dongbing Gu
Abstract: This paper proposes a path planning approach to the task of transporting a cable-suspended payload with two quadrotors. The transporting system is provided with a desired payload path which is predefined by the user without the consideration of system dynamics. The path planning problem is to find the optimal paths for the quadrotors, which can make the payload to move along the closest path to the desired one under the constraint of system dynamics. This problem is formulated as an incentive dynamic Stackelberg game where one quadrotor plays a leader role while the other plays a follower role, and each of them is defined with an individual cost function. By using an incentive strategy, a game solution can be found based on the linearized dynamic system. The benefit of using two cost functions over one cost function is that the whole system operates more efficiently in terms of individual costs. In the paper, the nonlinear system dynamics is derived first, and then linearized into a linear system. Then an incentive strategy is applied to the linearized system to find the optimal paths for the quadrotors. Some simulation results are provided to show the performance of proposed path planning approach. The planned paths are also used in real experiments where the paths are tracked by two quadrotors using PID controllers.
Keywords: UAV path planning; incentive dynamic Stackelberg game; aerial transportation; multiple quadrotor systems.
Interval prediction of oscillating time series based on grey system modelling
by Gaofei Xu, Xiaohui Wang, Zhigang Li, Yang Zhao
Abstract: This paper presents an interval prediction algorithm based on grey system modelling, which is proposed for the forecasting of strong-oscillation time series with small samples. In the proposed algorithm, the upper and lower envelope of an oscillating sequence is obtained through cubic spline interpolation, and distance between the envelope and the fitted sequence derived from grey system model is dynamically expanded according to the oscillation intensity. After that, prediction value of the envelope distance sequence is calculated, and adjusted adaptively based on the new information priority principle. Finally, the interval prediction result is obtained. To verify the performance of the algorithm, five application cases from different fields were adopted. Compared with five representative algorithms in the recently related field, the proposed algorithm has distinct advantages in the prediction of small-sample strong-oscillation time series.
Keywords: interval prediction; oscillating time series; small samples learning; spline interpolation; grey system modelling.
An improved genetic algorithm-based robot path planning method without collision in confined workspace
by Wenyu Tam, Lianglun Cheng, Tao Wang, Wenya Xia, Liangzhou Chen
Abstract: The manufacturing process of ships and marine engineering equipment involves a large number of welding processes of complex components. It is difficult for industrial robots to plan the welding path because of the complex distribution of the weld seams and environmental obstacles as well as the specific technological requirements of arc welding process. In this paper, the typical welding components are numerically modelled. Considering the joint collision-free constraint in the moving process of a robot and the welding technological constraints (e.g., welding direction) of some special welding seams, an improved Re-GA algorithm for planning robot welding operation path in complex workspace is proposed, and an optimal welding path can be obtained. The simulation results show that the Re-GA has reliable optimization ability in the case of a small population size.
Keywords: Re-GA; complex components; pose match; directed path planning.
Fuzzy H_infinity filtering for nonlinear 2-D systems in the Roesser model
by Khalid Badie, Mohammed Alfidi, Zakaria Chalh
Abstract: This study focuses on the H_infinity filtering problem for two-dimensional (2-D) discrete Takagi-Sugeno (T-S) fuzzy systems in the Roesser model. The objective is to design a stable filter guaranteeing the asymptotic stability and a prescribed H_infinity performance of the filtering error system. By using a new structure of the fuzzy Lyapunov function, and some analysis techniques, the stability and a prescribed H_infinity performance index are guaranteed for the overall filtering-error system, such that the coupling between the Lyapunov matrix and the system matrices is removed. In addition, sufficient conditions for the existence of such a filter are established in term of linear matrix inequalities (LMIs). When these LMIs are feasible, the explicit expression of the desired filter can be characterized. An illustrative example is presented to demonstrate the effectiveness of the developed results.
Keywords: H_infinity filtering; two-dimensional fuzzy systems; Takagi-Sugeno model; linear matrix inequalities.
Robustness of current source inverter based on H? control
by Yu Xu, Yang Li, Hezheng Li, Shunshoku Kanae, Jing Bai
Abstract: Aiming at the problem that the control performance of three-phase current-source inverter(CSI) is affected by load parameters and external disturbances easily, a robust H? control strategy is proposed firstly in this paper. A mathematical model of three-phase CSI is established. According to the model of CSI with uncertain parameters, the state feedback H? controller is designed and the feedback control laws is obtained. A robust H? controller based on CSI control system is designed. Finally, the effectiveness and feasibility of the scheme are verified by simulation.
Keywords: current source inverter; H? control;robustness.
A new robust adaptive fuzzy synergetic control for nonlinear systems with an application to an inverted pendulum
by Abderazak Saidi, Farid Naceri, Sundarapandian Vaidyanathan
Abstract: This paper deals with a nonlinear adaptive control design based on synergetic control, which also uses fuzzy systems to approximate the dynamics of non-linear systems. The stability of the closed-loop system is ensured by the Lyapunov synthesis in the sense that all the signals are bounded, and the controller parameters adjusted by adaptation laws. The proposed algorithm is applied to an inverted pendulum to track a sinusoidal reference trajectory. Simulations and discussion are presented to illustrate the new robust adaptive fuzzy synergetic control presented in this work.
Keywords: adaptive control; fuzzy adaptive control; nonlinear systems; synergetic control.
Nonlinear interconnected high gain observer for series-parallel resonant DC/DC converter
by Ouadia El Maguiri
Abstract: We are considering the problem of state observation in series-parallel resonant converters (LCC). This is a crucial issue in (LCC) output voltage control as the control model is nonlinear and involves nonphysical state variables, namely real and imaginary parts of complex electrical variables. An interconnected high gain observer is designed to get online estimates of these states. The observer is shown to be globally asymptotically stable. The global stability of the observer is analytically treated using the lyapunov theory; finally we present numerical simulation to illustrate the performance of the suggested approach.
Keywords: series-parallel resonant converter; averaging approximation; state variables observation; lyapunov theory; interconnected high gain observer.
Real-time sensor fault diagnosis method for closed-loop system based on dynamic trend
by WenBo Na, Yu Gao, TuoHong Zhu, XiuTong Zheng
Abstract: In order to solve the common problems of sensor fault detection, fault setting and fault location in the first-order closed-loop control system, a static fault diagnosis model based on the real-time trend of dynamic data flow is constructed. Aiming at the common fixed-value system and servo system in the first-order closed-loop control system, a data processing model based on sliding window is designed by collecting and analysing a large amount of real-time data, and the fault is isolated in the window based on the calibration parameters. Then, the adaptive threshold binarisation method is used to calculate the fault vector, and then the analytic model of fault location is obtained by the linear regression method. Finally, the feasibility and validity of closed-loop sensor fault diagnosis method based on dynamic data flow trend are verified by on-line simulation of Complex Process System Innovation Experimental Platform based on OPC communication technology.
Keywords: dynamic trend; sliding window; sensor; fault diagnosis; closed-loop system.
A novel control structure for a pioneer mobile robot: simulation and practical implementation
by Lauhic Ndong Mezui, Donatien Nganga-Kouya, Maarouf Saad, Francis Okou, Brice Hernandez
Abstract: This paper presents a new control structure for a non-holonomic robot. It consists of two controllers in series: a nonlinear trajectory tracking controller based on robot kinematics and a multivariable quasi-linear controller based on robot dynamics. The Lyapunov theory is used to obtain the nonlinear control law and a frequency domain design approach is employed to find the transfer functions of the quasi-linear controller. The stability and sensitivity analysis criteria are considered to increase the performance of the robot. The stabilization time and overshoot are considerably reduced. The proposed controller is simple and insensitive to external disturbances. Simulation and real-time test results are used to evaluate the effectiveness of this new control structure.
Keywords: mobile robot; non-holonomic; stability; sensitivity; robustness; transmission; trajectory tracking; quasi-linear controller.
Flexible flow shop scheduling with variable processing times based on differential shuffled frog leaping algorithm
by Zhijun Gao, Jiayu Peng, Meiqi Jia, Zhonghua Han
Abstract: In the problem of flexible flow shop scheduling with variable processing times, the change of processing speed often affects product quality and causes fluctuations in capacity, which makes it difficult to solve the scheduling problem. And these production lines widely exist in the actual production. Therefore, in the light of the Flexible Flow-shop Scheduling Problem with Variable Processing Times (FFSP-VPT), the FFSP-VPT mathematical model is established. The improved differential shuffled frog leaping algorithm is served as the global optimization algorithm, and the difference operator is introduced as a new location update strategy. Whats more, the crossover operation is taken to maintain the diversity of the population. It overcomes the shortcomings of the adaptive shuffled frog leaping algorithm which is easy to fall into local optimum and converges slowly. On the premise of ensuring the global optimisation goal, the two-stage coding method is used to determine the online sequence of the job and processing speed of the stage with variable processing times, so as to get much more effective solution to such problems. The simulation experiments confirm the improvement of the shuffled frog leaping algorithm in global search ability and its effectiveness in solving the flexible flow shop scheduling problem with variable processing times.
Keywords: flexible flow shop; variable processing times; differential shuffled frog leaping algorithm; two-stage coding.
Advanced Control of Three-Phase Battery Electric Vehicle Charger with V2X technology
by Aziz Rachid, Hassan El Fadil, Abdellah Lassioui, Fouad Giri
Abstract: The bidirectional electric vehicle (EV) charging, so-called vehicle-to-everything (V2X), has a double interest: economic and ecological. It promotes low carbon and cheap electricity because it’s readily available. To ensure this functionality, electric vehicles use bidirectional chargers with single or three-phase topologies. One of the main features of three-phase structures is its capability to avoid the problem of oscillating energy between the single?phase power grid and the dc?bus. In this paper, the control of bidirectional three-phase battery electric vehicle charger with vehicle-to-grid functionality is addressed. The charger structure is composed of two power converters: a bidirectional three-phase ac-dc power converter and a bidirectional dc-dc power converter associated with an EV battery. The principal control objectives are the following: (i) Estimation of the battery state of charge; (ii) Unity power factor during the grid-to-vehicle operating mode; (iii) Adjusting the reactive power injected into the power grid during vehicle-to-grid operating mode; (iv) dc-bus voltage regulation; and (v) Ensuring and safeguarding the battery charging and discharging process. To this end and based on the system modelling into dq coordinates, a nonlinear backstepping controller is designed. The fact is that the battery state of charge is not accessible to measurement. Hence, a partial nonlinear observer is designed to estimate all state variables on the battery-side. The performances of the proposed output feedback controller are highlighted using theoretical analysis and numerical simulations, which clearly illustrate that all control objectives are achieved.
Keywords: Bidirectional three-phase ac-dc converter, Bidirectional half-bridge dc-dc converter, BEV charger, Reactive power compensation, Nonlinear output feedback control, Nonlinear observer, V2X charger.
Adaptive Neuro-Fuzzy System based Maximum Power Point Tracking for Stand-alone Photovoltaic system
by Ahmad Azar, Ali Malek, Toufik Bakir , Arezki Fekik, Ahmad Taher Azar, Khaled Mohamad Almustafa , Dallila Hocine, El-Bay Bourennane
Abstract: The Maximum Power Point Tracker (MPPT) plays a very important role to extract the maximum power of the photovoltaic (PV) system by ensuring its optimal production under sunshine and temperature variations. This study presents an algorithm based MPPT named an Adaptive Neuro Fuzzy Inference System (ANFIS) which is built with the combination of the Artificial Neural Network (ANN) and the Fuzzy Logic Controller (FLC). The efficiency of the ANFIS algorithm is tested under Matlab/Simulink and compared with the fixed step conventional Perturb & Observe (P&O) and the gradient descent techniques under temperature and irradiance change. The obtained results showed a significant improvement in performances of the PV system using the ANFIS-MPPT technique which provides also faster convergence, stability in steady state, less oscillations around the MPP and higher efficiency to track the maximum power from the PV system compared to other techniques under different operating conditions.
Keywords: Photovoltaic system; Perturb & Observe (P&O); Maximum Power Point Tracking (MPPT); Gradient descent; Adaptive Neuro Fuzzy Inference System (ANFIS).
A new hyperjerk dynamical system with hyperchaotic attractor and two saddle-focus rest points exhibiting Hopf bifurcations, its hyperchaos synchronisation and circuit implementation
by Sundarapandian Vaidyanathan, Irene M. Moroz, Aceng Sambas
Abstract: A new 4-D dynamical hyperjerk system with hyperchaos is reported in this work. The proposed nonlinear mechanical system with hyperchaos has two saddle-focus rest points exhibiting Hopf bifurcations. A detailed bifurcation analysis of the new hyperjerk plant with theory and simulations is discussed. As a control application, an integral sliding mode controller has been designed for the global hyperchaos synchronisation of the new hyperjerk system with itself. Finally, a circuit model using MultiSim of the new hyperjerk system with hyperchaos is designed for practical implementation.
Keywords: Hyperchaos; hyperjerk; sliding mode control; synchronisation; circuit design
Special Issue on: ISMIC 2018 Data-driven Modelling and Intelligent Computation
Parameter estimation algorithm for d-step time delay systems
by Ya Gu, Peiyi Zhu, Xiangli Li, Jianfei Gu
Abstract: This article proposes the methods of parameter estimation and state estimation to calculate state space systems with delay. Associating the properties of linear conversion and shift operators, the state space model can be equal to the standard state space model, which can then be converted into the recognition model. The proposed stochastic gradient method is brought to calculate the system and converge to the recursive least squares estimates for state space models. Finally, one example is proposed to certify the theorems of this paper.
Keywords: state estimation; stochastic gradient algorithm; time delay.
The investigation of an improved ultrasonic tomography reconstruction method for bubble particle identification
by Jianfei Gu, Jicheng Liu, Yongxin Chou
Abstract: To improve the accuracy of particle image reconstruction, a new image reconstruction method, Improved Binary Back Projection Algorithm using Triangular-criteria (IBBPAT) for in-line Ultrasound Processing Tomography (UPT), is presented. An ultrasonic transducer array consisting of eight transducers working as both transmitters and receivers, and 16 ultrasonic transducers working only as receivers, is adopted in the simulation. The transducers that also function as transmitters emit fan-shaped ultrasonic waves. Based on Multiphysics software (COMSOL Inc., Palo Alto, CA), signals scattering a cylindrical wave by different distribution of bubble particles are simulated. The results are entered in to IBBPAT for bubble reconstruction, and then the position and size of each bubble particle are accurately reconstructed. As indicated by the final results, Spatial Image Error (SIE) of IBBPAT is clearly less than that of Binary Back Projection Algorithm (BBPA).
Keywords: particle image reconstruction; ultrasound processing tomography; binary back projection algorithm; numerical simulation.
Human behaviour recognition algorithm based on improved DMM and Fisher coding
by Feng Wei, Zhang Jiliang, Peng Li
Abstract: Human behaviour recognition has become a key technology in intelligent sensing, location and tracking tasks. In view of the different speed of action execution and DMM loss of time dimension information, this paper proposes a human action recognition method based on improved DMM and Fisher coding. First, in consideration of the different action speeds of long and short video, this paper adopts two different video segmentation strategies. Second, in order to make video-based DMM contain more time dimension information, this paper proposes an improved DMM; then, in order to better express the texture information of the image, this paper improves the extraction of LBP features by DMM. Finally, owing to the different feature lengths and high dimensions obtained by the long and short video, this paper adopts the Fisher vector for feature encoding and combines SVM to complete the action recognition. In the public action recognition database MSRAction3D and gesture recognition database MSRGesture3D, the accuracy rate of the algorithm is 96.25% and 96.00%, respectively, and it has higher recognition rate than many existing algorithms.
Keywords: human behavior recognition; depth motion map; video segmentation; Fisher coding.
Online soft sensing method based on improved weighted Gaussian model
by Che Xiaoqing, Xiong Weili
Abstract: Aiming at the problems of delay, nonlinearity and multi-mode and noise pollution in chemical processes, a novel soft-sensor modelling method based on improved weighted Gaussian model is proposed. The moving gray relational analysis method is used to extract the process delay information, and the modelling dataset is reconstructed to improve the accuracy of the model. The cumulative similarity factor is introduced into the selection rules of the model training set to improve the real-time performance of the model. When the new query sample arrives, an adaptive similarity threshold is used to determine criteria of updating local model of the current operating point, so as to reduce the model update frequency. The improved modelling method is applied to the debutaniser column process. The simulation results show that the model has high precision and real-time performance.
Keywords: variable time delay; moving gray relational analysis; weighted Gaussian model; cumulative similarity factor.
Multi-mode process monitoring based on multi-block information extraction PCA method with Local neighborhood standardisation
by Bingbin Gu, Weili Xiong
Abstract: In order to solve the problem of multi-mode process data in complex industrial processes, a multi-mode industrial process fault monitoring method based on multi-block information extraction PCA with local neighborhood standardisation is proposed. Firstly, define the cumulative observation information and rate of change information of the process variable. Then extract the two kinds of information from the existing observation information, and obtain the data sub-blocks of the three kinds of information. Secondly, the three data sets are separately standardised by the local neighborhood standardisation method. Next the Puata criteria are employed to eliminate singular points. Accordingly, three PCA models were built based on the three information datasets obtained, and each PCA model will get a monitoring result. Finally, the Bayesian inference method is used to fuse the results of the three models to obtain a final BIC monitoring index. The effectiveness and feasibility of the proposed method are proved by a numerical example and applications in Tennessee-Eastman (TE) process monitoring.
Keywords: principal component analysis; information extraction; multi-block modelling; multi-mode process; local neighborhood standardization.
Research on reconfigurable control for a hovering PVTOL aircraft
by Xinli Xu, Chunwei Zhang, Huosheng Hu
Abstract: This paper presents a novel reconfigurable control method for the planar vertical take-off and landing (PVTOL) aircraft when actuator faults occur. According to the position subsystem within the multivariable coupling, and the series between subsystems of position and attitude, a active disturbance rejection controller (ADRC) is used to counteract the adverse effects when actuator faults occur. The controller is cascade and ensures the input value of the controlled system can be tracked accurately. The coordinate transformation method is used for model decoupling due to the severe coupling. In addition, the Taylor differentiator is designed to improve the control precision based on the detailed research for tracking differentiator. The stability and safety of the aircraft is much improved in the event of actuator faults. Finally, the simulation results are given to show the effectiveness and performance of the developed method.
Keywords: reconfigurable control; PVTOL; ADRC; coordinate transformation; actuator faults; Taylor differentiator.
Multi-innovation parameter and state estimation for multivariable state space systems
by Xuehai Wang, Fang Zhu, Fenglin Huang
Abstract: This work considers the modelling and estimation problem of multivariable state space systems. Based on the observer canonical form, the identification model is derived and a combined state and multi-innovation estimation algorithm is presented by means of the Kalman filter principle. The efficacy of the algorithm is verified by a simulation example.
Keywords: multi-innovation; multivariable system; Kalman filter.
Research on passivity-based control strategy of three-phase current source inverter based on interconnection and damping assignment
by Shuo Zhan, Jing Bai, Chao Chao Li, Lin Yue
Abstract: In view of the facts that switching frequency of the high power current source inverter (CSI) is limited and the load parameter of the system is variable, and moreover the performance of output is affected, an interconnection and damping assignment of passivity-based control (IDA-PBC) for a three-phase CSI is proposed. The lumped-parameter model of the three-phase CSI with independent storage unit is established, and its port-controlled Hamilton (PCH) model in Park coordinate is proposed. The control rate of the closed-loop system is obtained by solving the parametric partial differential equation based on interconnection and damping assignment. The controller is designed, and the stability of system is analysed by Lyapunov function. The simulation and experiment results show that the inverter can output a good sinusoidal voltage, which has strong anti-interference ability to the load disturbance. The stability and robustness of the system are also improved.
Keywords: current source inverter; port-controlled Hamiltonian system; interconnection and damping assignment; passivity-based control.
The gradient and the Newton iterative modelling methods for an operational amplifier circuit
by Ling Xu
Abstract: In order to provide a new method to identify the mathematical model of circuit systems, this paper studies modelling methods by combining the iterative strategy with the nonlinear optimisation. When we analyse a complicated circuit, we only need to find the relationship between the input and the output and do not need to concern the specific components, which can be solved by system identification. With this purpose, this paper develops iterative modelling methods to construct the transfer function for an amplifier circuit system that can be described by the transfer function model of a first-order inertial system. For obtaining the transfer function model, the impulse response observed data are employed to design the objective function regarding the time constant and the gain of the first-order inertial system. In light of the nonlinear characteristic of the system output, the gradient optimisation and the Newton optimisation are adopted to minimise the objective function. Finally, the simulation experiment is carried out to test the performance of the developed modelling methods.
Keywords: parameter estimation; iterative identification; gradient optimisation; Newton optimisation; transfer function.
A modified model decomposition identification for bilinear-in-parameter systems
by Huibo Chen, Jiangbo Fan, Jing Li
Abstract: The so-called bilinear-in-parameter models are usually derived from the block-oriented nonlinear models and identified by different methods. Inspired by the model decomposition-based identification technique, this paper develops a recursive least squares algorithm to estimate the model parameters and obtain a global convergence, as shown by a simulation example.
Keywords: identification; bilinear model; least squares.
Steady flight of miniature fixed-wing unmanned aerial vehicle flocking by using biological algorithm
by Yongnan Jia, Qing Li, Weicun Zhang
Abstract: This paper focuses on solving the cohesive flocking problem from the biological point of view. Twelve variables are adopted to draw the kinematic characteristics of the fixed-wing UAV. A weighted and undirected graph is applied to describe the time-variant and distance-related interaction relationship among the UAVs. Based on the proposed model and the communication mechanism, a distributed cooperation approach is designed to force groups of miniature fixed-wing UAVs to be capable of collaboratively accomplishing a predefined task like a flock of birds. During the evolutionary process, there are three constraint conditions to be considered. The first one is that each UAV flies under a small roll angle and a small pitch angle. Second, one forward speed is necessary for the flight of each fixed-wing UAV. The last constraint condition is that the shorter the adjustment time, the better within the error margin of steady-state value. Three constraint conditions are skillfully taken as evaluation criteria to determine the communication network's coupling strength. Numerical simulations are provided to validate the feasibility of the proposed approach.
Keywords: steady flight; fixed-wing unmanned aerial vehicle; cohesive flocking; biological algorithm.
Sensor fault signal reconstruction based on sliding mode observer for flight control systems
by Rui Wang, Chengjie Wang, Hui Sun, Zengqiang Chen, Yigang Sun
Abstract: The sensor-fault detection for linear systems with bounded unknown input disturbance is investigated in this paper. By non-singular linear transformation, the sensor-faults are converted equivalently into actuator failures. The residual generation is proposed, which is robust to disturbance and sensitive to sensor faults. A sliding mode observer is further designed to give the estimation of residuals. The sensor faults can be detected and reconstructed by using the estimation of residuals. For typical sensor failure modes, the simulation results about the longitudinal model of the aircraft show the effectiveness of the proposed method.
Keywords: sensor fault; sliding mode observer; fault detection; signal reconstruction.
Recursive least squares algorithm and stochastic gradient algorithm for feedback nonlinear equation-error systems
by Guanglei Song, Ling Xu, Feng Ding
Abstract: Many industrial systems exhibit nonlinear characteristics. Generally, the structure of the system is taken by feedback closed-loop for the purpose of realising the automatic control of industrial process. Therefore, industrial systems are closed-loop feedback nonlinear systems with complicated structures. The mathematical models of systems provide the support and basis for the design of the control system and the better control performance. However, it is hard to determine the models of closed-loop feedback nonlinear systems owing to the complex structures. The goal of this study is to develop an identification way for a feedback nonlinear system including a forward channel and a feedback channel, where the forward channel is described by a controlled autoregressive model and the feedback channel takes the form of a static nonlinear function. By taking advantage of the least squares optimisation, a recursive least squares algorithm is established and shows its good performance to solve the identification problem for the feedback nonlinear system.
Keywords: parameter estimation; recursive identification; least squares; stochastic gradient; nonlinear system; feedback system.
Modelling arterial blood pressure waveforms for extreme bradycardia and tachycardia by curve fitting with Gaussian functions
by Yongxin Chou, Ya Gu, Jicheng Liu, Xufeng Huang, Jiajun Lin
Abstract: Arterial blood pressure (ABP) signal contains abundant information about heart beat rhythm and hemodynamic changes which can be employed to predict bradycardia and tachycardia. Thus, a waveforms modelling method based on curve fitting is proposed to extract some significant difference between bradycardia and tachycardia. First, the ABP signal is pre-processed and is split into a series of single-period waveforms. Then, a single-period ABP waveform model is proposed to describe the change of linear trend and waveform, and a nonlinear least squares method is employed to compute the parameters of the model. The bradycardia and tachycardia data from 2015 PhysioNet/CinC Challenge are engaged as experimental data. The results show that there are significant differences for many of the model parameters between bradycardia and tachycardia.
Keywords: arterial blood pressure; curve fitting; modelling; bradycardia; tachycardia.