International Journal of Automation and Control (26 papers in press)
Robust Attitude Stabilization Reactive Control of Quadruped Robot under Load Disturbance
by Xiaolu Zhu, Jinxiao Wan, Wei Xu, Chuan Zhou
Abstract: In order to solve the walking stability problem of quadruped robot under load disturbance, a robust attitude stabilization reactive control strategy based on ZMP (zero-moment point) stability criterion is proposed. This control strategy is composed of two layers, including an adaptive gait planner based on ZMP criterion at the upper planning layer and a robust joint angle tracking controller at the bottom layer. The adaptive gait planner calculates the desired ZMP position in real time by the robot body dynamics, which can adaptively plan the foot-end trajectory of the quadruped robot to ensure the attitude stability of the robot under load disturbance. The robust joint angle tracking controller is a sliding-mode tracking controller with disturbance observer, which calculates the disturbance and compensates for it through the complete dynamics of the quadruped robot with disturbance term. The robust joint angle tracking controller can enable quadruped robot to maintain robust joint angle tracking under the load disturbance. Finally, the effectiveness of the proposed reactive control strategy is verified by co-simulation of ADAMS and MATLAB.
Keywords: quadruped robot; load disturbance; zero-moment point (ZMP); disturbance observer; sliding-mode control; reactive control.
A Decoupler Assisted Optimized Controller Design for Greenhouse System
by SHRIJI GANDHI, Ravi Gandhi, Santosh Kakad, Manish Thakker
Abstract: This paper presents the application of the feedback feed-forward linearization integrated with the optimized PID controller for the greenhouse system (GHS). The GHS is a group of MIMO systems with highly coupled, disturbed, and nonlinear dynamics. Using the proposed approach, initially, the nonlinear MIMO system is separated into two independent SISO systems (i.e., temperature loop and humidity loop). After the decoupling, the separated loops are regulated by the Genetic Algorithm based optimized PID controllers. The extensive simulation study is carried out for the stabilizing and the tracking control operations for both the loops of the GHS in the presence of various initial conditions, various load disturbances (e.g. Canopy temperature, external temperature, external humidity, and solar radiation). Finally, the performance robustness of the proposed control structure is compared with the linear quadratic regulator(LQR) controller, conventional PID controller, and the Ziegler-Nichols tuned PID controller under diversified scenarios.
Keywords: Feedback feed-forward linearization; Greenhouse system; PID controller; Genetic Algorithm.
Novel Design of PID Controllers for Minimum and Non-Minimum Phase Time Delay Processes for Enhanced Performance
by Chandra Mohan Goud Ediga, Chidambaram M, Seshagiri Rao A
Abstract: A new technique is proposed for the design of PID controllers for minimum and non-minimum phase (NMP) stable first-order and second-order processes. The idea is to make the open-loop transfer function model equal to an integrating model with which the closed loop consists of first order dynamics. The resulting controller settings are functions of the tuning factor ? and process model parameters. The tuning parameter is selected based on the stability and robustness margins. Different case studies for simulation and experimental implementation are considered and the closed loop responses are examined. The proposed technique gives enhanced closed-loop performance for both set point and regulatory problems compared to the current techniques in both perfect and perturbed model conditions. Performance metrics such as integral error, control variation, stability margins, and sensitivity peak are compared and noted that the enhanced responses are achieved with the proposed simple method which has straight forward simple analytical relations for design of the controller.
Keywords: FOPTD process; SOPTD process; PID control; Total variation; Maximum sensitivity.
Adaptive differential evolution with linear population reduction for parameter estimation of solar cell models
by Zhen Yan, Wenyin Gong, Shuijia Li
Abstract: Parameter estimation of solar cell models is an important part of photovoltaic power generation system. However, it is still a challenging problem. In this study, an adaptive differential evolution with linear population reduction, called LRJADE, is developed to accurately estimate solar cell models parameters. In LRJADE, the linear population reduction strategy is employed to accelerate convergence speed. Additionally, the crossover rate repairing is also used. The performance of proposed LRJADE is verified by 13 benchmark functions and two solar cell model parameter estimation problems. Simulated results show that LRJADE not only obtains promising results in benchmark functions, but also achieves the very accurate solutions to solar cell model parameter estimation problems.
Keywords: Parameter estimation; solar cell models; differential evolution; adaptation.
Intelligent Optimal Hybrid Motion/Force Control of Constrained Robot Manipulator
by Komal Rani, Naveen Kumar
Abstract: This paper presents a neural network based optimal control approach
for hybrid motion/force control problem of robot manipulators in the presence
of structured and unstructured uncertainties. The quadratic optimization with
sliding mode control and neural network is utilized to propose an optimal hybrid
motion/force control of robotic manipulators. Firstly, the dynamics of robot
manipulator is reduced into state-space form describing the constrained and
unconstrained motion separately. Then the optimal hybrid motion/force control
scheme is derived utilizing the optimization of Hamilton Jacobi Bellman (HJB)
equation and sliding mode control approach. The structured and unstructured
uncertainties of the system are compensated using radial basis function neural
network and adaptive compensator. The radial basis function neural network
approximates the unknown dynamics and adaptive compensator is used to
estimate the bounds on neural network approximation error and the unstructured
uncertainties of the system. The neural networks are trained in online manner
using weight update algorithms derived with Lyapunov approach to guarantee the
tracking stability and error convergence with prescribed quadratic performance
index. Finally, the proposed control approach are verified through simulation
results with two link constrained manipulator in a comparative manner.
Keywords: Optimal hybrid Motion/force control; Sliding mode control; Neuralrnnetwork; Constrained and unconstrained motion; Hamilton Jacobi Bellman (HJB)rnoptimization; performance index.
Development of modified LQG controller for mitigation of seismic vibrations using swarm intelligence
by Gaurav Kumar, Roshan Kumar, Ashok Kumar, Brij Mohan Singh
Abstract: A method is presented to design and tune the modified Linear Quadratic
Gaussian (LQG) controller to obtain increased efficiency during the earthquake.
It utilizes swarm intelligence to tune the parameters of LQG based on quasi
resonance between the natural frequencies of the structure in the first two modes
and the predominant frequencies of the seismic signal. The modified controller
thus developed minimizes the energy of structure by altering its parameters
online. For testing of this modified controller, a benchmark prototype structure
is numerically tested under different seismic signatures recorded in near/far fault sites in the different soil conditions. A parametric study comparing the efficiencies of modified LQG, and other contemporary controllers is presented. It is observed for the El-Centro earthquake that the modified controller achieved reductions of 22%, 33%, and 27% in relative displacement, inter-story drift, and absolute acceleration respectively as compared to the conventional LQG controller. Similar results are observed for Gebze and Chi-Chi earthquakes. The modified controller is also evaluated in a situation where power vanishes at the peak of the seismic excitation.
Based on the results and discussion, the performance of the proposed controller
is observed superior among all controllers considered in this study.
Keywords: Semi-active Control; Magneto-Rheological Damper; Seismic Vibrations; Optimal Control; Particle Swarm Optimization; LQG.
Numerical integration-like algorithm for time-optimal trajectory optimization of multi-axis motion system based on iterative learning
by Tie Zhang, Cailei Liao, Yanbiao Zou, Zhongqiang Kang, Caicheng Wu
Abstract: In order to realize the high-velocity and high-precision motion of multi-axis motion system, a numerical integration-like time-optimal trajectory optimization algorithm combined with iterative learning is proposed. Based on the established dynamic model of the multi-axis motion system, the mathematical model with time optimization as the objective function is derived under kinematics and dynamics constraints. The planned trajectory is discretized and the uniform acceleration equation (SUVAT) between any two adjacent discrete points is assumed so that pseudo-velocity planning of the phase plane is carried out by SUVAT equation instead of numerical integration method, after which the optimal solution satisfying the constraints can be obtained. In order to improve the dynamic model and reduce the errors between the calculated and the actual measured torques, a PD-type iterative learning method with forgetting factor is used to continuously update the dynamic model.
Keywords: high-velocity; high-precision; multi-axis motion system; time-optimal control; dynamic model; phase plane; numerical integration; iterative learning; forgetting factor.
Control of Three-Links Robot Arm Based on Fuzzy Neural Petri Nets
by Salam Abdul Hady1, Abduladhem Ali, Waleed Breesam, Ameer Saleh, Yasir Al-Yasir, Raed Abd-Alhameed
Abstract: A fuzzy neural Petri Nets (FNPN) controller is utilized for controlling three links robot arm which considers a nonlinear dynamic system. The incorporation of the classical FNN with a Petri net (PN) has been suggested to produce a new representing system called FNPN structure to alleviate the computation burden. The motion equation of three links robot arm is derived from Lagranges equation. This equation has been incorporated with the motion equations of DC Servo motors which motivate the robot. For nonlinearity dynamic problem, this paper presents a direct adaptive control technique to control three links robot arm utilizing FNPN controller. The computer simulation depicts that the present FNPN controller accomplished better performance with fast response and minimum error.
Keywords: Fuzzy neural Petri Net; Forward Adaptive Control; Robot Arm Control.
Globally linearizing control of linear time-fractional diffusion-advection-reaction systems
by Ahmed MAIDI, Jean-Pierre Corriou
Abstract: The globally linearising control (GLC) structure is adopted to solve both the step tracking and disturbance rejection problems for distributed parameter system described by a time-fractional partial differential equation. The actuation is assumed to be distributed in the spatial domain while the controlled output is defined as a spatial weighted average of the state. First, following a similar reasoning to geometric control and based on the late lumping approach, an infinite dimensional state feedback that yields a fractional finite dimensional system in closed loop is developed. Then, the input of this resulting closed-loop system is defined by means of a robust controller to cope with step disturbances. Assuming that the output shaping function is non-vanishing, on the spatial domain, it is demonstrated that the GLC strategy is stable. Two applications examples are presented to show, through simulation runs, the stabilisation, step tracking and disturbance rejection capabilities of the GLC scheme.
Keywords: distributed parameter system; time-fractional partial differential equation; distributed control; input-output linearisation; globally linearising control; GLC; diffusion-advection-reaction system.
Cruise Control of Autonomous Battery Electric Vehicle using Super Twisting Sliding Mode based Active Disturbance Rejection Control
by Suhail Ahmad Suhail, Mohammad Abid Bazaz, Shoeb Hussain
Abstract: This paper proposes a novel control scheme for the design of cruise control for an autonomous battery electric vehicle (BEV), based on active disturbance rejection control (ADRC) and super twisting sliding mode control (STSMC) featuring accuracy, and rapid convergence. The central concept is to combine the advantages of STSMC to track the reference trajectory accurately with the ability of ADRC to reject the parameter uncertainties and external disturbances. The combination of ADRC and STSMC relaxes the dependency of the controller on the accuracy of the plant model. Compared to the sliding mode control (SMC), adaptive SMC, and fast terminal SMC, the proposed control scheme is independent of the plant model. Simulations are carried out to assess the effectiveness of the proposed control scheme. The simulation results show that the proposed control strategy can significantly reduce the chattering phenomenon, owing to the estimation capability of extended state observer (ESO). The simulation results also show that the proposed strategy is much better in terms of tracking performance than ADRC and proportional integral derivative (PID). The proposed method improves the robustness against modelling errors and disturbances and performs smooth tracking of the reference.
Keywords: active disturbance rejection control; ADRC; electric vehicle; sliding mode control; SMC; cruise control; extended state observer; ESO.
H_infinity Stabilization of Uncertain Discrete Time-Delayed System with Actuator Saturation by using Wirtinger Inequality
by Komal Agrawal, Richa Negi, VIPIN CHANDRA PAL, Vishwajeet Patel
Abstract: This paper contemplates the H? control of discrete-time delayed systems together with actuator saturation, parametric uncertainties and disturbances. H?-based state feedback controller is conceived to stabilise the closed loop system. Lyapunov Krasovskii functional (LKF), discrete Wirtinger-based summation inequality and convex hull approach are combined to obtain novel regional stability conditions. The estimated attraction domain is maximised using an optimisation method along with linear matrix inequality (LMI). A omparative study is shown between the obtained and existing findings. The results are found to be less conservative than the prior ones. Finally, instances signify efficacy of presented approaches.
Keywords: time-delay; discrete system; actuator saturation; linear matrix inequality; LMI; H? control; Wirtinger inequality.
Disturbance Observer-based Higher-order Sliding Mode Controller for Frequency Regulation of Hybrid Power Systems
by Vivek Patel, Dipayan Guha, Shubhi Purwar
Abstract: This paper discusses a higher-order integral sliding mode controller (HOISMC) for frequency regulation of an isolated and interconnected hybrid power system. An improved slap swarm algorithm (ISSA) has been adopted and implemented for concurrent tuning of the HOISMCs parameter to alleviate system oscillations and improve system stability in the wake of uncertain perturbations. To enhance control performance and minimise the chattering problem of sliding mode controller, a disturbance observer is designed and incorporated to refine HOISMCs control law. Simulation outcomes of the ISSA have been evaluated and compare with existing optimisation technique and the results reported in the state-of-art. The comparative study confirms the superiority of DO-HOISMC over the others controllers in terms of damping system oscillations. Moreover, the mastery of the DO-HOISMC is assessed incorporating system nonlinearities and control input-delay. Finally, the closed-loop robust stability of the studied HPS has been affirmed by applying Kharitonovs theorem considering parametric uncertainties.
Keywords: hybrid power system; HPS; disturbance observer; DO; salp swarm algorithm; SSA; higher-order integral sliding mode controller; HOISMC; Kharitonovs theorem.
Lyapunov-based MPC for nonlinear process with on-line triggered linearized model
by Ruo Wu, Dongya Zhao
Abstract: Most of the industrial processes are nonlinear, model predictive control (MPC) using an explicit nonlinear model can achieve satisfactory performance, however, it will bring a high computational burden. Although linear MPC is widely used in practice, a linear model cannot deal with the highly nonlinear system dynamic well overall a wide operating region. In this study, an error trigger rule evoking a re-modeling algorithm to re-linearize the known nonlinear analytical model has been proposed for closed-loop nonlinear systems with input constraints. The error-triggering can be conducted by an error quantizer that quantifies model error and the re-linearization program is triggered when the accumulated error exceeds the set threshold. The stability of the process is maintained by using the Lyapunov-based MPC. The effectiveness of the proposed control algorithm is validated by a chemical process simulation.
Keywords: Model predictive control; nonlinear systems;on-line linearization; error-trigger; computation time.
Dynamic-robust controller applied to a Temperature TITO process
by Monica Contreras, Marco Herrera, Paulo Leica, Oscar Camacho
Abstract: The purpose of this paper is to develop a dynamic sliding mode control (DSMC) for a two-input two-output (TITO) temperature system. First, the interaction between variables is analysed using the relative gains matrix (RGA) and the Bristol criterion to design a decoupler scheme. After that, a DSMC control scheme for the TITO system is then proposed using a decoupler; in addition, the proposed controller is tuned using the mean-variance mapping optimisation (MVMO) method. Next, experimental tests of reference changes, parametric uncertainties, and experimental disturbances are performed on the Temperature Control Lab (TCLab). Finally, the performance of the proposed controller is compared against a PI controller and an SMC using TVu, ISE, and IAE performance indices, where it is shown that the proposed controller can reduce the chattering effect for the TITO system.
Keywords: dynamic sliding mode control; DSMC; TITO process.
Autonomous decision-making and resource scheduling for integrated radio frequency system
by Hui Xue, Tao Zhang
Abstract: In view of the limitations of volume and weight of equipment assembled on airborne platform, integrated RF system will be the development trend of RF system on remote unmanned airborne vehicle (UAV). In order to improve the detection performances of an intelligence UAV integrated RF system, this paper proposes an agent-based RF system architecture for UAV integrated RF system. The proposed system architecture which can realise the autonomous decision-making of RF tasks and intelligent resource scheduling according to the obtained target information and threat environment. Firstly, the proposed system architecture of integrated RF system is introduced; secondly, the autonomous decision algorithm embedded in agent is proposed; Thirdly, the improved multi-task resource scheduling algorithm is described; then, the simulation experiments for UAV integrated RF system under different threat environments are given to verify the effectiveness of the proposed system architecture, autonomous decision-making algorithm and resource scheduling algorithm.
Keywords: autonomous decision-making; resource scheduling; integrated RF; agent; PetriNet.
Iterative Learning algorithms-based multiplicative thrust fault reconstruction and tolerant control for spacecraft formation flying systems
by Yule Gui, Qingxian Jia, Huayi Li, Zhong Zheng
Abstract: In this paper, the issues of multiplicative thruster fault reconstruction and reconfigurable fault-tolerant control for spacecraft formation flying system subject to loss of thruster effectiveness and a series of external space perturbations are investigated using iterative learning algorithms. Inspired by sliding mode methodology, a new Robust Iterative Learning Observer (RILO) is explored to reconstruct thrust effectiveness factor. Subsequently, a learning state-feedback fault-tolerant control approach is proposed based on the fault signals obtained from the RILO to guarantee the closed-loop spacecraft formation configuration is accurately maintained in the presence of multiplicative thrust faults and space perturbations. Finally, numerical simulations clearly validate the effectiveness and superiority of the proposed thrust fault-reconstructing and tolerant configuration maintenance control schemes for spacecraft formation flying systems.
Keywords: spacecraft formation; fault reconstruction; iterative learning observer; fault-tolerant control; learning state-feedback control.
Event-triggered containment control for multi-agent systems with switching topology in finite-time
by Wenjun Wei, Jiahui Lv, Fengyang Gao, Liben Yang
Abstract: In this paper, a distributed finite-time state observer and event-triggered control strategy are proposed for the event-triggered multi-agent containment control in finite-time, which considering that the network topology is brittle to switch due to disturbances and emergencies in actual communication. Firstly, we design a distributed control protocol through finite-time state observer under switching topology, which the follower agents can quickly converge to the convex hull formed by multiple leaders. The switching topology is more practical. We prove that the designed state observer can estimate the external system status in a finite-time by using graph theory and Lyapunov stability theory. Secondly, the event-triggered control mechanism is designed to reduce the communication times and the updating times between multi-agents and greatly decrease the energy consumption on the basis of satisfying the finite-time containment control. We also prove that the designed control strategy can eliminate Zeno behaviour. Finally, the effectiveness of the theoretical results is verified by a simulation example.
Keywords: containment control; event-triggered; finite-time; multi-agent systems; switching topology.
Voltage control in a wind-diesel power system using adaptive RBF sliding mode control of STATCOM
by Zahid Afzal Thoker, Shameem Ahmad Lone
Abstract: In this paper, voltage control with the design and application of sliding mode control based on adaptive radial basis function (RBF) neural network of a static synchronous compensator (STATCOM) is proposed. Firstly, the mathematical model of the wind-diesel power system with STATCOM has been established. With the construction of the switching manifold, a sliding mode controller is designed which generates the control law with which the converter operation of STATCOM is controlled to maintain the reactive power balance in the system. An adaptive RBF neural network is used to approximate the system function in the sliding mode controller to improve the performance of the system. The stability of the system with the control laws is guaranteed using Lyapunov stability criteria. MATLAB simulations are performed, and the system is exposed to disturbances in load and wind power. Comparative analysis of voltage deviations is presented to show the efficacy of the proposed methodology.
Keywords: wind-diesel power system; static synchronous compensator; STATCOM; sliding mode controller; SMC; adaptive RBF neural network.
Multi-Input Multi-Output Sliding-Mode Control of LCL-based Grid-?Connected Modified Y-Source Inverter for Power Conditioning of Photovoltaic Generation
by Seyede Tahere Hashemi, Mehdi Siahi, Majid Hosseinpour, Javad Olamaei
Abstract: A multi-input-multi-output (MIMO) sliding mode controller has been proposed in this paper in order to control a grid-connected photovoltaic (PV) power conditioning system with an LCL filter. In the proposed control approach, no linear-control method is used and simultaneous control of both the AC and DC inverter sides is done. Therefore, all the state variables are controlled simultaneously. The proposed sliding mode control (SMC) method provides several advantages, including zero grid current error, easy implementation, and robustness against parameter uncertainties of the investigated system. Hence, the resonance in the inverter output resulting from the LCL filter can be eliminated using SMC with no need for active damping techniques. Applying this approach can bring about maximum power point tracking (MPPT), control of the DC side, and injection of high-quality current into the grid at the AC side of the inverter. Various simulations have been conducted in MATLAB/Simulink for the proposed system.
Keywords: AC side control; DC side control; multi-input-multi-output; MIMO-SMC; modified Y-source; PV; single-phase; sliding mode control; SMC.
An experimental study of spatial temperature profile control of a distributed parameter heating system using model predictive control
by Jaivik Mankad, Nitin Padhiyar
Abstract: The problem of profile control is important owing to its industrial significance. This problem is viewed as a multi-objective problem and solved using different approaches such as augmentation of objectives or prioritised solving of each objective separately. The focus in our work is to experimentally study the lexicographic optimisation approach for prioritised control of different objectives. An experimental rig has been designed for the purpose of priority driven spatial property control of a distributed parameter system (DPS). The setup consists of a thin metal plate with four temperature sensors and four electric heaters located axially. Through this experimental rig, we demonstrate the concept of controlling the spatial profile in a DPS and address the relevant issues. Specifically, when the desired spatial profile is unachievable, we may be interested in controlling different parts of the profile depending upon their importance. We show an MPC formulation to achieve such a spatial profile control with user defined priority using lexicographic optimisation approach in this work.
Keywords: model predictive control; MPC; distributed parameter system; prioritised MPC; lexicographic optimisation; spatial temperature profile.
Novel Adaptive control for avoiding fuzzy rule explosion in nonlinear systems
by ASHWANI KHAROLA
Abstract: The study highlights three different control techniques namely proportional integral derivative (PID), adaptive neuro fuzzy inference system (ANFIS) and neural networks (NNs) for control of highly nonlinear triple inverted pendulum system mounted on a carriage. The objective is to control the complete system within 3.0 sec using above mentioned controllers. The controllers were compared in terms of performance attributes like settling time, steady state error and overshoot responses. The results indicate better performance of ANFIS controller compared to PID and NN controllers. The study also highlights the effect of shape, number and type of membership function on training of ANFIS controller. The study further proposes an ANFIS controller which has been designed using only three membership functions and can successfully solve the problem of rule explosion associated with fuzzy controllers.
Keywords: triple inverted pendulum; fuzzy rule explosion; artificial neural networks; proportional integral derivative; PID; ANFIS; MATLAB; Simulink; simulation.
Assessment of reading material using sensor data
by Aniruddha Sinha, Sanjoy Kumar Saha, Anupam Basu
Abstract: Reading is characterised by a sequence of complex processing in the brain. The experienced cognitive load and engagement level play a major role in the assimilation of content. In this paper, for the evaluation of a textual content, we use electroencephalogram (EEG) for brain signals and eyetracker for the eyegaze data. Experiment is done with two types (easy and difficult) of textual contents which are benchmarked using standard parameters of natural language processing. Features on cognitive load and engagement index extracted from alpha and beta frequency bands of EEG are found to be discriminative from left-temporal and right-prefrontal lobes respectively. Statistical features, derived from the shift in eyegaze fixations from the current line to the adjacent lines, are found to be discriminative. A difficulty score is computed using a novel mapping function derived from the mixture of two partial sigmoid. This enables more objective comparison of two contents and helps in finding differences in individual reading skills.
Keywords: textual content; eye-gaze; electroencephalogram; EEG; cognitive load; sigmoid.
Improved robust stability and stabilisation conditions for discrete-time linear systems with time-varying delay
by M. Venkatesh, Sourav Patra, Goshaidas Ray
Abstract: This paper presents robust stability and stabilisation problems of linear discrete-time systems with interval time-varying state-delay. By invoking a new Lyapunov-Krasovskii functional, a less conservative delay-dependent robust stability criterion is derived in terms of linear matrix inequalities (LMIs) using the summation inequality in combination with the extended reciprocally convex inequality. Then, a delay-dependent stabilisation problem of discrete-time systems is explored by designing a state feedback controller. The superiority of the proposed result over existing ones is demonstrated through numerical examples.
Keywords: time-delay systems; robust stability; stabilisation; summation inequality; Lyapunov-Krasovskii functional; extended reciprocally convex inequality; linear matrix inequality.
A virtual GPS design using information of indoor localisation system for robotics navigation
by Mohammad R. Hayajneh, Antonio M. Galiano
Abstract: In order to optimise gains, log data, and verify performance, people must perform repeated experiments on robotics platforms. Therefore, conducting such experiments outdoors might be inconvenient for researchers at this stage, particularly if GPS information is required for location identification. Additionally, testing robots such as drones may result in abnormal behaviours because of unproven approaches. Therefore, experiments in the laboratory will be safer and faster, but GPS receivers used in robots do not work properly in buildings. This work is the first of its kind to design a virtual GPS that calculates its data using an indoor motion tracking system by converting the local position information into global positions in geodetic coordinates. To maintain the connection between GPS and robot, the work also formats the location dataset in specific binary structure using UBX messages. A quadcopter with an on-board powerful computer was used to validate the real-time execution of the adopted approach.
Keywords: virtual-GPS; quadcopter; OptiTrack; UBX; Pixhawk; Raspberry Pi; navigation.
Stable optimal self-tuning interval type-2 fuzzy controller for servo position control system
by Ritu Rani De Maity, Rajani K. Mudi, Chanchal Dey
Abstract: An optimal self-tuning interval type-2 fuzzy controller for servo position control systems is reported here. To achieve precise positioning of the actuator, input scaling factors of an interval type-2 fuzzy proportional-integral controller are updated online depending on the latest operating conditions in terms of closed loop tracking error and change of error. To ensure desired performance, input scaling factors are obtained by adaptive cuckoo search-based optimisation algorithm. Efficacy of the proposed scheme is substantiated through performance comparison with recently reported peak observer based and online self-tuning based interval type-2 fuzzy PID, interval type-2 fuzzy PI, and also type-1 fuzzy PI controllers through simulation study along with real-time validation on a DC servo position control system. Lyapunov function-based stability analysis for the proposed controller is also provided.
Keywords: type-2 fuzzy control; self-tuning mechanism; optimal fuzzy control; servo position control; real-time experimentation.
Closed-loop thickness control and sensor placement in extrusion blow moulding
by Mostafa Darabi, Raffi Toukhtarian, Hossein Vahid Alizadeh, Benoit Boulet
Abstract: Extrusion blow moulding (EBM) is a polymer forming technique used to manufacture hollow plastic parts, such as fuel tanks. In this work, the feasibility of using closed-loop control in EBM is explored to compensate for machine drift and disturbances. A control system is proposed to regulate the extrusion process in EBM. The extrusion controller aims to increase process consistency by minimizing part thickness deviations. The thickness of the extrudate is measured during the extrusion cycle and any deviation from the desired thickness profile is compensated by changing the die gap in real time. The controller, which offers flexibility in thickness sensor placement, features a Smith predictor configuration embedding an H∞ controller. It compensates for the input-dependent polymer transport delay, the nonlinear steady-state swell, and nonminimum phase necking effects affecting the extrudate. This extrusion control technique may help reduce the production rate of off-specification parts and improve product quality.
Keywords: H∞ controller; optimal control; Smith predictor; parameter identification; polymer extrusion; sensor placement; variable die gap; input-dependent delay; Hammerstein block.