International Journal of System Control and Information Processing (8 papers in press)

Regular Issues

• A Cost-effective Airborne Multisensor System for Pipeline Monitoring using a Quadcopter  
  by Mbadiwe Samuel Benyeogor, Adeboye Olatunbosun, Oladayo O. Olakanmi, Olusegun I. Lawal 
  Abstract: In recent years, there has been a significant surge in the demand of multisensory quadcopters for search, rescue and disaster response operations. This also includes the development of multisensor surveillance systems for aerial inspection of oil and gas pipelines, using various systems integration techniques. This research starts with the modeling of the quadcopter's kinematics, aerodynamics stability, and controllability in 3D space. An autonomy is added by developing an edge-based flight controller which executes high-level commands, for example, trajectory control and gyro-stabilization. In addition, a pair of thermoresistive gas sensors and nondispersive infrared (NDIR) gas sensor are used as oil and gas leakage detection system, which are embedded under the quadcopter as a payload. Python scripts are used to visualise the quadcopters flight dynamics during flight tests for the prediction of its adaptability for instrument deployment. The result of this work is a prototype quadcopter system that can fly autonomously to survey a designated oil and gas pipeline infrastructure for the detection of possible leakages. Performance analysis has been carried out on the system and its subcomponents. Using an on-board signal processor, the measured data from the gas sensors are fused and streamed to the base station for further analyses and evaluation of the systems performance. The multisensory system that is developed functions as predicted. Moreover, we envisage that this system will help improve safety, situational awareness, and decision making in the oil and gas industries.
  Keywords: Flammable Gases; Multi-sensory Systems; NDIR sensing; Quadcopter.
  
  
• A Backbone-Edge Feature Extraction Method for Varied Industrial Parts  
  by Ping Wan, Ling Guo, Ming Li, Min Gao, Hongping Zhang, Jie Li 
  Abstract: Empty-loading ratio (ELR) in manufacturing factories is challenged due to plenty of industrial parts with diverse sizes and shapes, where edge detection is a primary operation to obtain ELR. Unlike traditional edge detection tasks, the purpose of ELR is to gain backbones of industrial parts and lter the details. Therefore, we present a backbone-edge feature extraction approach to deal with ELR computation problem. Thereinto, a multi-scale block CNN model is structured to learn primary information of industrial parts through hybrid operations (i.e., horizontal and vertical combinations) with both deep and shallow features. In the model, a neighbor-information-based loss function is designed to enhance backbone information. Furthermore, a ELR value is obtained through discovering minimized closed-regions based on backbone edge information from our model. Simulations on industrial parts in conveyor boxes from real world indicate that the proposed approach outperforms other state-of-the-art methods.
  Keywords: Edge detection; backbone feature; neighbor information; convolutional neural networks (CNN).
  
  
• Dynamic output feedback based robust controller design for selective catalytic reduction systems  
  by Baigeng Wang, Shurong Li 
  Abstract: Selective catalytic reduction (SCR) is an effective way to deal with the flue gas at the outlet of coal-fired boiler. The working process of SCR systems is to use the ammonia (NH₃) adsorbed on the catalyst to react with nitrogen oxide (NO_x) to generate nitrogen and water. This paper presents an observer based dynamic output feedback control for SCR systems. Since NH₃ coverage ratio cannot be measured directly, a reduced order observer is designed first. And then it is proved that the error of observer tends to 0 by using Lyapunov function. Furthermore, the dynamic equation of the composite including the observer state is obtained. Finally, the controller is designed to adjust the rate of NH₃ injection in order to satisfy the industrial demand.
  Keywords: SCR; selective catalytic reduction; dynamic output feedback; reduced order observer; robust control; Lyapunov function.
  DOI: 10.1504/IJSCIP.2021.10046202
   
  
• Event-triggered model predictive control for grid-connected three-phase inverter  
  by Qianling Chen, Benfei Wang 
  Abstract: With advantage of dealing with various nonlinear system, model predictive control (MPC) strategy has been applied commonly in power system, however, there is a large computation burden in the traditional MPC strategy. To overcome the shortcoming of MPC, this paper implements event triggered model predictive control (ET-MPC) strategy in grid-connected microgrid system (GMS), which activates MPC once the triggered condition is satisfied; otherwise MPC operation will be stopped. Therefore, it helps to reduce online calculation amount and switching action greatly. To verify the feasibility and superiority of the proposed ET-MPC strategy applied in GMS, the corresponding simulation studies have been conducted in Matlab/Simulink. The various simulation and comparison results, demonstrate the effectiveness of proposed ET-MPC strategy for the GMS.
  Keywords: GMS; grid-connected microgrid system; MPC; model predictive control; event triggered control; computation burden.
  DOI: 10.1504/IJSCIP.2021.10046205
   
  
• Enhancement of vehicle stability based on coordinated active rear steering and additional yaw moment  
  by Ping Wang, Xiaodong Ding, Jiamei Lin, Yongqiang Zhao, Jun Li 
  Abstract: To improve the handling and manoeuvrability of four-wheel steer (4WS) and in-wheel motor-driven electric vehicle (EV) under critical conditions, a coordinated control method combining active rear-wheel steering (ARS) and additional yaw moment control in the form of additional drive torque distribution is proposed. Considering the nonlinear properties of tyre force, a nonlinear model predictive controller (NMPC) is designed to track the desired yaw rate and constrain the sideslip angle. Meanwhile, the actuator and security constraints are satisfied effectively. Finally, the control performance is investigated by co-simulation with MATLAB/Simulink and CarSim. The results show that the vehicle longitudinal and lateral stability are both efficiently enhanced under extreme conditions.
  Keywords: critical conditions; four-wheel steer; vehicle stability control; avtive rear wheel steering; additional yaw moment; integrated vehicle dynamics system; electric vehicles; NMPC; nonlinear model predictive control; vehicle active safety.
  DOI: 10.1504/IJSCIP.2021.10046206
   
  
• Model predictive control based consensus scheme of discrete-time multi-agent systems with communication delay  
  by Guanghao Song, Xiaohua Liu, Rong Gao 
  Abstract: In this paper, the state consensus of MPC for discrete-time multi-agent systems with communication delays is considered. When there is delay in the process of information transmission between adjacent agents, the control protocol design method of multi-agent system without delay can not deal with this situation. Therefore, a new control protocol design method is needed. Based on the Pontryagin's maximum principle and mathematical induction, a distributed MPC state consensus protocol is presented. In order to facilitate discussion, a new graph is constructed, and the relationship between the new topology graph and the original topology graph is given through a lemma. By using the error between the agent and the neighbour agent, and constructing the Lyapunov-Krasovskii function, a MPC cost function with terminal constraint which is monotonically decreasing, and the conditions for achieving state consensus of discrete multi-agent system with communication delay are given.
  Keywords: communication delays; discrete multi-agent systems; state consensus; high-order; model predictive control.
  DOI: 10.1504/IJSCIP.2021.10046207
   
  
• Model-free predictive control for a class of switched nonlinear systems  
  by Ye Tian, Yunshuo Wang, Baili Su, Cheng Tan 
  Abstract: A model-free predictive control (MFPC) method is devised towards to a kind of high order discrete nonlinear switched systems whose models are undefined. Firstly, the estimation models are designed by using the compact dynamic linearisation technique and improved projection algorithm to approach the controlled nonlinear subsystems. Then, on the basis of the estimation models, the controller's explicit analytic solutions are obtained by solving the finite time domain rolling optimisation quadratic functions. Finally, an appropriate switching law is designed to make the subsystems switch reasonably in different subsystems, thus ensuring the stability of the whole system. The simulation results show that the control strategy is effective.
  Keywords: predictive control; model-free control; nonlinear systems; switched systems; data-driven control; pseudo partial derivative matrix; compact form dynamic linearisation; Lyapunov function; projection algorithm; stability.
  DOI: 10.1504/IJSCIP.2021.10046209
   
  
• Experimental research and analysis of modularity and robustness in target control efficiency of complex networks  
  by Mona Shahsavari, Ali Moeini, Mahmood Shabankhah, Ali Kamandi 
  Abstract: Studies on overall target control of directed networks from the point of view of diversity have revealed that random target control of real-world networks is in general not efficient. Therefore, one needs to search for those features of the network which have a positive effect on the efficiency of random target control. In this paper, we investigate in particular the modularity of the Erdös-Rényi and Scale-Free directed networks. We observe that in these types of networks one could modify the efficiency of target control by increasing the network modularity while keeping the degree distribution fixed. We also notice that the impact of modularity on target control of such networks depends on many factors including number and size of communities of the network as well as network 'prior' target control efficiency. Moreover, we show that target control efficiency of the network grows when there is some link failure. It is a surprising fact that directed networks respond more robustly to target control than to full control. Numerical simulations support our claim for random target control of directed networks.
  Keywords: target control; structural controllability; modularity analysis; robustness analysis; control efficiency; real world network; link failure.
  DOI: 10.1504/IJSCIP.2021.10046210