Browsing by Author "Zhang, Qichun"
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Item Open Access A hybrid mode membrane computing based algorithm with applications for proton exchange membrane fuel cells(MDPI, 2023-07-10) Zhao, Jinhui; Zhang, Wei; Hu, Tianyu; Xu, Ouguan; Yang, Shengxiang; Zhang, QichunMembrane computing is a branch of natural computing, which has been extended to solve various optimization problems. A hybrid mode membrane-computing-based algorithm (HMMCA) is proposed in this paper to solve complex unconstrained optimization problems with continuous variables. The algorithmic framework of HMMCA translates from its distributed cell-like membrane structure and communication rule. A non-deterministic evolutionary programming method and two computational rules are applied to enhance the computational performance. In a numerical simulation, 12 benchmark test functions with different variables are used to verify the algorithmic performance. The test results and comparison with three other algorithms illustrate its effectiveness and superiority. Moreover, a case study on a proton exchange membrane fuel cell (PEMFC) system parameter optimization problem is applied to validate its practicability. The results of the simulation and comparison with seven other algorithms demonstrate its practicability.Item Open Access EKF-based Enhanced Performance Controller Design for Non-linear Stochastic Systems(IEEE, 2017-08-21) Zhou, Yuyang; Zhang, Qichun; Wang Hong; Zhou Ping; Chai, TianyouIn this paper, a novel control algorithm is presented to enhance the performance of the tracking property for a class of non-linear and dynamic stochastic systems subjected to non- Gaussian noises. Although the existing standard PI controller can be used to obtain the basic tracking of the systems, the desired tracking performance of the stochastic systems is difficult to achieve due to the random noises. To improve the tracking performance, an enhanced performance loop is constructed using the EKF-based state estimates without changing the existing closed loop with PI controller. Meanwhile, the gain of the enhanced performance loop can be obtained based upon the entropy optimization of the tracking error. In addition, the stability of the closed loop system is analysed in the mean square sense. The simulation results are given to illustrate the effectiveness of the proposed control algorithm.Item Open Access Entropy-based Iterative Learning Estimation for Stochastic Non-linear Systems and Its Application to Neural Membrane Potential Interaction(2019-07-23) Zhang, Qichun; Dai, XuewuItem Open Access An introductory survey of probability density function control(Taylor and Francis, 2019-03-16) Ren, Mifeng; Zhang, Qichun; Zhang, JianhuaProbability density function (PDF) control strategy investigates the controller design approaches where the random variables for the stochastic processes were adjusted to follow the desirable distributions. In other words, the shape of the system PDF can be regulated by controller design.Different from the existing stochastic optimization and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. Motivated by the development of data-driven control and the state of the art PDF-based applications, this paper summarizes the recent research results of the PDF control while the controller design approaches can be categorized into three groups: (1) system model-based direct evolution PDF control; (2) model-based distribution-transformation PDF control methods and (3) data-based PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense.Item Open Access Observer-based parametric decoupling controller design for a class of multi-variable non-linear uncertain systems(Taylor and Francis, 2018-06-08) Zhang, Qichun; Yin, XinThis paper presents a novel decoupling control strategy for Lipschitz multi-variable non-linear uncertain systems. Using the explicit parametric design, an observer-based output feedback controller has been developed with free parameters while the closed-loop system can be further described by transfer function matrix with these free parameters. The coupling effects of the systems would be attenuated if the free parameters are optimised where the performance criterion is given based on the H∞ norm of the transfer functions. Moreover, the sufficient conditions of stabilization have been obtained for observer, controller and closed-loop system, respectively. Following the procedure of the presented control strategy, an illustrative numerical example is given to demonstrate the effectiveness of the presented control strategy. In addition, the similar design approach has been discussed for filtering problem which is a potential extension of the presented control strategy.Item Embargo Output Feedback Stabilization for a Class of Multi-Variable Bilinear Stochastic Systems With Stochastic Coupling Attenuation(IEEE, 2016-08-31) Zhang, Qichun; Zhou, Jinglin; Wang Hong; Chai, TianyouIn this technical note, stochastic coupling attenuation is investigated for a class of multi-variable bilinear stochastic systems and a novel output feedback m-block backstepping controller with linear estimator is designed, where gradient descent optimization is used to tune the design parameters of the controller. It has been shown that the trajectories of the closed-loop stochastic systems are bounded in probability sense and the stochastic coupling of the system outputs can be effectively attenuated by the proposed control algorithm. Moreover, the stability of the stochastic systems is analyzed and the effectiveness of the proposed method has been demonstrated using a simulated example.Item Open Access Output Feedback Stabilization for Dynamic MIMO Semi-linear Stochastic Systems with Output Randomness Attenuation(2018-09-07) Zhang, Qichun; Hu, Liang; Gow, J. A.In this paper, the problem of randomness attenuation is investigated for a class of MIMO semi-linear stochastic systems. To achieve this control objective, a m-block backstepping controller is designed to stabilize the closed-loop systems in probability sense. In addition, the output randomness attenuation can be achieved by optimising the design parameters using minimum entropy criterion. The effectiveness of this presented control algorithm can be verified by a given numerical example. In summary, the main contributions of this paper are characterized as follows: (1) an output feedback design method is adapted to stabilise the dynamic multi-variable semi-linear stochastic systems by block backstepping; (2) randomness of the system output is attenuated by searching the optimal design parameter based on the entropy criterion; (3) a framework of performance enhancement for stochastic systems is developed.Item Open Access Parametric Co-variance Assignment for a Class of Multivariable Stochastic Uncertain Systems: Output Feedback Stabilization Approach(Advances in Science, Technology and Engineering Systems Journal (ASTESJ), 2018-09-20) Zhang, QichunThis paper presents a novel parametric co-variance assignment strategy for multi-variable stochastic uncertain systems. Based upon the explicit parametric design and reduced-order closed-form co-variance model, the variances and co-variances of the system outputs can be assigned artificially using output feedback while the effect of the system uncertainties can be minimized by optimizing the free parameters. In addition, the stability of the closed-loop system has been analyzed and an illustrative numerical example is given to demonstrate the effectiveness of the presented strategy. As a summary, the contributions of this paper include the reduced-order co-variance model, the co-variance error based performance criterion and the parametric control design with stability analysis.Item Open Access Parametric decoupling control strategy for a class of nonlinear uncertain systems via observer-based output feedback(IEEE, 2017-10-26) Zhang, Qichun; Yin, XinIn this paper, the system decoupling problem has been investigated and a novel decoupling control strategy is presented for Lipschitz nonlinear uncertain multivariable systems. This control strategy consists of an explicit parametric state feedback controller and a linear state observer, where the free parameters of the controller can be adjusted to attenuate the coupling effects. In addition, the optimal parameters can be obtained using H infinity norm based performance criterion. The convergence of the observer, the robust stabilization of the controller and closed-loop system are analysed while the sufficient conditions are determined. Following the design procedure of the presented control strategy, an illustrative numerical example is given to demonstrate the effectiveness and correctness of the presented control strategy.Item Open Access Probabilistic Decoupling Control for Stochastic Non-Linear Systems Using EKF-Based Dynamic Set-Point Adjustment(2018 UKACC 12th International Conference on Control (CONTROL), 2018-11-01) Zhang, Qichun; Hu, LiangIn this paper, a novel decoupling control scheme is presented for a class of stochastic non-linear systems by estimation-based dynamic set-point adjustment. The loop control layer is designed using PID controller where the parameters are fixed once the design procedure is completed, which can be considered as an existing control loop. While the compensator is designed to achieve output decoupling in probability sense by a set-point adjustment approach based on the estimated states of the systems using extended Kalman filter. Based upon the mutual information of the system outputs, the parameters of the set-point adjustment compensator can be optimised. Using this presented control scheme, the analysis of stability is given where the tracking errors of the closed-loop systems are bounded in probability one. To illustrate the effectiveness of the presented control scheme, one numerical example is given and the results show that the systems are stable and the probabilistic decoupling is achieved simultaneously.Item Open Access RBFNN-based Minimum Entropy Filtering for a Class of Stochastic Nonlinear Systems(IEEE, 2019-05-01) Yin, Xin; Zhang, Qichun; Wang, Hong; Ding, ZhengtaoThis paper presents a novel minimum entropy filter design for a class of stochastic nonlinear systems which are subjected to non-Gaussian noises. Motivated by stochastic distribution control, an output entropy model is developed using RBF neural network while the parameters of the model can be identified by the collected data. Based upon the presented model, the filtering problem has been investigated while the system dynamics have been represented. As the model output is the entropy of the estimation error, the optimal nonlinear filter is obtained based on the Lyapunov design which makes the model output minimum. Moreover, the entropy assignment problem has been discussed as an extension of the presented approach. To verify the presented design procedure, a numerical example is given which illustrates the effectiveness of the presented algorithm. The contributions of this paper can be included as 1) an output entropy model is presented using neural network; 2) a nonlinear filter design algorithm is developed as the main result and 3) a solution of entropy assignment problem is obtained which is an extension of the presented framework.Item Open Access A Survey of the Probability Density Function Control for Stochastic Dynamic Systems(2018-09-07) Ren, Mifeng; Zhang, Qichun; Zhang, JianhuaProbability density function (PDF) control strategy investigates the controller design approaches in order to to realise a desirable distributions shape control of the random variables for the stochastic processes. Different from the existing stochastic optimisation and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. This paper summarises the recent research results of the PDF control while the controller design approaches can be categorised into three groups: 1) system model-based direct evolution PDF control; 2) model-based distribution-transformation PDF control methods and 3) databased PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense.