RBFNN-based Minimum Entropy Filtering for a Class of Stochastic Nonlinear Systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This 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

Parametric decoupling control strategy for a class of nonlinear uncertain systems via observer-based output feedback

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkIn 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

Probabilistic Decoupling Control for Stochastic Non-Linear Systems Using EKF-Based Dynamic Set-Point Adjustment

In 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

Parametric Co-variance Assignment for a Class of Multivariable Stochastic Uncertain Systems: Output Feedback Stabilization Approach

This 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

An EKF-Based Performance Enhancement Scheme for Stochastic Nonlinear Systems by Dynamic Set-Point Adjustment

YesIn this paper, a performance enhancement scheme has been investigated for a class of stochastic nonlinear systems via set-point adjustment. Considering the practical industrial processes, the multi-layer systematic structure has been adopted to achieve the control design requirements subjected to random noise. The basic loop control is given by PID design while the parameters have been fixed after the design phase. Alternatively, we can consider that there exists an unadjustable loop control. Then, the additional loop is designed for performance enhancement in terms of the tracking accuracy. In particular, a novel approach has been presented to dynamically adjust the set-points using the estimated states of the systems through extended Kalman filter (EKF). Minimising the entropy criterion, the parameters of the set-point adjustment controller can be optimised which will enhance the performance of the entire closed-loop systems. Based upon the presented scheme, the stochastic stability analysis has been given to demonstrate that the closed-loop tracking errors are bounded in probability one. To indicate the effectiveness of the presented control scheme, the numerical examples have been given and the simulation results imply that the designed systems are bounded and the tracking performance can be enhanced simultaneously. In summary, a new framework for system performance enhancement has been presented even if the loop control is unadjustable which forms the main contribution of this paper

Output Feedback Stabilization for Dynamic MIMO Semi-linear Stochastic Systems with Output Randomness Attenuation

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

A Novel Data-based Stochastic Distribution Control for Non-Gaussian Stochastic Systems

YesThis note presents a novel data-based approach to investigate the non-Gaussian stochastic distribution control problem. As the motivation of this note, the existing methods have been summarised regarding to the drawbacks, for example, neural network weights training for unknown stochastic distribution and so on. To overcome these disadvantages, a new transformation for dynamic probability density function is given by kernel density estimation using interpolation. Based upon this transformation, a representative model has been developed while the stochastic distribution control problem has been transformed into an optimisation problem. Then, data-based direct optimisation and identification-based indirect optimisation have been proposed. In addition, the convergences of the presented algorithms are analysed and the effectiveness of these algorithms has been evaluated by numerical examples. In summary, the contributions of this note are as follows: 1) a new data-based probability density function transformation is given; 2) the optimisation algorithms are given based on the presented model; and 3) a new research framework is demonstrated as the potential extensions to the existing s

Observer-based parametric decoupling controller design for a class of multi-variable non-linear uncertain systems

open access articleThis 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