Abstract
This paper presents a novel stochastic predictive tracking control strategy for nonlinear and non-Gaussian stochastic systems based on the single neuron controller structure in the framework of information theory. Firstly, in order to characterize the randomness of the control system, survival information potential (SIP), instead of entropy, is adopted to formulate the performance index, which is not shift-invariant, i.e., its value varies with the change of the distribution location. Then, the optimal weights of the single neuron controller can be obtained by minimizing the presented SIP based predictive control criterion. Furthermore, mean-square convergence of the proposed control algorithm is also analyzed from the energy conservation perspective. Finally, a numerical example is given to show the effectiveness of the proposed method.
Highlights
Since almost all the control systems are subject to random signals, stochastic systems are widely encountered in control engineering design
Compared with stochastic distribution control (SDC) (PDF shaping strategy) proposed earlier, it is more straightforward to us a minimum error entropy (MEE) based stochastic control algorithm to design a controller for tracking errors
It is more appropriate than the traditional minimum mean square error (MSE) criterion when dealing with nonlinearities and non-Gaussian disturbances
Summary
Since almost all the control systems are subject to random signals (such as those originating from system parameter variations and sensor noise, etc.), stochastic systems are widely encountered in control engineering design. In order to solve the problems existing in paper-making processes, the stochastic distribution control (SDC) theory was proposed by Wang (1996) [2] This theory aims at controlling the shape of the output PDF instead of the mean and variance for stochastic systems [3,4,5]. Compared with SDC (PDF shaping strategy) proposed earlier, it is more straightforward to us a minimum error entropy (MEE) based stochastic control algorithm to design a controller for tracking errors It is more appropriate than the traditional minimum mean square error (MSE) criterion when dealing with nonlinearities and non-Gaussian disturbances. Based on the preliminary work [19,34,35,36,37], in this paper, a single neuron stochastic predictive control method for nonlinear stochastic discrete systems affected by non-Gaussian noise is proposed.
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