Single Neuron Stochastic Predictive PID Control Algorithm for Nonlinear and Non-Gaussian Systems Using the Survival Information Potential Criterion

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.

Maximum Power Point Tracking Control for Non-Gaussian Wind Energy Conversion System by Using Survival Information Potential

“…The probability distribution of the error is usually unknown. Therefore, here we adopt a data-driven approach to estimate the SIP of the tracking error [ 30 , 31 ]. Supposing at time k there exist error samples which represent a set of N independent and identically distributed samples for e , then the estimated value of the survival function can be expressed as: …”

SIP-Based Single Neuron Stochastic Predictive Control for Non-Gaussian Networked Control Systems with Uncertain Metrology Delays

“…In order to overcome these drawbacks, a single neuron control strategy for non-Gaussian stochastic systems based on the survival information potential (SIP) criterion was proposed by Chen et al [29], in which control input was conservatively considered as a deterministic variable [28,30]. In fact, the randomness of control input exists in practical conditions, so paper [31] proposed a single neuron stochastic predictive PID control algorithm using SIP criterion, in which the performance index not only contains the SIP of tracking error, but also includes the SIP of control input. And it is for nonlinear systems with exact model.…”