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A neural network (NN)‐based cascade control system is developed, in which the primary proportional–integral–derivative (PID) controller is constructed by NN. A new entropy‐based measure, named the centred error entropy (CEE) index, which is a weighted combination of the error cross‐correntropy (ECC) criterion and the error entropy criterion (EEC), is proposed to tune the NN‐PID controller. The purpose of introducing CEE in controller design is to ensure that the uncertainty in the tracking error is minimised and also the peak value of the error probability density function being controlled toward zero. The NN‐controller design based on this new performance function is developed and the convergent conditions are investigated using a linearisation technique. During the control process, the CEE index is estimated by a Gaussian kernel function. Adaptive rules are developed to update the kernel size in order to achieve more accurate estimation of the CEE index. This NN cascade control approach is applied to superheated steam temperature control of a simulated power plant system, from which the effectiveness and strength of the proposed strategy are discussed by comparison with NN‐PID controllers tuned with EEC and ECC criterions. [ABSTRACT FROM AUTHOR]

In this brief, a new tracking control algorithm for a class of networked control systems (NCSs) with non-Gaussian random disturbances and delays is proposed. Due to non-Gaussian random noises involved in the systems, solely controlling the expected value of the linear quadratic performance index is insufficient to obtain a satisfactory optimal control algorithm. The proposed method in this note applies the ( h,\phi )- entropy of the quadratic performance index to characterize the randomness of the closed-loop system. In order to calculate the entropy, the formulation of the joint probability density functions (JPDFs) of the quadratic performance index is presented in terms of known JPDFs of disturbances and time-delay. By minimizing the entropy of the performance index, a new control algorithm is obtained for the considered nonlinear and non-Gaussian NCSs. In addition, the proposed control strategy is applied to a networked DC motor control system, which is subjected to non-Gaussian random disturbances and delays. The experimental results show the effectiveness of the obtained method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an improved cascade control methodology for superheated processes is developed, in which the primary PID controller is implemented by neural networks trained by minimizing error entropy criterion. The entropy of the tracking error can be estimated recursively by utilizing receding horizon window technique. The measurable disturbances in superheated processes are input to the neuro-PID controller besides the sequences of tracking error in outer loop control system, hence, feedback control is combined with feedforward control in the proposed neuro-PID controller. The convergent condition of the neural networks is analyzed. The implementation procedures of the proposed cascade control approach are summarized. Compared with the neuro-PID controller using minimizing squared error criterion, the proposed neuro-PID controller using minimizing error entropy criterion may decrease fluctuations of the superheated steam temperature. A simulation example shows the advantages of the proposed method. [Copyright &y& Elsevier]