A model‐based PID controller for Hammerstein systems using B‐spline neural networks

Abstract

SUMMARYIn this paper, a new model-based proportional-integral-derivative (PID) tuning and controller approach is introduced for Hammerstein systems that are identified on the basis of the observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a B-spline neural network. The control signal is composed of a PID controller, together with a correction term. Both the parameters in the PID controller and the correction term are optimized on the basis of minimizing the multistep ahead prediction errors. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on B-spline neural networks and the associated Jacobian matrix are calculated using the de Boor algorithms, including both the functional and derivative recursions. Numerical examples are utilized to demonstrate the efficacy of the proposed approaches.

A novel filtering-based recursive identification method for a fractional-order Hammerstein state space system with piecewise nonlinearity

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Separable Synchronous Gradient‐Based Iterative Algorithms for the Nonlinear ExpARX System

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Weighted multi‐innovation parameter estimation for a time‐varying Volterra–Hammerstein system with colored noise

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