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.

Adaptive control of stochastic Hammerstein systems with dead-zone input non-linearity

“…However, another problem that has emerged is that the computational burden is too large to be implemented online. In Hong et al (2014), a new model-based PID controller is introduced for an H system, which is modelled using a B-spline neural network. This control scheme has some significant advantages over many other model-based controllers for H non-linear systems.…”

Identification and Nonlinear PID Control of Hammerstein Model using Polynomial Structures

“…Serval nonlinear predictive control algorithms are existed based on PID [13], neural networks [14], B-spline neural networks [15], Fuzzy logic [16] adaptive predictive control [17] [18]. In most algorithms for nonlinear predictive control, their performance functions are minimized using nonlinear programming techniques to compute the future manipulated variables in on-line optimization.…”

Robust adaptive control of Hammerstein nonlinear systems and its application to typical CSTR problems

“…In the previous contributions, many model-based controllers have been proposed for Hammerstein models: In [5,6], nonlinear controllers have been proposed based on the classical linear pole placement principle. The model parameters are obtained by the 'off-line' identification, conducted prior to the control experiment; Dong and Tan [7] have proposed a robust internal model control scheme to deal with the model mismatch in Hammerstein models; In [8][9][10], fuzzy-logic and neuralnetwork-based Hammerstein models are also explored to give different looks in controlling practical processes. In [11][12][13][14], nonlinear model predictive controllers are with applications to realistic process conditions.…”