Approximately optimal tracking control for discrete time-delay systems with disturbances

In this research, a hybrid control system is proposed to address the temperature control of an infrared dryer. The control system includes a feedback-predictive controller and a neural network steady state control law. The feedback-predictive controller outputs the amplified value of the predicted error as the transient control command. The predictive model was employed to suppress the undesirable effect of the dead-time of the system. A multilayer perceptron was designed and tested based on a control equilibrium point and steady state control to be used as a feedforward controller. The stability of the control system in a continuous domain was proved with no limit on the amplification gain of the predictive-feedback controller. In other words, there is no concern about losing stability with accelerating convergence towards the reference. The entire control system was constructed in Simulink and compiled to a C code and applied on the experimental setup. Experimental results are outstanding in comparison with the results of an interactively tuned IMC-based PID controller.

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“…Considering (10), (15), and (17), the control law introduced in (9) is applicable as shown in Fig. 3.…”

Section: Predictive Approach For Feedback Control and Final Control Smentioning

confidence: 99%

Neuro‐Predictive Control of an Infrared Dryer with a Feedforward‐Feedback Approach

Mohammadzaheri¹,

Chen²,

Mirsepahi³

et al. 2014

Asian Journal of Control

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“…In the past decade, a great deal of work has been devoted to time delay systems (see, e.g., [15][16][17][18][19][20][21][22][23] and the references therein) and some results on tracking control for time delay systems have been reported (see, e.g., [24][25][26][27][28][29][30] and the references therein). However, to the best of our knowledge, there is little work being done on PI tracking control for MJSs, not to mention the issue on nonlinear Markov jump systems (NMJS) with partially known transition probabilities.…”

Section: Introductionmentioning

confidence: 99%

Gain-scheduled PI tracking control on stochastic nonlinear systems with partially known transition probabilities

Yin

Shi

Liu

2011

Journal of the Franklin Institute

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“…For deterministic discrete-time systems we have promoted an approximation approach to generate the suboptimal control law, which has avoided the complexity of solving two point boundary values problems generated by optimal control with time-delay terms [1]. In practical engineering systems, there are various external disturbances affecting the performance of systems.…”

Section: Introductionmentioning

confidence: 99%

Critical stability and stabilization of discrete-time stochastic systems and its applications

Sun

Zhang

2011

Int. J. Control Autom. Syst.

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In this paper, critical stability and critical stabilization for discrete stochastic systems with both state and control dependent noise are discussed via the spectrum technique. The Popov-BelevitchHautus (PBH) criterion for exact observability in a discrete version is presented. As applications, some interesting results on a class of generalized Lyapunov equations (GLE), unremovable spectra and discrete generalized algebraic Riccati equation (GARE) are obtained. Finally, the problem of assigning the spectra of discrete stochastic systems in a specified disk is considered and some numerical examples are given to demonstrate our results.

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Approximately optimal tracking control for discrete time-delay systems with disturbances

Cited by 23 publications

References 10 publications

Neuro‐Predictive Control of an Infrared Dryer with a Feedforward‐Feedback Approach

Neuro‐Predictive Control of an Infrared Dryer with a Feedforward‐Feedback Approach

Gain-scheduled PI tracking control on stochastic nonlinear systems with partially known transition probabilities

Critical stability and stabilization of discrete-time stochastic systems and its applications

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