2008•IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)Requires access

A Predictive Control-Based Approach to Networked Hammerstein Systems: Design and Stability Analysis

Yun‐Bo Zhao, Shuai Liu, D. Rees

Open publisher page 53 citations

Abstract

In this paper, a predictive control-based approach is proposed for a Hammerstein-type system which is closed through some form of network. The approach uses a two-step predictive controller to deal with the static input nonlinearity of the Hammerstein system and a delay and dropout compensation scheme to compensate for the communication constraints in a networked control environment. Theoretical results are presented for the closed-loop stability of the system. Simulation examples illustrating the validity of the approach are also presented.

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What this paper is about

In this paper, a predictive control-based approach is proposed for a Hammerstein-type system which is closed through some form of network. The approach uses a two-step predictive controller to deal with the static input nonlinearity of the Hammerstein system and a delay and dropout compensation scheme to compensate for the communication constraints in a networked control environment. Theoretical results are presented for the closed-loop stability of the system. Simulation examples illustrating the validity of the approach are also presented.

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OpenAlex reports 53 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In this paper, a predictive control-based approach is proposed for a Hammerstein-type system which is closed through some form of network. The approach uses a two-step predictive controller to deal with the static input nonlinearity of the Hammerstein system and a delay and dropout compensation scheme to compensate for the communication constraints in a networked control environment. Theoretical results are presented for the closed-loop stability of the system. Simulation examples illustrating the validity of the approach are also presented.

Key concepts: Model predictive control, Control theory (sociology), Dropout (neural networks), Compensation (psychology), Computer science, Stability (learning theory), Nonlinear system, Networked control system

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