Preview control of polytopic time‐varying systems subject to actuator saturation

This paper is concerned with the analysis of an extended dissipativity performance for a class of bidirectional associative memory (BAM) neural networks (NNs) having time‐varying delays. To achieve this, the idea of the delay‐partitioning approach is used, where the range of time‐varying delay factors is partitioned into a finite number of equidistant subintervals. A delay‐partitioning based Lyapunov–Krasovskii function is introduced on these intervals, and some new delay‐dependent extended dissipativity results are established in terms of linear matrix inequalities, which also depend on the partition size of the delay factor. Further, numerical examples are performed to acknowledge the extended dissipativity performance of delayed discrete‐time BAM NN; further, four case studies were explored with their simulations to validate the impact of the delay‐partitioning approach.

Section: Introductionmentioning

confidence: 99%

Extended dissipative output‐feedback control for discrete‐time bidirectional associative memory neural networks via delay‐partitioning approach

Adhira

Nagamani

2023

“…If the input constraint problem is not considered during the siding mode controller design process, the closed-loop system might be unstable for the integral saturation of SMC. For now, input constraint has been contained in many real scenarios [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Discrete‐time extended state observer‐based model‐free adaptive constrained sliding mode control with modified prescribed performance via CFDL and PFDL

Huang,

Dong,

Zhang

et al. 2023

To tackle the trajectory tracking problem and achieve high control accuracy in many actual nonlinear systems with unknown dynamics and input saturation, a novel discrete‐time extended state observer‐based model‐free adaptive constrained sliding mode control with modified prescribed performance is investigated via compact‐form dynamic linearization (CFDL) and partial‐form dynamic linearization (PFDL). Firstly, the original non‐affine system is turned into an affine one comprising an unknown nonlinear term and a linearly parametric term affine to the input via both PFDL and CFDL. Then, a discrete‐time extended state observer (DESO) is used to estimate the lumped disturbance containing the unknown nonlinear time‐varying term and the term relevant to the estimation error of pseudo partial derivative (PPD) parameter. Furthermore, a modified prescribed performance function is introduced in the model‐free adaptive sliding mode control scheme to keep the output tracking error in the prescribed bound without causing any asymmetric offset error in the steady‐state. Meanwhile, to suppress the influence of input saturation on the control system, an anti‐windup compensator is used. Finally, rigorous theoretical analyses show the robust convergence of the tracking error via the proposed CFDL and PFDL‐based methods under external disturbances. Simulations verify the superiority of the modified prescribed performance function, DESO, and anti‐windup compensator in the proposed method. Also, the effects of the PFDL‐based method and the CFDL‐based one are compared during the simulation.

“…Liu et al [24] combined optimal PC with

{H}_{\infty }

control to solve the disturbance attenuation problem in a zero‐sum game framework. In recent research progress, a novel method was proposed to track a previewable reference signal in the polytopic time‐varying system with input saturation [25]. The problem of observer‐based decentralized robust

{H}_{\infty }

output tracking control with preview action in a class of uncertain and disturbed nonlinear interconnected systems was investigated [26].…”

Section: Introductionmentioning

confidence: 99%

Estimation and rejection of periodic disturbance in a preview repetitive control system

Lan,

Liu

2023

This paper deals with the problem of two‐dimensional (2D) system‐based preview repetitive control (PRC) with external periodic disturbance for a class of continuous‐time linear systems. First, a repetitive learning‐based estimator (RLE) is incorporated into the controller block to compensate for the periodic disturbance. Next, the PRC law with repetitive learning compensation disturbance is obtained by combining the observer and RLE. Then, we combine the 2D system theory with the linear matrix inequality (LMI) method to derive sufficient conditions to ensure the robustness and stability of the closed‐loop system. Finally, numerical simulations verify the superiority of the proposed method.