Synchronization Control for Unreliable Network Systems in Intelligent Robots

This paper addresses the design of a sampled-data model predictive control (MPC) strategy for linear parameter-varying (LPV) systems. A continuous-time prediction model, which takes into account that the samples are not necessarily periodic and that plant parameters continuously vary with time, is considered. Moreover, it is explicitly assumed that the value of the parameters used to compute the optimal control sequence is measured only at the sampling instants. The MPC approach proposed by Kothare et al. [1], where the basic idea consists in solving an infinite horizon guaranteed cost control problem at each sampling time using linear matrix inequalities (LMI) based formulations, is adopted. In this context, conditions for computing a sampled-data stabilizing LPV control law that provides a guaranteed cost for a quadratic performance criterion under input saturation are derived. These conditions are obtained from a parameter-dependent looped-functional and a parameter-dependent generalized sector condition. A strategy that consists in solving convex optimization problems in a receding horizon policy is therefore proposed. It is shown that the proposed strategy guarantees the feasibility of the optimization problem at each step and leads to the asymptotic stability of the origin. The conservatism reduction provided by the proposed results, with respect to similar ones in the literature, is illustrated through numerical examples.

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“…Considering now (39) and (40), the auxiliary matrices defined in (38) and that σ k (τ) = σ k (0) + δ k (τ), one obtains the following:…”

Section: Stabilization Conditionsmentioning

confidence: 99%

Aperiodic sampled-data MPC strategy for LPV systems

Palmeira

Silva

Flores

et al. 2022

Journal of the Franklin Institute

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“…Y Wu et al 37 proposed an innovative strategy to research the matter of synchronization control for unreliable network systems.…”

Section: Fundamentals Of Annsmentioning

confidence: 99%

Heavy trucks with intelligent control of active suspension based on artificial neural networks

Hamza

Yahia

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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The active control of a suspension system is meant to provide an isolated behavior of the system spring-mass (for example, increased comfort and performance). During this article, we are going to explain the importance of developing an intelligent control approach for active truck suspensions based on the artificial neural network. From where the main objective of this article is to obtain a mathematical model for active suspension systems then build a hydraulic model for active suspension control for trucks using an artificial neural network. In this article, a corresponding artificial neural network nonlinear active suspension controller has been designed and optimized for approximate road profiles, using simulation according to International Organization for Standardization 2631-5 and International Organization for Standardization 8608 standardizations. The model developed with MATLAB Toolbox, estimated and validated from data collected during tests carried out with a truck in other research work. To model the system, the laws of physics are used to describe the system and experimental data or information supplied about the system to determine the parameters of the system. The statement of the problem of this research is to develop a robust artificial neural network controller for the nonlinear active suspension system of the heavy truck that can improve the performances and its verifications using graphical and simulation output. The results of the simulation show that the methodology offers excellent performance. In addition, the robustness of the artificial neural network hydraulic controller is demonstrated for a variety of road profiles that increase the capabilities of the proposed methodology and prove its effectiveness.

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“…The networked systems, which connect the controlled objects, sensors, controllers, and actuators in different geographical locations through the communication networks, have garnered particular attention due to its advantages of high flexibility, strong reliability, easy maintenance, and convenient installation. [1][2][3][4][5][6][7][8] In the process of the development of the real economy, networked control is a good choice for the deep integration of communication technology and real economy, and is increasingly widely used in the fields such as intelligent manufacturing, aerospace, UAV formation, and industrial network, 9,10 and so forth. Because of its important theoretical value and engineering practicability, it is of far-reaching significance to study the stability analysis and synthesis of networked systems.…”

Section: Introductionmentioning

confidence: 99%

A discretization approach to sampled‐data stabilization of networked systems with successive packet losses

Jin

Deng

et al. 2021

Intl J Robust & Nonlinear

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This article is concerned with the stabilization problem for a class of networked systems subject to successive packet losses. Different from the input delay approach used in some existing literature, the continuous-time system under consideration is first converted into a discrete-time stochastic system with system matrices subject to stochastic characteristic. In order to deal with the difficulties in the calculations of mathematical expectations of both matrix exponential and integral of matrix exponential function, the upper bound of successive packet losses and packet drop rate are assumed to be known and then the probabilities of the number of successive packet losses taking each value in a bounded set are calculated. Based on this, stability criteria are derived by recurring to the law of total expectation, which guarantee the exponential mean-square stability of resulting closed-loop discrete-time stochastic system with a prescribed H ∞ performance. Moreover, a controller design procedure is then proposed. Finally, to verify the analysis results and testify the effectiveness and applicability of the designed algorithm, a numerical simulation example is given.

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Synchronization Control for Unreliable Network Systems in Intelligent Robots

Cited by 20 publications

References 33 publications

Aperiodic sampled-data MPC strategy for LPV systems

Aperiodic sampled-data MPC strategy for LPV systems

Heavy trucks with intelligent control of active suspension based on artificial neural networks

A discretization approach to sampled‐data stabilization of networked systems with successive packet losses

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