Neural-network-based adaptive tracking control for Markovian jump nonlinear systems with unmodeled dynamics

Chang, Ru; Fang, Yiming; Li, Jianxiong; Liu, Le

doi:10.1016/j.neucom.2015.10.100

Cited by 6 publications

(9 citation statements)

References 34 publications

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“…Here, it is assumed that Á t is homogenous and irreducible [14,15,25], and for transition rate matrix˘, there is a unique stationary distribution = OE 1 ; 0.…”

Section: System Descriptions and Basic Assumptionsmentioning

confidence: 99%

“…An example of a cascading effect caused by actuator saturation is the 3 August 1996 blackout in Malaysia [8]. More recently, the adaptive neural control method was successfully developed to the Markovian jump nonlinear systems with triangular structure [25].In this paper, a decentralized prescribed performance adaptive tracking control design is developed for a class of Markovian jump uncertain nonlinear interconnected large-scale systems with input saturation. For example, a novel decentralized adaptive neural control scheme was proposed for a class of interconnected large-scale uncertain nonlinear time-delay systems with input saturation [9].…”

mentioning

confidence: 99%

“…It is well known that unknown nonlinearities exist extensively in many practical nonlinear systems, which make the control synthesis more difficult. More recently, the adaptive neural control method was successfully developed to the Markovian jump nonlinear systems with triangular structure [25]. More recently, the adaptive neural control method was successfully developed to the Markovian jump nonlinear systems with triangular structure [25].…”

mentioning

confidence: 99%

“…In order to deal with the unknown nonlinearities, some adaptive neural (fuzzy) control methods have been developed for interconnected nonlinear systems [20], for stochastic nonlinear systems [21,22], and for switched nonlinear systems [23,24]. More recently, the adaptive neural control method was successfully developed to the Markovian jump nonlinear systems with triangular structure [25].In this paper, a decentralized prescribed performance adaptive tracking control design is developed for a class of Markovian jump uncertain nonlinear interconnected large-scale systems with input saturation. By defining a novel state transformation with the performance function, the prescribed performance control problem is transformed to stabilization problem.…”

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confidence: 99%

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confidence: 99%

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Decentralized prescribed performance adaptive tracking control for Markovian jump uncertain nonlinear systems with input saturation

Chang

Fang

Liu

et al. 2016

Adaptive Control & Signal

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A decentralized prescribed performance adaptive tracking control problem is investigated for Markovian jump uncertain nonlinear interconnected large-scale systems. The considered interconnected large-scale systems contain unknown nonlinear uncertainties, unknown control gains, actuator saturation, and Markovian jump signals, and the Markovian jump subsystems are in the form of triangular structure. First, by defining a novel state transformation with the performance function, the prescribed performance control problem is transformed to stabilization problem. Then, introducing an intermediate control signal into the control design, employing neural network to approximate the unknown composite nonlinear function, and based on the framework of the backstepping control design and adaptive estimation method, a corresponding decentralized prescribed performance adaptive tracking controller is designed. It is proved that all the signals in the closed-loop system are bounded, and the prescribed tracking performances are guaranteed. A numerical example is provided to illustrate the effectiveness of the proposed control strategy. 256 R. CHANG ET AL. constraint and recurrent fuzzy neural networks were used to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system, the decentralized adaptive recurrent fuzzy neural networks control scheme was proposed in [6]. Based on the backstepping recursive design technique and the prescribed performance technique, a new adaptive fuzzy output-feedback control method was developed for a class of nonlinear large-scale systems, such that the tracking errors converged to an adjustable neighborhood of the origin with the prescribed performance bounds [7].For the interconnected large-scale systems, actuator saturation can lead to a cascading effect, and then, it may result in overall system collapse. An example of a cascading effect caused by actuator saturation is the 3 August 1996 blackout in Malaysia [8]. In recent years, some results on the decentralized control problem under actuator saturation have been reported. For example, a novel decentralized adaptive neural control scheme was proposed for a class of interconnected large-scale uncertain nonlinear time-delay systems with input saturation [9]. The decentralized H 1 controller was designed for a multi-zone space heating system, which is an interconnected dynamical system subject to external disturbances and input saturation [10]. Two decentralized sliding mode control laws were proposed and analyzed for spacecraft attitude synchronization under actuator failures, and the results could be extended to the case with control input saturation in the second controller [11]. The decentralized adaptive neural control was studied for a class of interconnected nonlinear systems with input saturation in [12,13].However, the aforementioned decentralized control methods in [2][3][4][5][6][7][8][9][10][11][12][13] are for non-switched nonlinear interconnected large-scale systems; therefore, they cannot be used to dea...

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“…Here, it is assumed that Á t is homogenous and irreducible [14,15,25], and for transition rate matrix˘, there is a unique stationary distribution = OE 1 ; 0.…”

Section: System Descriptions and Basic Assumptionsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Decentralized prescribed performance adaptive tracking control for Markovian jump uncertain nonlinear systems with input saturation

Chang

Fang

Liu

et al. 2016

Adaptive Control & Signal

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Event‐based adaptive finite‐time control scheme with improved performance assurance for nonlinear Markov jumping systems

Song,

Zhang,

Ahn

et al. 2024

Intl J Robust & Nonlinear

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SummaryThis article investigates the adaptive finite‐time control design with performance assurance for nonlinear Markov jumping systems. First, the radial basis function neural networks are applied to tackle unknown nonlinear terms. Subsequently, within the framework of the command‐filtered backstepping control, the new error compensation signals are devised to remove the effect caused by filter error. Additionally, an adaptive finite‐time prescribed performance control law with an event‐triggered mechanism is developed to achieve a balance between tracking performance and bandwidth consumption, which ensures that the closed‐loop system is practically finite‐time stable in mean square and guarantees that the tracking error adjusts to the predetermined area. Finally, two simulation examples are provided to verify the validity and superiority of the devised scheme.

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Introduction

Luan

Liu

2023

Lecture Notes in Control and Information Sciences

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Neural-network-based adaptive tracking control for Markovian jump nonlinear systems with unmodeled dynamics

Cited by 6 publications

References 34 publications

Decentralized prescribed performance adaptive tracking control for Markovian jump uncertain nonlinear systems with input saturation

Decentralized prescribed performance adaptive tracking control for Markovian jump uncertain nonlinear systems with input saturation

Event‐based adaptive finite‐time control scheme with improved performance assurance for nonlinear Markov jumping systems

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