Observer‐based adaptive control for stochastic nonstrict‐feedback systems with unknown backlash‐like hysteresis

Bai, Lu; Zhou, Qi; Wang, Lijie; Yu, Zhandong; Li, Hongyi

doi:10.1002/acs.2780

Cited by 53 publications

(18 citation statements)

References 47 publications

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“…Just as in other hybrid systems models, complexity is a cause for concern, we point out that the number of the unknowns is linear with the number of places and of transitions (Vargas, 2013; Dotoli et al, 2008). Although it is possible to use linear and robust control theory techniques for controller design, and heuristics or newly proposed design methods (Ahn et al, 2017; Bai et al, 2017; Kommuri et al, 2016; Sakthivel et al, 2017; Yang et al, 2016; Zhou et al, 2016, 2017), it is not possible to do both concurrently. Such issues will be the topics of further investigations.…”

Section: Simulation Examplementioning

confidence: 99%

A new pattern based Petri net to model sintering production process

LIU

2017

Transactions of the Institute of Measurement and Control

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In this paper, a new Petri net model based on pattern recognition method is presented for describing a certain stochastic hybrid systems. The application of this method concerns sintering production process. The main idea consists in describing the variation of patterns over time through the so-called ‘pattern class variable’ rather than state variable or output variable. A new petri net control model is constructed based on pattern class variable. The marks are defined as posterior probability of the cluster. By redefining the marks and transition on the basis of ordinary Petri nets, it can represent any combination of discrete-event and continuous element and has probability property. The simulation results are provided based on real plant data to illustrate the effectiveness of the proposed approach. This method might provide the satisfied results for the practical applications without having the exact mathematical models.

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Section: Simulation Examplementioning

confidence: 99%

A new pattern based Petri net to model sintering production process

LIU

2017

Transactions of the Institute of Measurement and Control

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“…At present, the backstepping technique has been extensively adopted to construct controllers for various kinds of nonlinear systems. [1][2][3][4][5] The finite-time control and fault tolerant control problems have sparked considerable research interest. Compared with infinite-time stability, finite-time stability is a stronger result in the sense of guaranteeing that system states are driven to a specified goal within a finite-time.…”

Section: Introductionmentioning

confidence: 99%

Fixed‐time fault tolerant control for a class of switched nonlinear systems

YangTingting

LiYongming

2020

Adaptive Control & Signal

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This work addresses the fixed-time fault tolerant control problem for nonstrict feedback switched nonlinear systems whose subsystems have chained integrators with positive odd rational powers. Besides, the powers of different channels and switched subsystems can be also possibly different. The fixed-time fault tolerant control law is formulated after using the adding one power integrator method, homogeneous system theory, backstepping technique, nested saturation control approach, which is capable of guaranteeing the global fixed-time stability for the resulting closed-loop stability. A numerical simulation example is given to validate the feasibility and effectiveness of the developed control strategy.

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“…The nonstrict‐feedback structure was considered in the works of Zhou et al and Li et al, and the problem of observer‐based adaptive fuzzy control was investigated in the work of Wang et al for nonstrict‐feedback systems. The stochastic disturbances were considered in other works for adaptive tracking control problem. In the works of Zhou et al and Li and Tong, multiple‐input–multiple‐output nonlinear systems were considered for the approximation‐based adaptive tracking control.…”

Section: Introductionmentioning

confidence: 99%

Decentralized intelligent tracking control for uncertain high‐order stochastic nonlinear strong interconnected systems in drift and diffusion terms

2018

Intl J Robust & Nonlinear

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This paper focuses mainly on decentralized intelligent tracking control for a class of high-order stochastic nonlinear systems with unknown strong interconnected nonlinearity in the drift and diffusion terms. For the control of uncertain high-order nonlinear systems, the approximation capability of RBF neural networks is utilized to deal with the difficulties caused by completely unknown system dynamics and stochastic disturbances, and only one adaptive parameter is constructed to overcome the overparameterization problem. Then, to address the problem from high-order strong interconnected nonlinearities in the drift and diffusion terms with full states of the overall system, by using the monotonically increasing property of the bounding functions, the variable separation technique is achieved. Lastly, based on the Lyapunov stability theory, a decentralized adaptive neural control method is proposed to reduce the number of online adaptive learning parameters. It is shown that, for bounded initial conditions, the designed controller can ensure the semiglobally uniformly ultimate boundedness of the solution of the closed-loop system and make the tracking errors eventually converge to a small neighborhood around the origin. Two simulation examples including a practical example are used to further illustrate the effectiveness of the design method. KEYWORDS decentralized intelligent control, neural network approximation, stochastic nonlinear systems, uncertain high-order nonlinear systems 2780Recently, many further studies have been developed for the control of high-order nonlinear systems. Sun et al 2,3 used the power integrator method to deal with the control for high-order nonlinear systems. The global adaptive control design was investigated in the work of Zhang et al 4 for uncertain high-order nonlinear systems based on a homogeneous domination approach. 5 The decentralized output-feedback control was developed in the work of Li et al 6 for a class of large-scale high-order stochastic nonlinear systems, and in the work of Sun et al, 7 the improved generalization of the homogeneous domination stabilization scheme was studied. However, the above-mentioned strategies fail to realize the tracking control for completely unknown high-order nonlinear systems; therefore, it is necessary to use the approximation-based control methods.As a solution to the system uncertainties, neural networks (NNs) and fuzzy logic systems have been broadly applied to the modeling and control design. [8][9][10][11][12] By using the approximation property of NNs, Wang and Wang 13 proposed a dynamic learning from the adaptive control. In the work of Dai et al, 14 neural learning control with predefined performance was studied for a marine surface vessel. The nonstrict-feedback structure was considered in the works of Zhou et al 15 and Li et al, 16 and the problem of observer-based adaptive fuzzy control was investigated in the work of Wang et al 17 for nonstrict-feedback systems. The stochastic disturbances were considered in other works 18-20...

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Observer‐based adaptive control for stochastic nonstrict‐feedback systems with unknown backlash‐like hysteresis

Cited by 53 publications

References 47 publications

A new pattern based Petri net to model sintering production process

A new pattern based Petri net to model sintering production process

Fixed‐time fault tolerant control for a class of switched nonlinear systems

Decentralized intelligent tracking control for uncertain high‐order stochastic nonlinear strong interconnected systems in drift and diffusion terms

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