Robust adaptive control of uncertain nonlinear systems with unmodeled dynamics using command filter

Yang, Yongliang; Tang, Liqiang; Zou, Wencheng; Ding, Dawei

doi:10.1002/rnc.5717

Cited by 25 publications

(22 citation statements)

References 34 publications

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“…However, in many practical applications, system dynamics is unknown. It is common to approximate system unknown dynamics by intelligent approximators such as neural networks (NNs) and fuzzy logic systems (FLSs) 17–22 . A NN‐based controller has been proposed in Reference 23 for a class of nonlinear systems with state constraints and state delays.…”

Section: Introductionmentioning

confidence: 99%

“…It is common to approximate system unknown dynamics by intelligent approximators such as neural networks (NNs) and fuzzy logic systems (FLSs). [17][18][19][20][21][22] A NN-based controller has been proposed in Reference 23 for a class of nonlinear systems with state constraints and state delays. In Reference 24, an adaptive FLS-based control scheme has been designed for a class of large-scale nonlinear systems with time-varying asymmetric state constraints.…”

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confidence: 99%

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Observer‐based controller for nonaffine time‐delayed systems subject to input nonlinearities, state constraints, and unknown control direction

Mohit

Shahrokhi

Kamalamiri

2022

Adaptive Control & Signal

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In this work, an adaptive observer-based control scheme has been designed for uncertain nonaffine nonstrict feedback systems subject to state time delay, various types of input nonlinearities, time-varying asymmetric state constraints, and unknown control direction. Compared to the existing controllers for systems with state constraints, the designed control scheme in this work can be applied to state-constrained systems with nonaffine structure subject to state delays, unknown control direction, and different types of input nonlinearities, while full-states measurement is not required. Moreover, by introducing a novel saturated Nussbaum function in the present work, not only has the problem of unknown control direction been handled, but also the probability of a control shock due to applying the conventional Nussbaum functions has been eliminated. The system unknown dynamics has been approximated by using neural networks (NNs), and by updating the maximum norm of NNs weight vectors, only one adaptive law is required for the controller implementation. By employing appropriate Lyapunov functions, it has been shown that all signals in the closed-loop system are bounded, state constraints are satisfied, and the output tracking error can be made small by selecting appropriate design parameters.The effectiveness of the designed control scheme has been shown through two simulation studies. Moreover, the superiority of the proposed saturated Nussbaum function in comparison with conventional Nussbaum functions has been shown via simulation. K E Y W O R D Sinput nonlinearity, intelligent approximator, nonstrict feedback systems, state constraints, unknown control direction INTRODUCTIONRecently, control of systems with constraints has attracted a great deal of attention because many real systems are subject to constraints such as wheeled mobile robotic systems 1 and chemical processes. 2 Constraints may appear as physical restrictions, performance requirements, or safety considerations. 3 Input, state, and output constraints are the three major types of constraints investigated in the literature. For example, an adaptive controller has been designed for a class of 2122

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Observer‐based controller for nonaffine time‐delayed systems subject to input nonlinearities, state constraints, and unknown control direction

Mohit

Shahrokhi

Kamalamiri

2022

Adaptive Control & Signal

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“…In [19], another auxiliary signal was constructed using the error between the desired control input and the saturation control input. In [20], to solve the disadvantages of conventional methods based on the hyperbolic tangent function, an nthorder auxiliary dynamic system was skillfully constructed to avoid the effect of input saturation.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Adaptive Fault-Tolerant Cooperative Control for Multiple UAVs with Input Saturation and State Constraints

et al. 2022

International Journal of Aerospace Engineering

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This paper proposes a fault-tolerant cooperative control (FTCC) scheme for multiple UAVs in a distributed communication network against input saturation, full-state constraints, actuator faults, and unknown dynamics. Firstly, by considering physical limitations, an auxiliary control signal is designed to simplify the analysis process. Secondly, to avoid the difficulty in the back-stepping design caused by full-state constraints, virtual control signals are constructed to transform constrained variables, while the dynamic surface control is adopted to avoid the phenomenon of “differential explosion.” Thirdly, a disturbance observer (DO) is designed to estimate the unknown uncertainty caused by parameter uncertainty and actuator fault. Moreover, a recurrent wavelet fuzzy neural network (RWFNN) is used to compensate for the estimation errors of DO. Finally, it is proved that all states are uniformly ultimately bounded (UUB) by Lyapunov and invariant set theory. The effectiveness of the proposed scheme is further demonstrated by the simulation results.

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“…A number of control design schemes for nonlinear systems with input saturation have been reported. [25][26][27][28][29] The auxiliary dynamic system was employed to cope with the saturation effect for SISO nonlinear systems, 27 where the explosion of complexity is solved through the command filter with signal compensation. The adaptive controller by using Gaussian error function to approximate nonsmooth saturation nonlinearity was developed for MIMO nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

“…29 However, the prescribed performance was not achieved in above works. [27][28][29] For PPC scheme, faster transient response may require larger control signal, and, thereby, leading to input saturation problem. Hence, it is very significant to consider the saturation constraint issue when accomplishing PPC for nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Command‐filtered‐based neuroadaptive control for multi‐input multi‐output saturated nonstrict‐feedback nonlinear systems with prescribed tracking performance

Yang

Liu

Guo

2022

Adaptive Control & Signal

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Summary In this article, the prescribed performance control strategy is extended to multi‐input multi‐output nonstrict‐feedback nonlinear systems with asymmetric input saturation, and not only each element in tracking error vector converges to a prescribed small region within preassigned finite time, but also the converging mode during the preset time is prespecifiable and controllable explicitly. By blending the barrier function with novel speed function, a prescribed performance controller using command‐filtered‐based vector‐backstepping design framework is proposed to steer the tracking error vector for the first time, where the boundedness of filter errors is guaranteed by sufficiently small time constant and an error compensator is constructed to handle the effects of filter errors. To attenuate the adverse effects resulted from nondifferentiable input saturation, hyperbolic tangent function is utilized to estimate asymmetric saturation function such that the control input is designed as a new state variable with initial value of zero in augmented system. Nussbaum function is employed to overcome singularity problem caused by the differentiation of hyperbolic tangent function. At each step of backstepping design, the universal approximation property of neural network and the command filter system are utilized to approximate uncertain dynamics and to solve algebraic loop obstacle due to nonstrict‐feedback structure, respectively. Moreover, only one parameter needs to be updated online to cope with the lumped uncertain dynamics by virtual parameter technology, rendering a control strategy with low complexity computation. The validity of the presented controller is verified by theoretical analysis and two‐link robotic system.

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Robust adaptive control of uncertain nonlinear systems with unmodeled dynamics using command filter

Cited by 25 publications

References 34 publications

Observer‐based controller for nonaffine time‐delayed systems subject to input nonlinearities, state constraints, and unknown control direction

Observer‐based controller for nonaffine time‐delayed systems subject to input nonlinearities, state constraints, and unknown control direction

Decentralized Adaptive Fault-Tolerant Cooperative Control for Multiple UAVs with Input Saturation and State Constraints

Command‐filtered‐based neuroadaptive control for multi‐input multi‐output saturated nonstrict‐feedback nonlinear systems with prescribed tracking performance

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