Abstract
This paper proposes an adaptive neural network (NN) control approach for a direct-current (DC) system with full state constraints. To guarantee that state constraints always remain in the asymmetric time-varying constraint regions, the asymmetric time-varying Barrier Lyapunov Function (BLF) is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time-varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with the existing works on DC systems. As far as we know, this system is the first studied situations with time-varying constraints. Using Lyapunov analysis, all signals in the closed-loop system are proved to be bounded and the constraints are not violated. In this paper, the effectiveness of the control method is demonstrated by simulation results.
Highlights
Due to the requirements of practice and the development of theory, the controller design of uncertain system has become a new research direction and attracted more and more scholars’ attention
(3) A novel adaptive tracking controller based on the neural networks and backstepping technique is structured to guarantee that all signals in the closed-loop system are bounded, the tracking errors converge to a small neighborhood of zero and the time-varying state constraints are not transitioned
This paper presents an adaptive tracking controller based on a backstepping technique with the asymmetric time-varying Barrier Lyapunov Function (BLF) for the DC motor systems
Summary
Due to the requirements of practice and the development of theory, the controller design of uncertain system has become a new research direction and attracted more and more scholars’ attention. In [17], adaptive control schemes based on neural networks were proposed for nonlinear systems with unknown functions. Based on neural networks and fuzzy logic systems, the significant studies proposed the novel adaptive tracking control methods for nonlinear SISO systems in [18–20] and MIMO systems in [21–23]. This paper presents an adaptive NN tracking control method for DC motor systems with time-varying state constraints. (1) The time-varying state constraints are first considered in the DC motor systems; comparing with the existing on DC motor systems, the proposed control method is more general and extensive in the engineering field. (3) A novel adaptive tracking controller based on the neural networks and backstepping technique is structured to guarantee that all signals in the closed-loop system are bounded, the tracking errors converge to a small neighborhood of zero and the time-varying state constraints are not transitioned The contributions of this paper are summarized as follows. (1) The time-varying state constraints are first considered in the DC motor systems; comparing with the existing on DC motor systems, the proposed control method is more general and extensive in the engineering field. (2) To guarantee that the state constraints always remain in the time-varying constrained sets, the asymmetric time-varying BLF is utilized. (3) A novel adaptive tracking controller based on the neural networks and backstepping technique is structured to guarantee that all signals in the closed-loop system are bounded, the tracking errors converge to a small neighborhood of zero and the time-varying state constraints are not transitioned
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