Abstract
This paper proposes a command filtering backstepping (CFB) scheme with full-state constraints by leading into time-varying barrier Lyapunov functions (T-BLFs) for a dual-motor servo system with partial asymmetric dead-zone. Firstly, for the convenience of the controller design, the conventional partial asymmetric dead-zone model was replaced with a new smooth differentiable model owing to its non-smoothness. Secondly, neural networks (NNs) were utilized to approximate the nonlinearity that exists in the dead-zone model, improving the control performance. In addition, CFB was utilized to deal with the inherent computational explosion problem of the traditional backstepping method, and an error compensation mechanism was introduced to further reduce the filtering errors. Then, by applying the T-BLF to the CFB process, the states of the system never violated the prescribed constraints, and all signals in the dual-motor servo system were bounded. The tracking error and synchronization error could converge to a small desired neighborhood of the origin. In the end, the effectiveness of the proposed control scheme was verified through simulations.
Highlights
Over the past few decades, tracking control of motors have attracted considerable attention in the field of control theory and engineering [1,2,3,4,5]
Fuzzy finite-time command filtering backstepping (CFB) was developed for position tracking control of induction motors with input saturation [15]
Taking these factors into account, adaptive neural networks (NNs) based on CFB for the dual-motor servo system with full-state constraints was investigated in this paper
Summary
Over the past few decades, tracking control of motors have attracted considerable attention in the field of control theory and engineering [1,2,3,4,5]. Fuzzy finite-time CFB was developed for position tracking control of induction motors with input saturation [15] It guarantees the convergence of the tracking error in finite time and improves the dynamic performance of the control system. Taking these factors into account, adaptive NNs based on CFB for the dual-motor servo system with full-state constraints was investigated in this paper. (2) By using CFB, the issue of “explosion of complexity” that arises from the traditional backstepping in the dual-motor system was solved, and the error compensation mechanism introduced can effectively reduce the filtering errors to gain a smaller tracking error. (3) In dual-motor servo systems, adaptive NNs are used to approximate the nonlinear parts, improving the control precision of the system.
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