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This paper is devoted to the problem of prescribed performance trajectory tracking control for symmetrical underactuated unmanned surface vessels (USVs) in the presence of model uncertainties and input quantization. By combining backstepping filter mechanisms and adaptive algorithms, two robust control architectures are investigated for surge motion and yaw motion. To guarantee the prespecified performance requirements for position tracking control, the constrained error dynamics are transformed to unconstrained ones by virtue of a tangent-type nonlinear mapping function. On the other hand, the inaccurate model can be identified through radial basis neural networks (RBFNNs), where the minimum learning parameter (MLP) algorithm is employed with a low computational complexity. Furthermore, quantization errors can be effectively reduced even when the parameters of the quantizer remain unavailable to designers. Finally, the effectiveness of the proposed controllers is verified via theoretical analyses and numerical simulations. [ABSTRACT FROM AUTHOR]

Electric vehicle has become the main trend of the development of automobile industry. As a highly symmetrical system, the characteristics of drive motor will have a great impact on the driving comfort. Aiming at the control regulation of permanent magnet synchronous motor (PMSM) drive, a model-free and non-cascade sliding mode control with a fast-reaching law is proposed in this paper. Firstly, the mathematical model of PMSM is constructed as an ultra-local model without considering any motor parameters. Then, to improve the response speed, an integral sliding mode method with a fast reaching law is proposed, and the fast convergence can be realized. The controller system adopts single loop non-cascade control, which greatly simplifies the system structure. In order to further improve the anti-disturbance performance of the system, the dual disturbance observers are used to compensate the disturbance through feed-forward control. The stability of the system is proved by Lyapunov. Finally, the experimental results show that the proposed method has faster convergence speed and stronger anti-disturbance ability. [ABSTRACT FROM AUTHOR]