2,312 publications from this institution
This paper investigates the trajectory tracking control problem for a class of unmanned surface vehicles subject to unknown uncertainties, output constraints and input quantization. Adaptive neural networks (NNs) are applied to handle the uncertainties and quantization while output-dependent universal barrier functions are used to cope with output constraints. Due to limited communication bandwidths, the uniform quantizer is used to quantize input signals before being sent. Based on state feedback, an adaptive NN-based control strategy is proposed to solve the tracking problem with time-invariant output constraints, and then another NN-based control law is developed to deal with the time-varying output constraints. It is proved that the desired output constraints can be achieved and the tracking errors can converge to zero asymptotically. Further, the proposed control law is extended to the case without output constraints. Finally, simulation results are presented to demonstrate the effectiveness of the new control strategies.
This paper proposes a module-based and unified approach to chaotic circuit design, where the description is based on the state equations without physical dimensions for simplicity of a general discussion. The main design process consists of transformation of state variables, transformation from differential to integral operations, and transformation of the time-scale. The designed circuit consists of anti-adder module integrator module, and inverter module. A novel 3-scroll Chua's circuit and a generalized Lorenz-like circuit are designed and implemented for verifying the effectiveness of this systematic circuit design methodology. Experimental observations are provided for confirmation. Comparing with the traditional circuit design methods, this new design approach has the following typical characteristics: (i) module-based and unified design; (ii) independent adjustment of system parameters; (iii) adjustment of distribution regions for the frequency spectra of chaotic signals; (iv) prominent observability.
