具有输入量化和全状态约束的非严格反馈随机非线性系统的有限时间动态面控制

研究具有输入量化和全状态约束的非严格反馈随机非线性系统的有限时间自适应跟踪控制.首先,利用双曲正切函数进行非线性映射,消除全状态约束的限制,将系统变换为无约束系统;其次,引入滞回量化器克服量化信号中的抖动和量化误差.为实现有限时间控制,提出概率意义下半全局有限时间稳定控制方法,加快系统的收敛速度,并在此基础上采用径向基函数神经网络逼近未知非线性函数;接着,基于动态面控制技术和高斯函数的性质,对变换后的非严格反馈随机系统进行自适应控制设计,所设计的控制器能够保证闭环系统中的所有信号在概率意义下有限时间稳定;最后通过仿真实验表明所设计控制方案的有效性.

Finite-time dynamic surface control for nonstrict-feedback stochastic nonlinear systems with input quantization and full-state constraints

This paper mainly studies the finite-time adaptive tracking control of a class of non-strict feedback stochastic nonlinear systems with input quantization and full-state constraints. First, the hyperbolic tangent function is used for nonlinear mapping, which eliminates the constraints of the full-state constraints and transforms the system into an unconstrained system. Second, a hysteresis quantizer is introduced to avoid the chattering and reduce the quantisation error in the quantized signal. Third, in order to achieve finite time control, a semi-global finite time stability criterion is proposed in the sense of probability, which speeds up the convergence speed of the system. On this basis, radial basis function neural networks are used to approximate the unknown nonlinear functions. Based on the dynamic surface control technology and the properties of the Gaussian function, adaptive control design is performed for the transformed non-strict feedback stochastic system. The designed controller can guarantee that all signals in the closed-loop system are semi-globally finite time stable in probability(SGFTSP). Simulation results show the effectiveness of the proposed control scheme.