Class Of Stochastic Nonholonomic Systems Research Articles

Adaptive neural control for stochastic nonholonomic systems with full-state constraints and unknown covariance noise

In this paper, adaptive neural control is investigated for a class of stochastic nonholonomic systems disturbed by unknown covariance noise under the condition of full-state constraints. Compared with the related literatures, this paper largely generalizes the results in recent works on stochastic nonholonomic systems. The distinctive features of this paper are three folds: the full-state constrains are introduced; the restriction assumed on incremental covariance is removed; the growth assumptions imposed on drift and diffusion terms are somewhat weakened. In the control design procedure, the barrier Lyapunov function (BLF) is applied to conquer the effect of full-state constraints to system performance, unknown covariance noise is compensated with the aid of adaptive control design, the radial basis function neural networks are utilized to approximate unknown nonlinear functions and the backstepping technique is used to construct the desired controller. Then, by adopting the switching strategy to eliminate the phenomenon of uncontrollability, the proposed adaptive neural controller renders the closed-loop system to be semi-globally uniformly ultimately bounded. Moreover, the system states remain in the defined compact sets and the output tracks the reference signal well. Finally, the simulation example shows the effectiveness of the proposed scheme.

Further Results on Adaptive State-feedback Stabilization for a Class of Stochastic Nonholonomic Systems with Time Delays

This paper further studies the adaptive stabilization problem for a class of stochastic nonholonomic systems with time delays, unknown parameterization and control coefficients. By using input-state-scaling technique, backstepping recursive approach, and the parameter separation technique, we design an adaptive state-feedback controller under weaker conditions on the drift and diffusion terms. Then, by adopting the switching strategy to eliminate the phenomenon of uncontrollability, the proposed adaptive state-feedback controller can guarantee the states of the closed-loop system to be global boundedness in probability. Finally, the simulation result shows the effectiveness of the proposed scheme.

Adaptive stabilization of stochastic nonholonomic systems with nonlinear parameterization

This paper investigates the problem of adaptive stabilization by state-feedback for a class of stochastic nonholonomic systems in chained form with nonlinear parameterization. A parameter separation technique is introduced to transform the nonlinear parameterized nonholonomic system into a linear-like parameterized one. Then, using input-state-scaling and backstepping techniques, an adaptive state-feedback controller is obtained. Based on switching strategy to eliminate the phenomenon of uncontrollability, global asymptotic regulation in probability of the closed-loop system states are guaranteed. Finally, a simulation example is provided to illustrate the effectiveness of the proposed method.

Adaptive Exponential Stabilization for a Class of Stochastic Nonholonomic Systems

This paper investigates the adaptive stabilization problem for a class of stochastic nonholonomic systems with strong drifts. By using input-state-scaling technique, backstepping recursive approach, and a parameter separation technique, we design an adaptive state feedback controller. Based on the switching strategy to eliminate the phenomenon of uncontrollability, the proposed controller can guarantee that the states of closed-loop system are global bounded in probability.

Further Result on Finite-Time Stabilization of Stochastic Nonholonomic Systems

This paper further investigates the problem of finite-time state feedback stabilization for a class of stochastic nonholonomic systems in chained form. Compared with the existing literature, the stochastic nonholonomic systems under investigation have more uncertainties, such as thex0-subsystem contains stochastic disturbance. This renders the existing finite-time control methods highly difficult to the control problem of the systems or even inapplicable. In this paper, by extending adding a power integrator design method to a stochastic system and by skillfully constructingC2Lyapunov function, a novel switching control strategy is proposed, which renders that the states of closed-loop system are almost surely regulated to zero in a finite time. A simulation example is provided to demonstrate the effectiveness of the theoretical results.

State-feedback stabilisation for stochastic non-holonomic systems with time-varying delays

This study investigates the problem of state-feedback stabilisation for a class of stochastic non-holonomic systems in chained form with time-varying delays. By using input-state-scaling technique and backstepping recursive approach, and choosing an appropriate Lyapunov–Krasoviskii functional, a state-feedback controller is constructed. Based on switching strategy to eliminate the phenomenon of uncontrollability, the proposed controller could ensure that the closed-loop system is globally asymptotically regulated at origin in probability. A simulation example is provided to illustrate the effectiveness of the proposed method.

Finite‐Time Stabilization of Stochastic Nonholonomic Systems and Its Application to Mobile Robot

This paper investigates the problem of finite‐time stabilization for a class of stochastic nonholonomic systems in chained form. By using stochastic finite‐time stability theorem and the method of adding a power integrator, a recursive controller design procedure in the stochastic setting is developed. Based on switching strategy to overcome the uncontrollability problem associated with x0(0) = 0, global stochastic finite‐time regulation of the closed‐loop system states is achieved. The proposed scheme can be applied to the finite‐time control of nonholonomic mobile robot subject to stochastic disturbances. The simulation results demonstrate the validity of the presented algorithm.