Stochastic High-order Nonlinear Systems Research Articles

Output Tracking of High-Order Stochastic Nonlinear Systems with Application to Benchmark Mechanical System

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This note considers output tracking of high-order stochastic nonlinear systems without imposing any restriction on the high-order and the drift and diffusion terms. By using the backstepping design technique, a smooth state-feedback controller is given to guarantee that the solution process is bounded in probability and the error signal between the output and the reference signal can be regulated into a small neighborhood of the origin in probability. A practical example of stochastic benchmark mechanical system and simulation are provided to demonstrate the effectiveness of the control scheme. </para>

Output-feedback stabilization for stochastic nonlinear systems whose linearizations are not stabilizable

This brief paper investigates the problem of output-feedback stabilization for a class of high-order stochastic nonlinear systems which are neither necessarily feedback linearizable nor affine in the control input. Based on the ideas of the homogeneous systems theory and the adding a power integrator technique, an output-feedback controller is constructed to ensure that the equilibrium at the origin of the closed-loop system is globally asymptotically stable (GAS) in probability. The efficiency of the output-feedback controller is demonstrated by a simulation example.

State-feedback stabilization for stochastic high-order nonlinear systems with a ratio of odd integers power

This paper investigates the problem of globally asymptotically stable in probability by state-feedback for a class of stochastic high-order nonlinear systems with a ratio of odd integers power. By extending the adding a power integrator technique and choosing an appropriate Lyapunov function, a linear smooth state-feedback controller is explicitly constructed to render the system globally asymptotically stable in probability. Furthermore, we address the problem of state-feedback inverse optimal stabilization in probability. A simulation example is provided to show the effectiveness of the proposed approach.

Output-feedback control of a class of high-order stochastic nonlinear systems

This article investigates the problem of output-feedback stabilisation for a class of high-order stochastic non-linear systems in which the diffusion terms depend on unmeasurable states besides the output. By introducing a new rescaling transformation, adopting an effective observer and choosing the appropriate Lyapunov function, an output-feedback controller is constructed to ensure that the equilibrium at the origin of the closed-loop system is globally asymptotically stable in probability, the output can be regulated to the origin almost surely, and the problem of inverse optimal stabilisation in probability is solved. The efficiency of the output-feedback controller is demonstrated by several simulation examples.

Adaptive State-feedback Stabilization for More General High-order Stochastic Nonlinear Systems

Adaptive State-feedback Stabilization for More General High-order Stochastic Nonlinear Systems

Inverse optimal stabilization for stochastic nonlinear systems whose linearizations are not stabilizable

After considering the stabilization of a class of high-order stochastic nonlinear systems which are neither necessarily feedback linearizable nor affine in the control input, in this brief paper, we further address the problem of state-feedback inverse optimal stabilization in probability, i.e., our redesigned stabilizing backstepping controller is also optimal with respect to meaningful cost functionals.

Adaptive State-feedback Stabilization for More General High-order Stochastic Nonlinear Systems

An adaptive state-feedback stabilization is investigated for a class of high-order stochastic nonlinear systems in which the upper bound of the function fi(·) depends on the state x i+1 besides the states x 1, …, x i and z. A smooth adaptive state-feedback controller is designed, which guarantees that the closed-loop system has an almost surely unique solution, and the equilibrium is globally stable in probability. A numerical simulation example is given to show the systematic design and the effectiveness of controller.

State feedback stabilization for high-order stochastic nonlinear systems with zero dynamics

In this paper, for a class of high-order stochastic nonlinear systems with zero dynamics which are neither necessarily feedback linearizable nor affine in the control input, the problem of state feedback stabilization is investigated for the first time. Under some weaker assumptions, a smooth state feedback controller is designed, which ensures that the closed-loop system has an almost surely unique solution on [0,∞), the equilibrium at the origin of the closed-loop system is globally asymptotically stable in probability, and all the states can be regulated to the origin almost surely. A simulation example demonstrates the control scheme.

Adaptive state-feedback stabilization for high-order stochastic non-linear systems with uncertain control coefficients

This paper investigates the adaptive state-feedback stabilization problem for a class of high-order stochastic non-linear systems with unknown lower and supper bounds for uncertain control coefficients. Under some weaker and reasonable assumptions, a smooth adaptive state-feedback controller is designed, which guarantees that the closed-loop system has an almost surely unique solution on [0,∞, the equilibrium of interest is globally stable in probability and the states can be regulated to the origin almost surely. A simulation example is given to show the systematic design and effectiveness of the controller.