非完整系统的离散时间反馈镇定

讨论非完整系统的镇定问题。针对一类具有两个控制、三个状态的非完整系统，提出一种利用分段常值控制序列反馈镇定此类系统的策略，并以两杆平面空间机器人系统为例，验证了控制策略的有效性。     

一类高阶随机非线性系统的状态反馈镇定

针对一类既不可反馈线性化也不仿射于控制输入的高阶随机非线性系统,研究其状态反馈镇定问题.利用齐次占优和反推技术,所设计的状态反馈控制器使得整个闭环系统在[0,+∞)上几乎处处有唯一解,并使得闭环系统的平衡点是依概率全局渐近稳定的.主要贡献是完全取消了高阶系统的阶次限制,得到了新的结果.最后通过数值仿真验证了所提出控制方案的有效性.     

非完整链式系统的时变光滑指数镇定

针对非完整链式系统,导出了一种时变光滑反馈控制律,该控制律可以保证系统状态全局指数收敛到原点,克服了以往控制律非光滑或虽光滑但却只能渐近镇定系统的缺陷.所得控制律应用于移动机器人系统的镇定.     

一类上三角随机非线性系统的状态反馈镇定

针对一类具有上三角结构的随机非线性系统,研究其状态反馈镇定问题.通过引入有效的坐标变换,将所要研究的系统转化为一个具有可调增益的等价系统;然后利用低增益齐次占优技术,通过巧妙地选取低增益参数,使得整个闭环系统达到依概率全局渐近稳定.主要贡献是,将低增益齐次占优技术推广到了随机系统,并解决了一类上三角随机系统的镇定问题.数值仿真验证了所提出控制方案的有效性.     

一类非完整约束系统的镇定与能控性

考虑非完整约束系统的镇定与能控性问题. 文中首先证明了虽然一般地说这类系统不能由光滑反馈镇定, 但它的存在性依赖于初值. 当初值在一个零测集外时, 这种反馈镇定确实存在. 然后我们证明了这类系统在分段光滑控制下全局可控. 由于证明是构造性的, 它给出了相应的控制.     

一类线性切换系统的鲁棒状态反馈镇定

考虑一类标称系统存在共同Lyapunov函数的切换系统的鲁棒镇定问题.在不确定性不满足匹配条件下,设计出鲁棒状态反馈控制器,并在给定的切换策略下,确保闭环系统在其平衡点处渐近稳定.仿真结果表明所设计控制器是有效的.     

不确定非完整链式系统的鲁棒镇定

不确定非完整链式系统的镇定问题在过去十年中得到了许多有趣的结果, 涌现出了几类模型和相应的控制方法. 目前已经可以对这方面的发展做出总结. 本文给出了非完整链式系统的模型、镇定控制方法和一些结论, 特别是基于视觉传感器得到了一些新的有趣的不确定链式模型. 同时, 利用新的二步法、视觉反馈、状态缩放变换和切换等技术设计了新的不确定链式模型的鲁棒镇定控制器. 希望这项工作可以做到抛砖引玉, 也可以为这个领域的专家做一个较好的参考.     

具有输入时滞的随机前馈非线性系统的状态反馈镇定

针对一类具有输入时滞的随机前馈非线性系统, 首次研究了它的状态反馈镇定问题. 首先引入一个变量变换, 将其与齐次占优方法巧妙结合, 通过构造合适的 Lyapunov-Krasovskii 泛函, 设计了一个状态反馈控制器, 使得闭环系统的平衡点依概率全局渐近稳定.     

状态受限的非完整链式系统的实际镇定

考虑了状态受限的非完整单链式系统的实际镇定问题. 利用链式系统的特殊结构设计了两步控制算法. 该方法能够使闭环系统的状态最终收敛到原点的任意小邻域中, 且若系统初值小于任意正数M, 那么除了一个收敛到零的状态变量外, 其余状态变量在整个实际镇定过程中均小于正数 (1+ε)M, 其中ε可事先任意小. 最后对平面移动机器人进行了仿真, 验证了所给方法的有效性.     

一类高阶次随机非线性系统的状态反馈镇定

针对一类高阶次随机非线性系统,研究其状态反馈镇定问题.基于最近发展起来的增加幂次积分器技术,通过适当地选取Lyapunov函数和设计参数,给出了一个光滑的状态反馈反推控制器的设计过程,所设计的控制器保证了闭环系统在原点处的平衡点是概率意义下全局渐近稳定的.     

Output feedback stabilization for stochastic nonholonomic systems with nonlinear drifts and Markovian switching

This paper investigates the output feedback stabilization of stochastic nonholonomic systems which have both nonlinear drifts and Markovian switching. The state‐scaling and backstepping techniques are exploited in the design of control laws. The output feedback stabilizing control laws and switching control strategy are proposed so that the closed‐loop system can be stabilized in probability in the large. In the end, an example of nonholonomic mobile robots is provided to illustrate the effectiveness of control laws.     

Adaptive state-feedback stabilization of high-order stochastic systems with nonlinear parameterization

This paper investigates the adaptive state-feedback stabilization of high-order stochastic systems with nonlinear parameterization. By using the parameter separation lemma in [Lin, W., & Qian, C. (2002a). Adaptive control of nonlinearly parameterized systems: A nonsmooth feedback framework. IEEE Transactions on Automatic Control, 47, 757-774.] and some flexible algebraic techniques, and choosing an appropriate Lyapunov function, a smooth adaptive state-feedback controller is designed, which guarantees that the closed-loop system has an almost surely unique solution for any initial state, the equilibrium of interest is globally stable in probability, and the state can be regulated to the origin almost surely.     

Adaptive output feedback control for nonholonomic systems with uncertain chained form

The output feedback adaptive control problem is investigated for nonholonomic systems with strongly nonlinear uncertainties and unknown virtual control directions. A nonlinear output feedback switching controller based on the output measurement of the first subsystem is employed in order to make the state scaling effective and ensure the convergence of the system states. The novel observer/estimator is introduced for state and unknown parameter estimates. The integrator backstepping technique by the use of a constructive recursive is applied to the design of the adaptive controller and to overcome the unknown virtual control directions. The simulation result validates the effectiveness of the proposed scheme.     

Adaptive state-feedback stabilization for a large class of high-order stochastic nonlinear systems

Under the more general conditions on the power order and the nonlinear functions, this paper investigates the problem of adaptive state-feedback stabilization for a class of high-order stochastic nonlinear systems with time-varying control coefficients. Based on the backstepping design method and homogeneous domination technique, the closed-loop system can be proved to be globally stable in probability and the states can be regulated to the origin almost surely. The efficiency of the state-feedback controller is demonstrated by a simulation example.     

Adaptive robust control of nonholonomic systems with stochastic disturbances

Based on the Brockett’s necessary condition for feedback asymptotic stabilization[1], nonholonomic systems fail to be stabilized at the origin by any static continuous state feedback though they are open loop controllable. There are two novel approaches …     

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.     

Stabilization of uncertain chained nonholonomic systems using adaptive output feedback

In this paper, adaptive output feedback control is presented to solve the stabilization problem of nonholonomic systems in chained form with strong nonlinear drifts and uncertain parameters using output signals only. The objective is to design adaptive nonlinear output feedback laws which can steer the closed‐loop systems to globally converge to the origin, while the estimated parameters remain bounded. The proposed systematic strategy combines input‐state scaling with backstepping technique. Motivated from a special case, adaptive output feedback controllers are proposed for a class of uncertain chained systems. The simulation results demonstrate the effectiveness of the proposed controllers. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Global inverse optimal stabilization of stochastic nonholonomic systems

Optimality has not been addressed in existing works on control of (stochastic) nonholonomic systems. This paper presents a design of optimal controllers with respect to a meaningful cost function to globally asymptotically stabilize (in probability) nonholonomic systems affine in stochastic disturbances. The design is based on the Lyapunov direct method, the backstepping technique, and the inverse optimal control design. A class of Lyapunov functions, which are not required to be as nonlinearly strong as quadratic or quartic, is proposed for the control design. Thus, these Lyapunov functions can be applied to design of controllers for underactuated (stochastic) mechanical systems, which are usually required Lyapunov functions of a nonlinearly weak form. The proposed control design is illustrated on a kinematic cart, of which wheel velocities are perturbed by stochastic noise.     

Output feedback stabilization for nonholonomic systems with unknown unmeasured states‐dependent growth

This paper is concerned with the global output feedback stabilization for a class of nonholonomic systems with unknown parameter, polynomial‐of‐output, and unmeasurable states dependent growth. A dynamic high‐gain observer is first designed to reconstruct the unmeasurable system states and, in addition, to compensate the serious parameter unknowns in nonlinear drifts. Then, we design a compact adaptive controller without invoking the backstepping technique, which reduces the complexity of controller. Additionally, a switching control strategy is employed to overcome the smooth feedback obstacle associated with nonholonomic systems. It is shown that the proposed control laws guarantee that all closed‐loop system states are globally bounded and ultimately converge to zero. The simulation results demonstrate the effectiveness of the proposed control strategy.     

Adaptive fuzzy switched control design for uncertain nonholonomic systems with input nonsmooth constraint

In this paper, a fuzzy adaptive switched control approach is proposed for a class of uncertain nonholonomic chained systems with input nonsmooth constraint. In the control design, an auxiliary dynamic system is designed to address the input nonsmooth constraint, and an adaptive switched control strategy is constructed to overcome the uncontrollability problem associated with x₀(t₀) = 0. By using fuzzy logic systems to tackle unknown nonlinear functions, a fuzzy adaptive control approach is explored based on the adaptive backstepping technique. By constructing the combination approximation technique and using Young's inequality scaling technique, the number of the online learning parameters is reduced to n and the ‘explosion of complexity’ problem is avoid. It is proved that the proposed method can guarantee that all variables of the closed-loop system converge to a small neighbourhood of zero. Two simulation examples are provided to illustrate the effectiveness of the proposed control approach.