带状态约束的非线性系统单向辅助面滑模控制

针对带有状态输出有界约束的一类不确定仿射非线性系统,提出一种单向辅助面滑模控制器设计方法．该方法通过将状态量的约束条件引入单向辅助面的设计,并利用单向辅助面构成系统状态输出量正不变集的边界,以保证系统状态能够满足状态输出量的约束条件．随后给出了该方法稳定性以及单向辅助面构成正不变集的理论证明,并通过仿真验证了所提出的设计方法的有效性．     

一种改进指数趋近律单向辅助面滑模控制

针对连续非线性系统,结合指数趋近律,采用一种改进指数趋近律的单向辅助面滑模控制方法。首先依据给定的连续非线性系统和状态约束,设计基于指数趋近律的单向辅助面滑模控制器,在此基础上,引入了改进指数趋近律单向辅助面滑模控制器,并给出有限时间收敛的理论证明和收敛时间计算公式。最后通过仿真验证了改进的指数趋近律单向辅助面滑模控制方法具有更快的收敛速度和更有效的抖振抑制能力。     

滑模控制一类非线性分布式时滞系统

针对一类状态不可测的非线性不确定分布式时滞系统, 给出了系统滑动模态鲁棒渐近稳定的充分条件. 设计了一类滑模观测器, 同时采用线性矩阵不等式的处理方法给出了该观测器存在的充分条件. 再应用滑模控制的趋近率方法和基于观测器所得到的估计状态, 综合了一类滑模控制器. 该控制器同时保证了估计状态下的滑模面和估计误差状态下的滑模面的渐近可达性.     

基于观测器的滑模控制非线性中立型时滞系统

针对一类状态不可测的非线性不确定中立型时滞系统,基于滑模控制理论,采用线性矩阵不等式的处理方法,提出了滑动模态鲁棒渐近稳定时滞相关的充分条件,设计了一类滑模观测器,同时给出了该观测器存在的充分条件;然后应用滑模控制的趋近率方法和基于观测器所得到的系统估计状态,综合了一类滑模控制器,该控制器同时保证了估计状态下滑模面和估计误差状态下滑模面的渐近可达性;最后通过数值实例证明了该控制方案的可行性.     

非线性增益递归滑模动态面自适应NN控制

针对一类严反馈非线性不确定系统的跟踪控制问题,提出一种非线性增益递归滑模动态面 (Dynamic surface control, DSC)自适应控制方法. 通过设计一个新的非线性增益函数,并构造递归滑模动态面的控制策略和新的Lyapunov函数,同时利用神经网络在线逼近系统不确定项, 该方法有效解决了具有输入饱和约束条件下系统控制精度与动态品质间的矛盾,增强了控制器对其自身参数摄动的非脆弱性. 理论证明了闭环系统所有状态是半全局一致最终有界的,且跟踪误差可收敛至任意小.     

基于积分滑动流形的高阶滑模控制器设计

滑模变结构控制方法因其易实现,鲁棒性强等优点广泛应用于实际控制系统中,讨论了具有积分滑动流形的高阶滑模控制器的设计方法.通过设计含积分滑动流形的高阶滑模面,使系统状态在一阶乃至高阶滑模面上均能达到滑动模态.同时利用高阶滑模面为状态变量设计新的状态空间系统,将原先促使系统状态接近并停留在滑模面上的控制目标,拓展为使高阶滑模状态变量趋近于零的控制目标,并结合最优控制方法来设计等效控制量,利用积分流形设计切换控制的切换面,通过严格证明来证实控制器设计的稳定性.在仿真验证部分采用了一阶倒立摆模型,通过比较常规趋近律滑模控制方法和本文方法的仿真结果,可以得出本文方法在减小系统控制量抖振方面的重要作用和优异效果.     

分数阶混沌系统的主动滑模同步

结合主动控制和滑模控制原理,提出了一个同步分数阶混沌系统的主动滑模控制方法.该方法首先用分数阶积分对所有维状态分量设计一个滑模面,分数阶混沌系统在该滑模面上稳定.然后采用极点配置的方法获得主动滑模控制器中的增益矩阵.应用Lyapunov稳定性理论、分数阶系统稳定理论对所提的控制器的存在性和稳定性分别进行了分析.对分数阶Lorenz系统进行数值仿真,仿真结果验证了该方法的有效性.     

带记忆比例–积分–时滞输出滑模控制器设计

传统的比例–积分–微分(PID)滑模控制具有高频测量噪声鲁棒性差、参数整定方法复杂等缺点. 针对上述问题, 本文设计了一种基于带记忆输出反馈的比例–积分–滞后(PIR)滑模控制器, 并提出了基于频域分析的控制器参数自整定方法. 在控制器选型上, 将“带记忆”反馈机制引入到滑模面设计中, 提高滑模面的平滑滤波和高频噪声抑制能力. 首先, 基于PIR滑模面设计了等效控制律, 实现输出反馈下的闭环系统的滑模态稳定. 其次, 分析了基于输出反馈的PIR滑模面的有限时间可达性, 确保系统在存在匹配扰动时仍保持良好的鲁棒性. 最后, 在控制器参数整定方面, 通过迭代消除法消去时滞项在闭环特征方程中的影响, 同时基于衰减速率对系统进行极点配置, 实现PIR参数的在线自整定. 仿真结果表明, 该整定方法能够保证闭环二阶系统具有良好的鲁棒性和抗干扰性能.     

一类不确定线性切换系统的鲁棒H∞滑模控制

对一类含有非匹配不确定性且有外部干扰的线性切换系统,研究了H∞滑模变结构控制问题.利用LMI技术和单Lyapunov函数方法,设计单H∞滑模面,切换律以及子系统的变结构控制器,确保了切换系统的闭环系统为鲁棒稳定,且具有H∞扰动衰减度γ.系统状态到达滑模面后,该滑模面成为切换系统的全局鲁棒滑模面,可提高系统的暂态性能和鲁棒性.仿真例子说明所提出设计方法的有效性.     

基于分层滑模控制的VTOL飞行器轨迹跟踪

针对VTOL飞行器的轨迹跟踪和稳定性问题,在考虑输入耦合前提下,提出了一种分层滑模控制方案.首先,将整个系统分成两个子系统,分别设计两个子系统的滑模面;然后利用其中一个子系统滑模量来构造中间变量,进而构造出整个系统总的滑模面;再利用等效控制法求取系统在该滑模面上的等效控制量,采用李雅普诺夫方法设计了系统的切换控制量,从而获得系统总的控制量.该控制器能够保证各个滑模面的稳定性和误差闭环系统的全局渐近稳定性.最后的仿真结果表明了该方法的有效性和可行性.     

MPC of constrained discrete-time linear periodic systems — A framework for asynchronous control: Strong feasibility, stability and optimality via periodic invariance

State-feedback model predictive control (MPC) of discrete-time linear periodic systems with time-dependent state and input dimensions is considered. The states and inputs are subject to periodically time-dependent, hard, convex, polyhedral constraints. First, periodic controlled and positively invariant sets are characterized, and a method to determine the maximum periodic controlled and positively invariant sets is derived. The proposed periodic controlled invariant sets are then employed in the design of least-restrictive strongly feasible reference-tracking MPC problems. The proposed periodic positively invariant sets are employed in combination with well-known results on optimal unconstrained periodic linear-quadratic regulation (LQR) to yield constrained periodic LQR control laws that are stabilizing and optimal. One motivation for systems with time-dependent dimensions is efficient control law synthesis for discrete-time systems with asynchronous inputs, for which a novel modeling framework resulting in low dimensional models is proposed. The presented methods are applied to a multirate nano-positioning system.     

基于容许集的多胞不确定系统三模RMPC

针对输入和状态受约束的多胞不确定线性系统,提出了基于容许集的扩大吸引域三模鲁棒模型预测控制方法.在多面体不变集离线模型预测控制算法的基础上引入容许集,以多面体不变集序列的并集作为模态1,基于N步容许集的控制容许集作为模态2,并利用离线设计和在线优化的控制策略,设计了三模变终端约束鲁棒模型预测控制算法,以实现系统渐近稳定.该算法不仅降低了在线运算量,而且扩大了吸引域.最后的仿真结果验证了所提出算法的有效性.     

Robust output feedback model predictive control of constrained linear systems

This paper provides a solution to the problem of robust output feedback model predictive control of constrained, linear, discrete-time systems in the presence of bounded state and output disturbances. The proposed output feedback controller consists of a simple, stable Luenberger state estimator and a recently developed, robustly stabilizing, tube-based, model predictive controller. The state estimation error is bounded by an invariant set. The tube-based controller ensures that all possible realizations of the state trajectory lie in a simple uncertainty tube the ‘center’ of which is the solution of a nominal (disturbance-free) system and the ‘cross-section’ of which is also invariant. Satisfaction of the state and input constraints for the original system is guaranteed by employing tighter constraint sets for the nominal system. The complexity of the resultant controller is similar to that required for nominal model predictive control.     

Sliding mode-based adaptive tube model predictive control for robotic manipulators with model uncertainty and state constraints

In this paper, the optimal tracking control for robotic manipulatorswith state constraints and uncertain dynamics is investigated, and a sliding mode-based adaptive tube model predictive control method is proposed. First, utilizing the high-order fully actuated system approach, the nominal model of the robotic manipulator is constructed as the predictive model. Based on the nominal model, a nominal model predictive controller with the sliding mode is designed, which relaxes the terminal constraints, and realizes the accurate and stable tracking of the desired trajectory by the nominal system. Then, an auxiliary controller based on the node-adaptive neural networks is constructed to dynamically compensate nonlinear uncertain dynamics of the robotic manipulator. Furthermore, the estimation deviation between the nominal and actual states is limited to the tube invariant sets. At the same time, the recursive feasibility of nominal model predictive control is verified, and the ultimately uniformly boundedness of all variables is proved according to the Lyapunov theorem. Finally, experiments show that the robotic manipulator can achieve fast and efficient trajectory tracking under the action of the proposed method.     

离散非线性时变凸多面体系统族的鲁棒正不变集

动态系统的状态约束和控制约束等问题可归结为状态空间中某些集合的正不变性.利用 混合单调分解方法研究离散非线性、时变凸多面体系统族的线性状态约束集合的鲁棒正不变性． 对由矩阵凸多面体和加性区间扰动描述的线性时变离散系统族,得到了鲁棒正不变集的充分必要 条件;对非线性系统族则得到有关充分条件.这些条件均由系统族的顶点表述,易于检验,同时给 出示例.     

Necessary and sufficient conditions for constraint satisfaction in switched systems using switch‐robust control invariant sets

This paper studies the control of constrained systems whose dynamics and constraints switch between a finite set of modes over time according to an exogenous input signal. We define a new type of control invariant sets for switched constrained systems, called switch–robust control invariant (switch‐RCI) sets, that are robust to unknown mode switching and exploit available information on minimum dwell‐time and admissible mode transitions. These switch‐RCI sets are used to derive novel necessary and sufficient conditions for the existence of a control‐law that guarantees constraint satisfaction in the presence of unknown mode switching with known minimum dwell‐time. The switch‐RCI sets are also used to design a recursively feasible model predictive controller (MPC) that enforces closed‐loop constraint satisfaction for switched constrained systems. We show that our controller is nonconservative in the sense that it enforces constraints on the largest possible domain, ie, constraints can be recursively satisfied if and only if our controller is feasible. The MPC and switch‐RCI sets are demonstrated on a vehicle lane‐changing case study.     

一种基于衰减集结的鲁棒预测控制器

高效鲁棒预测控制 (ERPC) 是一种在线计算量较小, 且控制性能较好的鲁棒预测控制算法. 但采用单一椭圆不变集的设计方法存在保守性. 本文采用衰减集结策略, 通过离线设计在系统状态空间中投影彼此正交的两个椭圆不变集, 在线进行凸组合的方法设计 ERPC 控制器, 使系统初始可行域进一步扩大, 并在一定程度上改善了控制性能.     

Robust variable horizon model predictive control for vehicle maneuvering

This paper provides a new method of robust maneuver control with guaranteed finite‐time arrival and satisfaction of constraints, despite the action of an unknown but bounded disturbance. The new method extends the constraint tightening approach to robust model predictive control of constrained linear systems by combining it with a variable horizon. This relaxes the requirement for the target to be an invariant set, which is assumed by many stabilizing MPC formulations but can be restrictive in vehicle maneuvering applications. The target sets for vehicle maneuvers are typically determined by the mission requirements and are not generally invariant sets. The new controller guarantees finite‐time arrival within an arbitrary target set, i.e. not necessarily invariant, and is therefore applicable when the target is predetermined by other factors. Several simulation examples are presented including spacecraft rendezvous control with sensor visibility constraints and UAV guidance through obstacle fields. Copyright © 2006 John Wiley & Sons, Ltd.     

基于多面体不变集的变终端约束集RMPC

针对一类状态和输入受约束的多胞不确定线性时变系统,提出了一种基于多面体不变集的变终端约束集鲁棒模型预测控制算法.首先采用基于状态反馈增益的多面体不变集计算方法,给出了一种新的控制不变集序列构造方法,然后以控制不变集序列的并集作为终端约束集,结合在线优化和增益切换,实施变终端约束集双模鲁棒预测控制.该算法不仅有效地扩大了...