时变终端滑模自适应有限时间控制算法

为实现非线性系统输出对期望轨迹的有限时间内精确跟踪，提出一种有限时间鲁棒控制算法。通过设计一种无到达过程的时变终端滑模面，在保证有限时间收敛的基础上，消除了传统滑模控制中固有的稳态误差，实现系统输出对期望轨迹的精确跟踪。设计了自适应更新律补偿由参数摄动导致的系统扰动，增强系统对内部未知参数摄动的鲁棒性。对比仿真结果表明：时变终端滑模控制比线性滑模控制的轨迹跟踪时间快41.5%；线性滑模控制器下的轨迹跟踪稳态误差为0.005，时变滑模控制器使轨迹跟踪的稳态误差降为0，实现精确跟踪。     

基于时变终端滑模的任意迭代初态抑制控制EI北大核心CSCD

为解决迭代学习过程中的任意迭代初值和迭代收敛理论证明难的问题,本文构造了一种轨迹跟踪误差初值恒位于滑模面内的时变终端滑模面,将轨迹跟踪误差初值不为零的轨迹跟踪控制问题转换为滑模面初值恒为零的滑模面跟踪控制问题,建立了任意迭代初值与相同迭代初值的迭代学习控制理论连接桥梁.本文提出一种基于时变滑模面的比例–积分–微分(PID)型闭环迭代学习控制策略,基于压缩映射原理证明了迭代学习的收敛性,给出了迭代收敛条件.时变终端滑模面经有限次迭代学习收敛到零,达到轨迹跟踪误差最终稳定在时变滑模面内的目的;Lyapunov稳定理论证明了位于滑模面内的轨迹跟踪误差在有限时间内收敛到原点,达到轨迹局部精确跟踪目的.随机初态下的工业机器人轨迹跟踪控制数值仿真验证了本文方法的有效性和系统对外部强干扰的鲁棒性.     

一种改进的基于概率约束DMC控制算法

动态矩阵控制（DMC）是模型预测控制（model predictive control）的一种典型算法，通过模型预测、在线滚动优化和反馈校正来对被控系统实施优化控制，能有效处理系统输入输出约束和抑制随机干扰。为了使具有概率约束的被控对象具有更好的动态响应速度和鲁棒性，在优化性能指标函数中引入跟踪误差的微分项，再通过高斯近似把概率约束指标转换为确定约束，建立有约束DMC预测控制算法。理论分析和计算机仿真结果表明了该算法使系统具有更好的控制品质。     

任意初态下的工业机器人迭代学习控制

对迭代初值为任意值的工业机器人轨迹跟踪控制系统,提出了一种基于滑模面的非线性迭代学习控制算法,使机器人轨迹能快速、精确跟踪上期望轨迹。基于有限时间收敛原理,构建了关于机器人轨迹跟踪误差的迭代滑模面,在滑模面内,机器人轨迹跟踪误差在预定时间内收敛到零。设计了基于滑模面的迭代学习控制算法,理论证明了随着迭代次数的增加,处于任意初态的轨迹将一致收敛到滑模面内,解决了迭代学习中的任意初值问题。数值仿真验证了该算法的有效性和抗干扰能力。     

桥吊时变滑模控制过程的优化建模仿真研究

利用传统方法对桥吊时变滑模控制策略中会产生收敛速度慢,控制作用不连续,误差大等诸多缺憾.针对这一问题,提出基于螺旋算法的桥吊时变滑模控制模型,在二阶超螺旋算法的基础上引入了新的高阶滑模控制律,跟踪了误差的高阶导数,建立滑动量,保证了控制的精度.仿真结果证明利用改进后的模型对桥吊时变滑模控制,具有控制过程收敛速度快、控制作用平滑连续等优势.     

基于2D理论的点对点综合预测迭代学习控制

基于二维系统综合预测迭代学习控制(2D-IPILC)方法,结合轨迹更新策略研究点对点跟踪问题的控制算法.该算法既能够充分利用点对点问题在非跟踪点的自由度,也可以通过引入模型预测控制来提高时间轴的抗干扰能力.由于轨迹更新中引入时变参数,该2D模型为时变2D模型,因此分析状态转移矩阵特性和系统全响应,进而采用2D理论分析算法的收敛性和收敛条件,并分析参数对控制效果的影响.相比固定轨迹算法,该算法的收敛速度更快,稳定性比直接型优化算法更好.最后通过仿真实例验证了所提出算法的效果.     

变参考轨迹下的鲁棒迭代学习模型预测控制

迭代学习模型预测控制是针对间歇过程的先进控制方法.它能通过迭代高精度跟踪给定参考轨迹,并保证时域上的闭环稳定性.然而,现有的迭代学习模型预测控制算法大多基于线性/线性化系统,且没有考虑参考轨迹变化的情况.本文基于线性参变系统提出一种能有效跟踪变参考轨迹的鲁棒迭代学习模型预测控制算法.首先,采用线性参变模型准确涵盖原始非线性系统的动态特性.然后,将鲁棒H_∞控制与传统迭代学习模型预测控制相结合,抑制变参考轨迹带来的跟踪误差波动,通过优化线性矩阵不等式约束下的目标函数求得控制输入.深入分析了鲁棒迭代学习模型预测控制的鲁棒稳定性和迭代收敛性.最后,通过对数值例子和连续搅拌反应釜系统的仿真验证了所提出算法的有效性.     

基于串联结构的分布式模型预测控制

分布式模型预测控制(Distributed model predictive control, DMPC)是一类用于多输入多输出的大规模系统的控制方式.每个智能体通过相互协作完成整个系统的控制. 已有的分布式预测控制算法可以划分为迭代式算法和非迭代算法:迭代算法在迭代到收敛情况下,具有集中式预测控制(Centralized model predictive control, CMPC)算法的性能,但迭 代次数过多,子系统间通信量大;非迭代算法不需要迭代,但性能有一定损失.本文提出了一种基于串联结构的非迭代分布式预测控 制算法.本文算法在串联结构系统中可以有效减少计算量,并结合氧化铝碳分解(Alumina continuous carbonation decomposition process, ACCDP)这一串联过程,通过仿真验证了算 法的有效性;同时分析了算法运用在串联结构下的性能并证明了其稳定性.     

一种带参考批次的线性时变系统迭代学习控制算法

针对线性时变系统的轨迹跟踪控制问题,提出一种带参考批次的迭代学习控制算法,并给出了算法的收敛性分析.该迭代学习控制算法不需要事先了解线性时变对象的太多知识,而是将当前批次输入轨迹的较小变化所引起的输出轨迹作为参考批次,并以当前批次与参考批次的输入变化与对应的输出变化之比作为学习律,从而实现目标轨迹的跟踪.以一个典型的线性时变系统为例进行仿真分析,验证了所提出算法的有效性.     

一种带参考批次的线性时变系统迭代学习控制算法

!针对线性时变系统的轨迹跟踪控制问题,提出一种带参考批次的迭代学习控制算法,并给出了算法的收敛性分析.该迭代学习控制算法不需要事先了解线性时变对象的太多知识,而是将当前批次输入轨迹的较小变化所引起的输出轨迹作为参考批次,并以当前批次与参考批次的输入变化与对应的输出变化之比作为学习律,从而实现目标轨迹的跟踪.以一个典型的线性时变系统为例进行仿真分析,验证了所提出算法的有效性.

     

Sliding mode predictive control: A survey

This paper reviews the development and application of sliding mode predictive control (SMPC) in a tutorial manner. Two core design paradigms are revealed in the combination of sliding mode control (SMC) and model predictive control (MPC). In the first case, MPC is used in the reaching phase to ensure a sliding mode is attained. In the second case, MPC is used to solve the existence problem and define the required performance in the sliding mode. The two approaches are discussed in detail from the perspectives of both theory and application. Finally, some future challenges and opportunities in the area of SMPC are summarized.     

Sliding mode predictive control of a solar air conditioning plant

In this work, sliding mode predictive control (SMPC) was applied to an air conditioning solar plant. The process has a variable time delay with non-minimum phase behavior, whose inverse response changes with the operating point. Disturbances are produced by changes in solar irradiation and solar collectors’ inlet temperature. The algorithm combines the design technique of sliding mode control (SMC) with model-based predictive control (MPC). The SMPC showed a considerable robustness improvement with respect to MPC under experimental validation on a real plant and also showed an enhanced ability to handle set point changes and disturbance rejection.     

Sliding mode predictive tracking control for uncertain Steer-by-Wire system

The Steer-by-Wire (SbW) system is probably the most significant innovation among drive-by-Wire technologies in the automotive industry. Without the mechanical link, the most challenging issue is to control the wheels to closely follow the driver’s command. To improve the robustness of the model predictive control (MPC) in the presence of modeling uncertainties and disturbances in the steering control processes, a sliding mode predictive tracking control (SMPC) strategy for a SbW system with uncertain dynamics is proposed. The simulation and experimental results demonstrate that the performance of the proposed SMPC tracking controller is superior to both SMC and MPC methods for the steering angle tracking task.     

简化模型预测控制及仿真研究

本文介绍了简化模型预测控制算法ＳＭ－ＰＣ，指出ＳＭＰＣ算法是作为克服内部模型控制（ＩＭＣ）因式分解中求取过程传函逆阵费时等缺点而推出的一种简化算法。将本算法通过对一个多侧线精馏塔进行仿真，结果表明此算法具有良好的控制效果     

Sliding mode prediction based control algorithm for discrete-time non-linear uncertain coupled systems

By introducing predictive control strategy into the design of sliding mode control (SMC), a novel SMC algorithm for a class of discrete-time non-linear uncertain coupled systems is presented in this paper. To enlighten by the recursive sliding mode approach, a special sliding mode prediction model (SMPM) is created at first. Then taking model mismatch into consideration, the error between the output of SMPM and the practical sliding mode value is used to make feedback correction for SMPM. Applying receding horizon optimization, the desired sliding mode control law which is a non-switching type, is obtained subsequently. The reachability of sliding mode is achieved by making predictive value of sliding mode track the expected sliding mode reference value. Due to feedback correction and receding horizon optimization, the influence of uncertainties can be compensated in time, strong robustness to matched or unmatched uncertainties is possessed. Theoretical analysis proves the closed-loop system is robustly stable, without requiring the known boundaries of uncertainties. Simulation results of a numerical example and a rotational inverted pendulum illustrate the validity of the proposed algorithm.     

Stochastic model predictive control for constrained discrete-time Markovian switching systems

In this paper we study constrained stochastic optimal control problems for Markovian switching systems, an extension of Markovian jump linear systems (MJLS), where the subsystems are allowed to be nonlinear. We develop appropriate notions of invariance and stability for such systems and provide terminal conditions for stochastic model predictive control (SMPC) that guarantee mean-square stability and robust constraint fulfillment of the Markovian switching system in closed-loop with the SMPC law under very weak assumptions. In the special but important case of constrained MJLS we present an algorithm for computing explicitly the SMPC control law off-line, that combines dynamic programming with parametric piecewise quadratic optimization.     

High precision tracking of a piezoelectric positioner using a discrete-time sliding mode control

This paper proposes a new comprehensive control strategy to precisely control a piezoelectric positioner by combining discrete-time sliding mode control (DSMC) with the Prandtl-Ishlinskii hysteresis model. In order to obtain precision tracking control, a direct inverse hysteresis compensation method is firstly adopted to compensate for the asymmetric hysteresis nonlinearity. Due to the existence of hysteresis modeling error, the dynamics behavior of the piezoelectric positioner with hysteresis compensation can be treated as a linear second order plant plus an unknown lumped disturbance term. Then a discrete-time sliding mode controller with a disturbance observer is designed to stabilize the position error and improve the position accuracy. The stability of DSMC and the convergence of the disturbance observer are both carried out. It is shown that the tracking performances are robust to the parametric uncertainties and unknown disturbances. Eventually, different trajectory-tracking experiments are performed, and the comparative experimental results are presented to confirm the significantly better performance of the proposed control strategy.

     

带终端滑模约束的非线性模型预测控制方法

将预测控制和滑模控制结合起来,提出一种非线性性模型预测控制方法。给出一种可行的双模控制方案,系统状态位于终端区外时采用提出的预测控制,在终端区内部采用高线设计的滑模控制。对系统终端滑模附加不等式约束,使得系统状态在预测时域的末端位于高线设计的滑动模态区内,从而使预测时域减小。仿真结果表明了算法的有效性。     

机器人全局PID模糊滑模跟踪控制与仿真研究

为解决机器人跟踪控制过程中采用PID控制算法会出现抖动和误差的问题,提出了一种机器人全局PID模糊滑模跟踪控制算法.通过将PID滑模控制和模糊控制相结合,设计了全局PID模糊滑模控制;基于模糊规则,对滑模控制增益进行自适应调整,从而消除建模误差和干扰,削弱了控制时产生的抖振,在线调整控制器参数和估计误差,并通过积分来消除外界干扰,因此提高了控制精度.仿真结果表明,与常规的PID算法相比,该方法在处理控制抖动和消除误差以及干扰方面具有极高的鲁棒性.     

基于即时学习的MIMO系统滑模预测控制方法

针对MIMO非线性系统的控制问题,采用数据驱动的控制策略,将具有本质自适应能力的即时学习算法与具有强鲁棒性的滑模预测控制相结合,设计了一种基于即时学习的滑模预测（LL-SMPC）控制方法．该方法在在线局部建模的基础上,采用滑模预测控制律求取最优控制量,具有较强的自适应和抗干扰能力,并避免TDiophantine方程的求解,有效减少了计算量．通过仿真研究,验证了算法的有效性．