基于非线性干扰观测器的翼伞鲁棒反步跟踪控制

针对干扰条件下的无人翼伞飞行器路径跟踪控制问题,提出一种基于非线性干扰观测器的反馈增益鲁棒反步控制方法.采用二阶跟踪-微分器设计干扰观测器对系统复合干扰进行估计和补偿,设计了反馈增益反步跟踪控制律,通过合理设计增益参数,消除了部分复杂非线性项,避免了虚拟量高阶导数问题,简化了控制器形式.根据Lyapunov理论设计鲁棒反馈补偿项,在保证稳定性的同时提高了系统的鲁棒性.仿真实验结果验证了所提出方法的有效性.     

基于扰动补偿的无微分模型参考自适应控制系统设计

针对一类含有参数不确定性和未知非线性扰动的系统,本文提出一种基于扰动补偿的无微分模型参考自适应控制方法,实现系统输出对参考模型输出信号的高精度跟踪.首先,利用被控对象模型信息设计扰动估计器,对系统非线性扰动进行在线估计;其次,基于非线性扰动估计值设计参考模型和无微分参数更新律,构建无微分模型参考自适应控制器,建立基于扰动补偿和状态反馈的自适应控制律,以消除参数不确定性和非线性扰动对系统输出的影响,保证系统输出对参考模型输出的准确跟踪;然后,给出闭环系统误差信号收敛条件和控制器参数整定方法;最后,通过数值仿真验证所提方法的有效性和优越性.     

含有广义饱和函数的全局渐近稳定非线性PID控制

为解决传统饱和函数条件比较苛刻的问题,本文提出了一种饱和函数,并将其应用于线性PD（比例－微分）+非线性PI（比例－积分）控制律．应用李亚普诺夫稳定性定理和拉萨尔不变原理推导了非线性PID控制律全局渐近稳定条件．为提高非线性PID控制精度,以位置跟踪误差绝对值时间积分和关节力矩输出误差绝对值时间积分为目标函数,以全局渐近稳定性条件以及关节额定驱动力矩为约束条件,应用多目标遗传算法NSGA-II（non-dominated sorted genetic algorithm-II）进行非线性PID控制律参数整定．选择优化后位置跟踪误差绝对值时间积分最小的饱和函数,并研究含有该饱和函数的非线性PID控制律对模型不确定性、输入干扰力矩和噪声的鲁棒性．与传统PID控制律和含有传统饱和函数的非线性PID控制律相比,本文方法的位置跟踪精度分别提高了近2个数量级和1个数量级．提出的饱和函数在靠近平衡点处具有较强反作用,使误差快速收敛于平衡点,有助于提高非线性PID控制律的位置跟踪精度以及鲁棒性．     

线性时不变系统PID–型迭代学习控制律的单调收敛形态

传统的迭代学习控制机理中, 积分补偿是典型的策略之一, 但其跟踪效用并不明确. 本文针对连续线性时 不变系统, 对传统的PD–型迭代学习控制律嵌入积分补偿, 利用分部积分法和推广的卷积Young不等式, 在Lebesgue- p范数意义下, 理论分析一阶和二阶PID–型迭代学习控制律的收敛性态. 结果表明, 当比例、积分和导数学习增益满 足适当条件时, 一阶PID–型迭代学习控制律是单调收敛的, 二阶PID–型迭代学习控制律是双迭代单调收敛的. 数值 仿真验证了积分补偿可有效地提高系统的跟踪性能.     

高阶不确定非线性系统的线性自抗扰控制

针对一类具有内部动态和外部扰动未知的SISO高阶非线性系统,提出一种通用的线性自抗扰控制方案.该方案基于单参数调节的高增益观测器思想,分别设计线性跟踪微分器、线性扩张状态观测器和线性状态误差反馈控制律.利用Lagrange中值定理和Cauchy-Schwarz不等式将系统总扰动的微分值转化为关于系统估计和跟踪误差的函数,可以解决因系统控制增益未知所导致的控制量微分值难以预先确定的问题.在此基础上,基于Lyapunov稳定性定理证明闭环系统误差信号有界,并进一步分析得到系统估计和跟踪误差与控制器参数的定量关系,即都可以随观测器增益的增大而达到无限小.仿真比较结果验证了所提出方案的有效性,与韩式自抗扰控制方案相比,该方案结构简单,调节参数少,易于工程实现.     

一类严格反馈系统变比例增益精确微分补偿控制

针对包含不确定函数和未知外部扰动的一类严格反馈型非线性系统,提出基于精确扰动观测器的变比例增益自适应模糊控制器.系统中的未知不确定函数由模糊逻辑系统在线逼近,同时将模糊逻辑系统的逼近误差和未知外部扰动定义为总扰动,利用精确扰动观测器进行精确微分补偿控制. 将非线性函数应用于设计可调节的输出反馈增益,有效消除系统的稳态误差,使得系统跟踪误差可以控制在零的任意小邻域内.最后,通过Lyapunov定理证明闭环系统中所有信号均是有界的.数值仿真表明了所提出方案的有效性.     

时变不确定非线性系统自适应滑模跟踪控制

针对一般的具有时变且界未知的非线性不确定性的单输入多输出非线性系统,提出一种自适应滑模跟踪控制器的框架.在该框架内,系统的时变且界未知的非线性不确定性可以通过函数逼近技术(FAT)表示成为一组正交基函数序列的组合,并通过滑模控制技术和直接Lyapunov方法获得基函数系数的更新律以及对不确定性逼近误差的在线自适应补偿,从而得到自适应的滑模控制律.所提出的基于函数逼近技术的自适应滑模跟踪控制策略在直流电机跟踪控制系统实验装置上进行了实际控制实验,并进行了性能的对比与分析.     

具有反馈信息的迭代学习控制律在Lebesgue-p范数意义下的收敛性

针对一类线性时不变系统, 提出了具有反馈信息的PD-型(Proportional-derivative-type)迭代学习控制律, 利用卷积的推广的Young不等式, 分析了控制律在Lebesgue-p范数意义下的单调收敛性. 分析表明, 收敛性不但决定于系统的输入输出矩阵和控制律的微分学习增益, 而且依赖于系统的状态矩阵和控制律的比例学习增益; 进一步, 当适当选取反馈增益时, 反馈信息可加快典型的PD-型迭代学习控制律的单调收敛性. 数值仿真验证了理论分析的正确性和控制律的有效性.     

基于神经网络的不确定机器人自适应滑模控制

提出一种机器人轨迹跟踪的自适应神经滑模控制。该控制方案将神经网络的非线性映射能力与变结构控制理论相结合,利用RBF网络自适应学习系统不确定性的未知上界,神经网络的输出用于自适应修正控制律的切换增益。这种新型控制器能保证机械手位置和速度跟踪误差渐近收敛于零。仿真结果表明了该方案的有效性。     

基于有限时间跟踪微分器的迭代学习控制

针对迭代学习控制（Iterative learning control,ILC）中的初始状态问题,提出了采用有限时间跟踪微分器安排过渡过程方法,根据迭代学习控制中期望轨迹已知的特点,设计了其参数有明显物理意义并且调节方便的有限时间跟踪微分器. 在此基础上,针对一类具有不确定性的非线性时变系统的迭代学习控制问题,提出了具有对不确定项进行估计的迭代学习控制算法,并应用类Lyapunov方法给出了相关定理证明. 仿真结果表明所提出的方法是有效的.     

Pulse compensation for PD-type iterative learning control against initial state shift

The rectangular pulse function is adopted to incorporate feed-forward compensation for various proportional-derivative-type iterative learning control updating laws applied to a class of linear time-invariant systems with initial state shift. The objective of pulse compensation is to suppress the tracking discrepancy incurred by initial state shift. By means of the generalised Young inequality of the convolution integral, the tracking performance of the pulse-based learning updating laws is analysed and the suppressive effect of the pulse compensation is evaluated by measuring the tracking error in the sense of Lebesgue-p norm. The derivation clarifies that the upper bound of the asymptotical tracking error can be improved by tuning the compensation gain properly though it is determined not only by the proportional and derivative learning gains but also by the system state, input and output matrices as well. Numerical simulations show that pulse compensation can effectively suppress the tracking error caused by initial state shift.     

Composite adaptive locally weighted learning control for multi-constraint nonlinear systems

A composite adaptive locally weighted learning (LWL) control approach is proposed for a class of uncertain nonlinear systems with system constraints, including state constraints and asymmetric control saturation in this paper. The system constraints are tackled by considering the control input as an extended state variable and introducing barrier Lyapunov functions (BLFs) into the backstepping procedure. The system uncertainty is approximated by a composite adaptive LWL neural networks (NNs), in which a prediction error is constructed by using a series-parallel identification model, and NN weights are updated by both the tracking error and the prediction error. The update law with composite error feedback improves uncertainty approximation accuracy and trajectory tracking accuracy. The feasibility and effectiveness of the proposed approach have been demonstrated by formal proof and simulation results.     

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。     

Robust Discrete-Time Iterative Learning Control for Nonlinear Systems With Varying Initial State Shifts

This note is concerned with the robust discrete-time iterative learning control (ILC) design for nonlinear systems with varying initial state shifts. A two-gain ILC law is considered using a 2-D analysis approach. Sufficient conditions are derived to guarantee both convergence of the learning process for fixed initial condition and boundedness of the tracking error for variable initial condition. It is shown that the error data with anticipation in time can well handle the varying initial state shifts in discrete-time ILC.      

重复学习控制及其在一类非线性系统中的应用

为跟踪或抑制仅周期已知的未知周期参考或扰动信号，提出一种新的重复学习控制方法，利用系统的稳态误差并通过迭代学习构造前馈补偿，实现了误差的渐近收敛，将所提出方法应用于一类常见的扰动信号和系统输出具有未知非线性关系的非线性系统，假设其满足连续里普希斯条件，利用重复学习控制器，系统的稳态误差可以减小到极低的程度，该方法控制精度高，实现简单，与传统的基于时延内模的重复控制方法相比，具有对非重复性干扰不敏感的优点，仿真结果验证了该方法的有效性。     

非线性系统在任意初值下的PID型迭代学习控制

针对一类在有限时间区间上重复运行的非线性系统,给出了一种可以解决迭代学习控制中任意初值问题的PID型迭代学习算法及其收敛条件。采用算子理论证明了该算法的收敛性,结果表明该算法不仅有效解决了迭代学习控制的初值问题,而且放宽了收敛条件。仿真分析及与PD型迭代学习控制算法的仿真结果的对比证明,非线性系统在任意初值条件下经过PID型迭代学习后跟踪精度显著提高,输出误差曲线更快速趋于零,表明了该算法的有效性。     

Adaptive learning control for general nonlinear systems with nonuniform trial lengths,initial state deviation,and unknown control direction

This paper studies the iteration varying trail lengths problem for high‐order continuous‐time nonlinear systems, where the initial state may deviate from the desired value and the sign of input gain is unknown. First, to deal with the general nonlinear systems, a fuzzy approximation technique is applied for each dimension of the nonlinear function and the backstepping technique is then used for controller design and performance analysis. Moreover, to deal with the randomly varying trial length problem, we introduce a novel composite energy function for the asymptotic convergence analysis. Furthermore, the initial state deviation issue is resolved by introducing an initial state learning protocol such that the initial state tracking error converges to zero asymptotically. Last but not least, the unknown control direction is regulated by applying a Nussbaum function and the analysis in the presence of nonuniform trial lengths is strictly established. Based on these treatments, we prove that the tracking error converges to zero as iteration number increases and all signals are bounded. The effectiveness of the proposed framework is verified by numerical simulations.     

基于迭代扩张状态观测器的数据驱动最优迭代学习控制

针对一类带扰动有限时间内重复运行的离散时间非线性非仿射不确定系统,本文提出了一种基于迭代扩张状态观测器的数据驱动最优迭代学习控制方法.首先,提出了改进的迭代动态线性化方法,将被控系统线性化为与控制输入有关的仿射形式,并将不确定性合并到一个非线性项中;然后,设计了迭代扩张状态观测器对非线性不确定项进行估计,作为对扰动的补偿;最后,设计了性能指标函数,通过最优技术,提出了参数迭代更新律和最优学习控制律.本文通过数学分析,证明了跟踪误差的有界收敛性.仿真结果验证了方法的有效性.所提出的新型迭代动态线性化方法可很大程度上降低线性化后的控制增益的动态复杂性,使其易于估计.所提出的迭代扩张状态观测器可以在重复中学习,对非重复扰动可进行有效的估计.此外,本文控制器的设计与分析是数据驱动的控制方法,除了被控系统的输入输出数据以外,不需要任何其他模型信息.     

加速抑制随机初态误差影响的迭代学习控制

针对一类具有不确定性的多输入多输出非线性系统,提出一种迭代学习控制算法.该算法具有的特点是:针对任意初态情形,结合开环 D型迭代学习控制器的优点,在时间轴上设计了一个随迭代次数增加而缩短的时间段.在该时间段上,控制算法对状态偏差进行修正,以使系统输出在此段时间后跟踪期望输出,且系统跟踪误差收敛到一个界内.这个界仅由系统自身不确定性和不确定的外界干扰决定,与初态误差无关.当外界扰动为0,以及迭代次数趋于无穷时,经过上述时间段后,系统输出精确跟踪期望输出.理论证明和仿真结果都说明了该算法的有效性.     

A Combined Adaptive Law for Fuzzy Iterative Learning Control of Nonlinear Systems With Varying Control Tasks

To deal with the iterative control of uncertain nonlinear systems with varying control tasks, nonzero initial resetting state errors, and nonrepeatable mismatched input disturbance, a new adaptive fuzzy iterative learning controller is proposed in this paper. The main structure of this learning controller is constructed by a fuzzy learning component and a robust learning component. For the fuzzy learning component, a fuzzy system used as an approximator is designed to compensate for the plant nonlinearity. For the robust learning component, a sliding-mode-like strategy is applied to overcome the nonlinear input gain, input disturbance, and fuzzy approximation error. Both designs are based on a time-varying boundary layer which is introduced not only to solve the problem of initial state errors but also to eliminate the possible undesirable chattering behavior. A new adaptive law combining time- and iteration-domain adaptation is derived to search for suitable values of control parameters and then guarantee the closed-loop stability and error convergence. This adaptive algorithm is designed without using projection or deadzone mechanism. With a suitable choice of the weighting gain, the memory size for the storage of parameter profiles can be greatly reduced. It is shown that all the adjustable parameters as well as internal signals remain bounded for all iterations. Moreover, the norm of tracking state error vector will asymptotically converge to a tunable residual set even when the desired tracking trajectory is varying between successive iterations.