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。     

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.     

伺服系统中抑制非线性扰动的有限维重复控制方法

针对一类具有严重非线性扰动的伺服系统,提出一种有限维重复控制方法以实现高精度的信号跟踪.所考虑的扰动与系统输出间存在未知非线性关系,仅设其满足连续里普希斯条件,便可处理大多数实际情况.应用不动点原理,给出了所提出方法有效的充分条件,该条件也是应用基于系统周期不变性的学习控制方法的必要条件.在稳定的闭环系统中,利用H阶有限维重复控制器,可实现参考信号前H次谐波的渐近跟踪.仿真结果证明了方法的有效性.     

一类受扰旋转单摆系统的建模与跟踪控制设计

本文考虑一类受扰旋转单摆系统的建模与跟踪控制问题. 首先, 利用动静法和相对运动原理建立受扰情形下空间旋转摆系统的动力学模型. 然后, 分别以实际跟踪和渐近跟踪为控制目标, 给出相应的控制设计方法. 具体地,利用向量式的反推控制设计方法与不确定性动态补偿机制, 给出自适应实际跟踪控制器, 保证闭环系统所有状态都有界且在有限时间内系统输出到达并保持在参考信号给定的邻域内. 利用反推设计方法, 并结合扰动的学习、切换补偿机制设计自适应切换渐近跟踪控制器, 通过在线调节控制器参数, 保证闭环系统所有状态都有界且系统输出渐近跟踪到给定的参考信号. 最后, 仿真实验验证所提理论结果的有效性. 值得指出的是, 与相关文献相比, 本文所给出的控制设计方法允许系统同时含有未知参数和扰动, 并且扰动不必有已知上界, 因而具有更强的抑制不确定性的能力.     

带有Kalman滤波的调速系统重复补偿PID控制

针对PID调节器对交流调速系统跟踪周期输入信号精度不高,波形畸变严重,以及系统中存在控制干扰信号和测量噪声信号的情况,该文基于重复控制的自学习能力和Kalman滤波在抑制随机扰动中的作用,提出了一种带有Kalman滤波的基于重复控制补偿的PID控制设计方法.仿真结果表明,该控制方法能较好地跟踪周期性输入信号,并消除系统的随机扰动,具有较好的控制品质.     

含有参数化和非参数化不确定性系统重复学习控制

针对含有参数化和非参数化的高阶非线性系统,设计了一种重复学习控制方案.假设未知时变参数和参考信号的共同周期是已知的,通过参数重组技巧,将所有未知时变项合并为一个周期时变向量.将改进Backstepping方法与分段积分机制相结合,构造了微分-差分参数自适应律和重复学习控制律,使跟踪误差在误差平方范数意义下渐近收敛于零.利用Lyapunov泛函,给出了闭环系统收敛的充分条件.仿真结果验证了该方法的有效性.     

非线性不确定系统准最优学习控制

针对不确定非线性系统, 提出准最优学习控制方法, 解决参数与非参数不确定特性同时存在情形下的轨迹跟踪问题. 给出迭代学习与重复学习两种控制策略, 根据Sontag公式解决标称系统的优化控制, 并以鲁棒学习手段处理参数与非参数不确定特性. 提出断续函数连续化方案, 以避免传统Sontag公式在实现时可能存在的颤振问题. 分析证明经过足够多次迭代或足够多个周期的重复运行后, 闭环系统可实现系统状态以预设精度跟踪参考信号. 仿真结果表明所设计学习系统在收敛速度 方面快于非优化设计.     

周期时变系统的鲁棒自适应重复控制

针对周期时变系统,提出一种鲁棒自适应重复控制方法.该方法利用周期学习律估计周期时变参数,并结合鲁棒自适应方法处理非周期不确定性.与现有重复控制不同的是,在控制器设计中引入了新变量—周期数,利用周期系统的重复特性,使界的逼近误差随周期数的增加而逐渐减少,保证了系统的全局渐近稳定性.同时将该方法应用于一类非线性参数化系统,使系统在非参数化扰动的情形下,输出误差仍能收敛于0,倒立摆模型的仿真验证了此结果.该设计方法适用于消除神经网络逼近误差对重复控制系统的影响,理论证明了基于神经网络的鲁棒自适应重复控制系统中所有变量的有界性和输出误差的渐近收敛性,关于机械臂模型的仿真结果验证了受控系统具有良好的跟踪性能.     

参考轨迹更新的点到点迭代学习控制算法优化及应用

针对受非重复扰动作用的离散线性系统的输出跟踪控制问题,提出一种基于参考轨迹更新的点到点迭代学习控制算法.首先通过构建性能指标函数对控制器进行范数优化,并给出相应的收敛性条件,使得系统输出能够跟踪上更新后参考轨迹处的期望点.其次,当系统输出端受到某批次非重复扰动的影响时,进一步通过引入拉格朗日乘子算法构造多目标性能指标函数,以优化鲁棒迭代学习控制器,达到提高收敛速度和跟踪精度的目的.最后将该算法应用于电机驱动的单机械臂控制系统中,仿真结果验证了算法的合理性和有效性.     

不确定Euler-Bernoulli梁方程的边界输出跟踪

本文讨论边界带有控制输入非同位的内部不确定和外部扰动的Euler-Bernoulli梁方程输出跟踪问题. 为处理边界干扰, 文章首先设计了一个新的总扰动估计器, 在线估计未知扰动. 其次基于估计出来的总扰动, 设计一个伺服系统跟踪参考信号. 最后根据自抗扰方法获得控制输出跟踪的反馈控制. 闭环系统被证明是适定和有界的, 且受控系统的输出指数跟踪参考信号.     

一类非参数不确定系统的自适应重复学习控制

本文针对一类非参数不确定系统提出一种全限幅自适应重复学习控制方法. 利用期望轨迹的周期特性, 构 造周期性期望控制输入, 并基于Lyapunov方法设计自适应重复学习控制器, 实现系统对周期性期望轨迹的高精度跟 踪, 且无需已知非参数不确定性的上界. 设计全限幅学习律估计未知的期望控制输入, 保证估计值被限制在指定的 界内. 同时, 通过构造完全平方式消除部分误差相关项, 控制器设计中可避免使用符号函数, 从而抑制控制器抖振问 题. 最后, 基于Lyapunov方法对误差收敛性进行了分析, 并通过仿真对比验证本文所提方法的有效性.     

控制方向未知非线性系统的自适应重复控制

针对控制方向未知的、存在周期性非参数不确定性的一类非线性系统,给出零误差跟踪的重复控制方法.引入Nussbaum函数设计自适应重复控制器,参数估计修正律采用完全饱和形式,将参数估计囿于预先给定的范围内.分析表明,闭环系统中所有信号本身有界,且跟踪误差本身趋于零.数值仿真结果验证了算法的有效性.     

Separated design of robust model predictive control for LPV systems with periodic disturbance

To eliminate the steady-state error of systems with periodic disturbance, the repetitive control (RC) is a useful approach. For practical applications, the controller is designed to both steer system output to a given set-point (or track a given reference signal) and reject periodic disturbance. The learning procedure of RC and the control action to steer system output to a set-point may influence each other and prolong the convergence time RC. In order to reduce this interaction, this paper proposes a separated design approach. A linear parameter varying (LPV) system is considered. A repetitive predictive control (RPC) and a robust model predictive control (RMPC) are separately designed, respectively, corresponding to reject the periodic disturbance and steer system output to the set-point. The convergence of the proposed RPC sub-controller is derived. The numerical examples show that the proposed design is effective.     

A non-standard iterative learning control approach to tracking periodic signals in discrete-time non-linear systems

In this paper we use the formalism of iterative learning control (ILC) to solve the repetitive control problem of forcing a system to track a prescribed periodic reference signal. Although the systems we consider operate continuously in time, rather than with trials that have distinct starting and ending times, we use the ILC approach by defining a 'trial' in terms of completion of a single 'period' of the output trajectory, where a period is an interval from the start of the trial until the system returns to its initial state. The ILC scheme we develop does not use the standard assumption of uniform trial length. In the final result the periodic motion is achieved by 'repetition' of the learned ILC input signal for a single period. Analysis of the convergence of the algorithm uses an intermediate convergence result for the typical ILC problem. This intermediate result is based on a multi-loop control interpretation of the signal flow in ILC. The idea is demonstrated on an example and it is noted that it may be possible to generalize the ideas to broader classes of systems and ILC algorithms.     

Learning control for induction motor servo drives with uncertain rotor resistance

The tracking control problem for induction motor servo drives with mechanical and electrical uncertainties is addressed. Under the assumptions that the reference profile for the rotor angle is periodic of known period and the rotor flux modulus reference signal is persistently exciting, a robust adaptive learning control is designed, which is adaptive with respect to the uncertain rotor resistance and is able to ‘learn’ the periodic disturbance signal due to mechanical uncertainties by identifying the Fourier coefficients of its truncated approximation.     

Period-robust repetitive model predictive control

Repetitive model predictive control (RMPC) incorporates the idea of repetitive control (RC) into the basic formulation of model predictive control (MPC) to enable the user to take full advantage of the constraint handling, multivariable control features of MPC in controlling a periodic process. The RMPC achieves perfect asymptotic setpoint tracking/disturbance rejection in periodic processes, provided that the period length used in the control formulation matches the actual period of the reference/disturbance signal exactly. Even a small mismatch between the actual period of the process and the controller period can deteriorate the RMPCs performance significantly. The period mismatch can occur either from an inaccurate estimation of the actual frequency of disturbance due to resolution limit or from trying to force the controller period to be an integer multiple of the sampling time. For such cases, an extension of RMPC called “period-robust” repetitive model predictive control (pr-RMPC) is proposed. It is based on the idea of using weighted, multiple memory loops in RC, such that small changes in period length do not diminish the tracking/rejection properties by much. Simulation results show that, in case of a slight period mismatch, pr-RMPC achieves significant improvement over the standard RMPC in rejecting periodic disturbances.     

Observer‐based tracking control for switched stochastic systems based on a hybrid 2‐D model

This paper is concerned with the observer‐based output tracking problem for a class of linear switched stochastic systems with time delay and disturbance by using repetitive control approach. More precisely, a two‐dimensional hybrid model is incorporated to obtain and optimize the repetitive controller. In particular, the repetitive controller is used to improve the tracking performance through its continuous learning actions. In addition, an equivalent‐input‐disturbance estimator is incorporated into the repetitive control design approach to reduce the effect of the external disturbances. The main aim of the control design is to track the periodic reference signal with the measured output of the system under consideration even in the presence of an unknown bounded disturbance. By constructing a suitable Lyapunov‐Krasovskii functional and using average dwell time approach and Jensen inequality, sufficient conditions are obtained in terms of linear matrix inequalities to guarantee the mean‐square exponential stability of the considered system. Eventually, a numerical example is provided to demonstrate the effectiveness of the developed method.     

Tutorial review on repetitive control with anti-windup mechanisms

In many control system applications, tracking a periodic reference signal or rejecting a disturbance signal with a limited frequency band is a necessary task. Repetitive control systems are designed to perform such tasks. Because the repetitive control systems by nature have introduced unstable controller structures, control signal amplitude constraints commonly encountered in control system applications need to be considered with special care. Otherwise, the repetitive control system could become unstable when the control signals became saturated. Using the same framework of Model Predictive Control (MPC), but without the cost of online optimization that usually occurs in the MPC algorithms, this paper shows the design and implementation procedures of repetitive control of multi-input and multi-output systems with anti-windup mechanisms. Furthermore, by using Fourier analysis of a reference signal or a disturbance signal, the structure of a repetitive control system is determined. Simple and complex simulation examples are used to illustrate the procedures of design and implementation.