Iterative learning control design with high‐order internal model for discrete‐time nonlinear systems

In this paper, a high‐order internal model (HOIM)‐based iterative learning control (ILC) scheme is proposed for discrete‐time nonlinear systems to tackle the tracking problem under iteration‐varying desired trajectories. By incorporating the HOIM that is utilized to describe the variation of desired trajectories in the iteration domain into the ILC design, it is shown that the system output can converge to the desired trajectory along the iteration axis within arbitrarily small error. Furthermore, the learning property in the presence of state disturbances and output noise is discussed under HOIM‐based ILC with an integrator in the iteration axis. Two simulation examples are given to demonstrate the effectiveness of the proposed control method. Copyright © 2016 John Wiley & Sons, Ltd.     

Perfect tracking for maximum-phase nonlinear systems by iterative learning control

A method to track a desired trajectory by iterative learning control is proposed for uncertain maximum-phase nonlinear systems. The relation between the variations in the initial state, input and output is derived and it is shown that the inverse mapping from the desired output to the initial state and input is stable using the time reversal of unstable manifolds for a maximum-phase system as given by Doyle et al. Based on these facts, an input update law is proposed to find the initial state and the input for perfect tracking. Also, it is shown that perfect tracking can be made possible over a finite control horizon by using a non-causal input starting at any fixed state. Simulation results show that the proposed method works well.     

Anti‐windup design of active disturbance rejection control for sampled systems with input delay

A novel anti‐windup design of active disturbance rejection control (ADRC) is proposed for industrial sampled systems with input delay and saturation. By using a generalized predictor to estimate the delay‐free system output, a modified extended state observer is designed to simultaneously estimate the system state and disturbance, which could become an anti‐windup compensator when the input saturation occurs. Accordingly, a feedback controller is analytically designed for disturbance rejection. By proposing the desired closed‐loop transfer function for the set‐point tracking, a prefilter is designed to tune the tracking performance while guaranteeing no steady‐state output tracking error. A sufficient condition for the closed‐loop system stability is established with proof for practical application subject to the input delay variation. Illustrative examples from the literature are used to demonstrate the effectiveness and merit of the proposed control design.     

An iterative learning controller with reduced sampling rate for plants with variations of initial states

In this paper a discrete-time iterative learning controller for single input single output systems is presented. The iterative learning controller works with a reduced sampling rate that ensures the reduction of an appropriate norm of the error trajectory from cycle to cycle and can cope with initial state error. Initial state error occurs when the initial state of the system is different from the initial state that is implicitly given by the reference trajectory. If the initial state changes for every learning iteration, then the controller cannot achieve ideal tracking but the error trajectory is bounded. Using two different sample times together with a potentially time variant learning gain improves the controller performance for dealing with initial state error. Simulation examples are presented to show the results of the proposed iterative learning controller with reduced sampling rate.     

非线性系统迭代跟踪控制的批次遗忘学习算法

针对P型迭代学习算法对初始偏差和输出误差扰动敏感,以及PD型迭代学习算法容易放大系统噪声,降低系统鲁棒性的问题,研究了具有任意有界扰动及期望输出的重复运行非线性时变系统的PD型迭代学习跟踪控制算法.利用迭代学习过程记忆的期望轨迹、期望控制以及跟踪误差,给出基于变批次遗忘因子的学习控制器设计,并借助λ范数理论和Bellman-Gronwall不等式,讨论保证闭环跟踪系统批次误差有界的学习增益存在的充分必要条件,及分析控制算法的一致收敛性.本算法改善了系统的鲁棒性和动态特性,单关节机械臂的跟踪控制仿真验证了方法的有效性.     

Sampled‐data iterative learning control with well‐defined relative degree

This paper addresses the problem of iterative learning control with well‐defined relative degree. The solution is a family of sampled‐data learning algorithms using lower‐order differentiations of the tracking error with the order less than the relative degree. A unified convergence condition for the family of learning algorithms is derived and is proved to be independent of the highest order of the differentiations. In the presence of initial condition errors, the system output is ensured to converge to the desired trajectory with a specified error bound at each sampling instant. The bound will reduce to zero whenever the bound on initial condition errors tends to zero. Numerical examples are provided to illustrate the tracking performance of the proposed learning algorithms. Copyright © 2004 John Wiley & Sons, Ltd.     

Output tracking of stochastic nonlinear systems with unstable linearization

This paper studies the output tracking problem for a class of stochastic nonlinear systems whose linearization parts may have unstable modes via output‐feedback control. This is in contrast with most of the existing results where only state‐feedback control is considered. On the basis of the homogeneous domination technique, an output tracking controller is designed. It is shown that the expectation of tracking error can be made arbitrarily small while all the states of the closed‐loop system remain to be bounded in probability. Finally, a simulation example is given to illustrate the effectiveness of the tracking controller.     

Nonrepetitive trajectory tracking for nonlinear autonomous agents with asymmetric output constraints using parametric iterative learning control

In this paper, we present a novel parametric iterative learning control (ILC) algorithm to deal with trajectory tracking problems for a class of nonlinear autonomous agents that are subject to actuator faults. Unlike most of the ILC literature, the desired trajectories in this work can be iteration dependent, and the initial position of the agent in each iteration can be random. Both parametric and nonparametric system unknowns and uncertainties, in particular the control input gain functions that are not fully known, are considered. A new type of universal barrier functions is proposed to guarantee the satisfaction of asymmetric constraint requirements, feasibility of the controller, and prescribed tracking performance. We show that under the proposed algorithm, the distance and angle tracking errors can uniformly converge to an arbitrarily small positive number and zero, respectively, over the iteration domain, beyond a small user‐prescribed initial time interval in each iteration. A numerical simulation is presented in the end to demonstrate the efficacy of the proposed algorithm.     

Performance analysis of 2‐DOF tracking control for a class of nonlinear uncertain systems with discontinuous disturbances

In this paper, the tracking control problem is considered for a class of uncertain nonlinear systems with infinite discontinuous points in the external disturbance. The extended state observer–based 2‐degree‐of‐freedom control is used with one degree to estimate and cancel the “total disturbance” and the other to force the closed‐loop system to have desired characteristics. The tracking error between the state vector and its ideal trajectory in the entire transient process is adequately discussed to illuminate the performance of resulting control systems. The quantitative analysis shows that the tracking error can be small enough by tuning the bandwidth of the extended state observer. Moreover, the necessary and sufficient condition for the tracking error and the estimation error of the “total disturbance” to converge to zero is presented. The simulation results of a motion test demonstrate that the desired performance of the control system can be achieved despite discontinuous disturbance and nonlinear uncertainties.     

即时学习算法在非线性系统迭代学习控制中的应用

运用即时学习算法来解决一类非线性系统的迭代学习控制初值问题。对于任何类型的迭代学习控制算法，即时学习算法都能有效地估计初始控制量，减小了初始输出误差，加快了算法的收敛速度，使得经过有限次迭代后系统输出能严格跟踪理想信号。对机器人系统的仿真结果表明了该方法的有效性。     

Time‐varying input delay compensation for nonlinear systems with additive disturbance: An output feedback approach

This paper addresses the output feedback tracking control of a class of multiple‐input and multiple‐output nonlinear systems subject to time‐varying input delay and additive bounded disturbances. Based on the backstepping design approach, an output feedback robust controller is proposed by integrating an extended state observer and a novel robust controller, which uses a desired trajectory‐based feedforward term to achieve an improved model compensation and a robust delay compensation feedback term based on the finite integral of the past control values to compensate for the time‐varying input delay. The extended state observer can simultaneously estimate the unmeasurable system states and the additive disturbances only with the output measurement and delayed control input. The proposed controller theoretically guarantees prescribed transient performance and steady‐state tracking accuracy in spite of the presence of time‐varying input delay and additive bounded disturbances based on Lyapunov stability analysis by using a Lyapunov‐Krasovskii functional. A specific study on a 2‐link robot manipulator is performed; based on the system model and the proposed design procedure, a suitable controller is developed, and comparative simulation results are obtained to demonstrate the effectiveness of the developed control scheme.     

Transverse function control with prescribed performance guarantees for underactuated marine surface vehicles

This paper studies the problem of stabilizing reference trajectories (also called as the trajectory tracking problem) for underactuated marine vehicles under predefined tracking error constraints. The boundary functions of the predefined constraints are asymmetric and time‐varying. The time‐varying boundary functions allow us to quantify prescribed performance of tracking errors on both transient and steady‐state stages. To overcome difficulties raised by underactuation and nonzero off‐diagonal terms in the system matrices, we develop a novel transverse function control approach to introduce an additional control input in backstepping procedure. This approach provides practical stabilization of any smooth reference trajectory, whether this trajectory is feasible or not. By practical stabilization, we mean that the tracking errors of vehicle position and orientation converge to a small neighborhood of zero. With the introduction of an error transformation function, we construct an inverse‐hyperbolic‐tangent‐like barrier Lyapunov function to show practical stability of the closed‐loop systems with prescribed transient and steady‐state performances. To deal with unmodeled dynamic uncertainties and external disturbances, we employ neural network (NN) approximators to estimate uncertain dynamics and present disturbance observers to estimate unknown disturbances. Subsequently, we develop adaptive control, based on NN approximators and disturbance estimates, that guarantees the prescribed performance of tracking errors during the transient stage of on‐line NN weight adaptations and disturbance estimates. Simulation results show the performance of the proposed tracking control.     

随机激励下板球系统建模与有限时间全状态预设性能跟踪控制

研究板球系统受到随机激励时的数学建模与轨迹跟踪控制问题.首次建立了板球系统的随机数学模型,并结合backstepping方法、有限时间预设性能函数、全状态约束及新的预设性能推导方法设计了具有未知输入饱和的随机板球系统实际有限时间全状态预设性能跟踪控制器,实现了随机激励下板球系统的有限时间预设性能轨迹跟踪控制.所设计的控制器保证了系统跟踪误差能够被预先给定的有限时间性能函数约束,并且能在任意给定的停息时间内收敛到预先给定的邻域内.最后通过仿真实验验证了所设计控制器具有更好的控制效果.     

误差约束严格反馈系统的迭代学习控制

针对一类严格反馈非线性系统,本文提出误差跟踪学习控制算法,旨在解决状态约束问题和系统的初值问题.文中构造了二次分式型对称障碍Lyapunov函数以及二次分式型非对称障碍Lyapunov函数,并结合反推技术来分别设计学习控制器.两种控制方案里分别采用积分学习律和微分–差分学习律估计未知系数.系统跟踪误差在控制器作用下囿于预设的界内,从而实现迭代过程中对状态的约束;引入期望误差轨迹,经迭代学习后,两种控制方案均能够实现状态误差在整个作业区间上对期望误差轨迹的完全跟踪,并且实现系统输出在预指定作业区间上精确跟踪参考信号.数值仿真结果表明了控制方案的有效性.     

High‐order internal model‐based iterative learning control design for nonlinear distributed parameter systems

This article deals with the problem of iterative learning control algorithm for a class of nonlinear parabolic distributed parameter systems (DPSs) with iteration‐varying desired trajectories. Here, the variation of the desired trajectories in the iteration domain is described by a high‐order internal model. According to the characteristics of the systems, the high‐order internal model‐based P‐type learning algorithm is constructed for such nonlinear DPSs, and furthermore, the corresponding convergence theorem of the presented algorithm is established. It is shown that the output trajectory can converge to the desired trajectory in the sense of (L²,λ) ‐norm along the iteration axis within arbitrarily small error. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.     

Adaptive Terminal ILC for Iteration‐varying Target Points

Terminal iterative learning control (TILC) has been developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations. In this work, the desired terminal point is not fixed but allowed to change run‐to‐run among a set of fixed points and a new adaptive terminal iterative learning control scheme is developed to achieve learning objective over iterations. The control signal is updated from the measured terminal value at the end of a run, instead of the whole output trajectory. Although the reference terminal point is iteration‐varying, the new adaptive TILC guarantees that the tracking error converges to zero iteratively. Both rigorous mathematical analysis and simulation results confirm the applicability and effectiveness of the proposed approach.     

State Space Constrained Iterative Learning Control for Robotic Manipulators

Real‐life work operations of industrial robotic manipulators are performed within a constrained state space. Such operations most often require accurate planning and tracking a desired trajectory, where all the characteristics of the dynamic model are taken into consideration. This paper presents a general method and an efficient computational procedure for path planning with respect to state space constraints. Given a dynamic model of a robotic manipulator, the proposed solution takes into consideration the influence of all imprecisely measured model parameters, making use of iterative learning control (ILC). A major advantage of this solution is that it resolves the well‐known problem of interrupting the learning procedure due to a high transient tracking error or when the desired trajectory is planned closely to the state space boundaries. The numerical procedure elaborated here computes the robot arm motion to accurately track a desired trajectory in a constrained state space taking into consideration all the dynamic characteristics that influence the motion. Simulation results with a typical industrial robot arm demonstrate the robustness of the numerical procedure. In particular, the results extend the applicability of ILC in robot motion control and provide a means for improving the overall trajectory tracking performance of most robotic systems.     

Stable adaptive controller design of robotic manipulators via neuro‐fuzzy dynamic inversion

In this paper, a stable adaptive control approach is developed for the trajectory tracking of a robotic manipulator via neuro‐fuzzy (NF) dynamic inversion, an inverse model constructed by the dynamic neuro‐fuzzy (DNF) model with desired dynamics. The robot neuro‐fuzzy model is initially built in the Takagi‐Sugeno (TS) fuzzy framework with both structure and parameters identified through input/output (I/O) data from the robot control process, and then employed to dynamically approximate the whole robot dynamics rather than its nonlinear components as is done by static neural networks (NNs) through parameter learning algorithm. Since the NF dynamic inversion comprises a cluster of reference trajectories connecting the initial state to the desired state of the robot, the dynamic performance in the initial control stage of robot trajectory tracking can be guaranteed by choosing the optimum reference trajectory. Furthermore, the assumption that the robot states should be on a compact set can be excluded by NF dynamic inversion design. The system stability and the convergence of tracking errors are guaranteed by Lyapunov stability theory, and the learning algorithm for the DNF system is obtained thereby. Finally, the viability and effectiveness of the proposed control approach are illustrated through comparing with the dynamic NN (DNN) based control approach. © 2005 Wiley Periodicals, Inc.     

Optimal preview-based stable-inversion for output tracking of nonminimum-phase linear systems

In this article, a new approach to output tracking of nonminimum-phase systems is proposed. The proposed technique extends the preview-based stable-inversion method to optimally utilize finite-preview (in time) of the future desired output trajectory to find the feedforward input (called the inverse input) for achieving precision output tracking for nonminimum-phase systems. It has been shown that having a large enough preview time is critical to ensure the precision in the preview-based output tracking. The available preview time, however, can be limited due to the physical constraints, and more generally, the associated cost and/or hardware limits. Therefore, we propose obtaining the optimal preview-based inverse input by minimizing, within the preview time window, the predicted tracking error (under the preview-based inverse input) relative to the input energy. A simulation study on a piezoelectric actuator model is used to illustrate the proposed technique.