Repetitive control of Hamiltonian systems based on variational symmetry

This paper is concerned with repetitive control of Hamiltonian systems, which is based on iterative learning control utilizing the variational symmetry of those systems. Variational symmetry allows us to obtain an algorithm to solve a certain class of optimal control problems in a repetitive control framework. Therefore, the proposed method can deal with not only trajectory tracking control problems but also optimal trajectory generation problems, never before considered in a repetitive control framework. A convergence analysis of this algorithm is also discussed. Furthermore, some numerical simulations demonstrate the effectiveness of the proposed method.     

Trajectory tracking control of port-controlled Hamiltonian systems via generalized canonical transformations

This paper addresses trajectory tracking control of port-controlled Hamiltonian systems via generalized canonical transformations and passivity-based control. The main strategy adopted in this paper is to construct an error system, which describes the dynamics of the tracking error, by a passive port-controlled Hamiltonian system. After obtaining the error system, tracking control of the original system can be achieved by stabilizing the error system via passivity-based approach. First, a fundamental framework is provided for constructing the error system via generalized canonical transformations. Then a concrete design procedure is derived for a class of electro-mechanical systems. Furthermore, the proposed method is applied to a magnetic levitation system and laboratory experiments demonstrate its effectiveness.     

A composite energy function-based learning control approach for nonlinear systems with time-varying parametric uncertainties

A new learning control approach is developed in this note to address a class of nonlinear systems with time-varying parametric uncertainties. The concept of composite energy function (CEF), which provides the system information along both time and learning repetition horizons, is introduced in the analysis of learning control. CEF consists of two parts. The first part is a standard Lyapunov function,. which is used to access system behavior along time horizon during each learning cycle. The second part is an L/sup 2/ norm of parametric learning errors which reflects the variation of the system status when the control system is updated on the basis of learning cycles. The proposed learning control algorithm achieves asymptotical convergence along a learning repetition horizon. At the same time, the boundedness and pointwise convergence of the tracking error along time horizon is guaranteed. The proposed learning control strategy is applicable to quite general classes of nonlinear systems without requiring the global Lipschitz continuity condition and zero relative degree condition.     

Initial condition issues on iterative learning control for non-linear systems with time delay

Most of the available results on iterative learning control address trajectory tracking problem for systems without time delay. The role of the initial function in tracking performance of iterative learning control for systems with time delay is not yet fully understood. In this paper, asymptotic properties of a conventional learning algorithm are examined for a class of non-linear systems with time delay in the presence of initial function errors. It is shown that a non-zero initial function deviation can cause a lasting tracking error on the entire operation. Impulsive action is one method to eliminate such lasting tracking error but it is not a practical approach. As an alternative, an initial rectifying action is introduced in the learning algorithm. The initial rectifying action is finite and used over a specified interval. It is shown to be effective in the improvement of tracking performance, in particular robustness and uniform convergence. The results are further extended to systems with multiple time delays. An example is given and computer simulations are presented to demonstrate the performance of the proposed approach.     

Adaptive learning tracking control of robotic manipulators with uncertainties

An adaptive learning tracking control scheme is developed for robotic manipulators by a synthesis of adaptive control and learning control approaches. The proposed controller possesses both adaptive and learning properties and thereby is able to handle robotic systems with both time-varying periodic uncertainties and time invariant parameters. Theoretical proofs are established to show that proposed controllers ensure asymptotical tracking performance. The effectiveness of the proposed approaches is validated through extensive numerical simulation results.     

基于非策略Q-学习的网络控制系统最优跟踪控制

针对具有数据包丢失的网络化控制系统跟踪控制问题,提出一种非策略Q-学习方法,完全利用可测数据,在系统模型参数未知并且网络通信存在数据丢失的情况下,实现系统以近似最优的方式跟踪目标.首先,刻画具有数据包丢失的网络控制系统,提出线性离散网络控制系统跟踪控制问题;然后,设计一个Smith预测器补偿数据包丢失对网络控制系统性能的影响,构建具有数据包丢失补偿的网络控制系统最优跟踪控制问题;最后,融合动态规划和强化学习方法,提出一种非策略Q-学习算法.算法的优点是:不要求系统模型参数已知,利用网络控制系统可测数据,学习基于预测器状态反馈的最优跟踪控制策略;并且该算法能够保证基于Q-函数的迭代Bellman方程解的无偏性.通过仿真验证所提方法的有效性.     

Integrated predictive iterative learning control based on updating reference trajectory for point-to-point tracking

A novel control technique is proposed by combining iterative learning control (ILC) and model predictive control (MPC) with updating-reference trajectory for point-to-point tracking problem of batch process. In this paper, a batch-to-batch updating-reference trajectory, which passes through the desired points, is firstly designed as the tracking trajectory within a batch. The updating control law consists of P-type ILC part and MPC part, in which P-type ILC part can improve the performance by learning from previous executions and MPC part is used to suppress the model perturbations and external disturbances. Convergence properties of the integrated predictive iterative learning control (IPILC) are analyzed theoretically, and the sufficient convergence conditions of output tracking error are also derived for a class of linear systems. Comparing with other point-to-point tracking control algorithms, the proposed algorithm can perform better in robustness. Furthermore, updating-reference relaxes the constraints for system outputs, and it may lead to faster convergence and more extensive range of application than those of fixed-reference control algorithms. Simulation results on typical systems show the effectiveness of the proposed algorithm.     

Simple LMI based learning control design

In this note, a simple linear matrix inequality (LMI) design method is proposed for iterative learning control (ILC). The design can ensure a monotonic error decay in 2‐norm. Experimental results on a SCARA robot shows that the design can achieve nearly perfect tracking. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Global output feedback tracking control for a class of Lagrangian systems

The problem of global tracking control without velocity measurement of the so-called Euler–Lagrange systems has been paid a lot of attention for the past several years. In this note, a nice property of one-degree-of-freedom Euler–Lagrange systems is highlighted, which in particular allows us to obtain a new solution to the problem of tracking control for this class of systems. The solution is under the form of a linear-like observer-based controller which gives fairly good results, as illustrated in the simulation. The method can be generalized to any system having the same property.     

Discrete-time adaptive iterative learning control with unknown control directions

An adaptive iterative learning control scheme is proposed for a class of discrete-time nonlinear systems with random initial conditions and iteration-varying desired trajectories. The discrete Nussbaum gain method is incorporated into the control design to tackle the problem associated with the lack of a priori knowledge of the control directions. The proposed control algorithm guarantees the boundedness of all the signals in the controlled system. The tracking error converges to zero asymptotically along the iterative learning axis. The effectiveness of the proposed control law is verified through numerical simulation.     

On Repetitive Learning Control for Periodic Tracking Tasks

In this note, a repetitive learning control (RLC) approach is proposed to deal with periodic tracking tasks for nonlinear dynamical systems with nonparametric uncertainties. We address two fundamental issues associated with the learning control methodology: The existence of the solution, and learning convergence property. Applying the existence theorem of the neutral differential difference equation, and using Lyapunov-Krasovskii functional, the existence of the solution and learning convergence can be proven rigorously. A further extension of the RLC to cascade systems is also explored     

MLP-based adaptive neural control of nonlinear time-delay systems with the unknown hysteresis

In this note, the authors study the tracking problem for uncertain nonlinear time-delay systems with unknown non-smooth hysteresis described by the generalised Prandtl–Ishlinskii (P-I) model. A minimal learning parameters (MLP)-based adaptive neural algorithm is developed by fusion of the Lyapunov–Krasovskii functional, dynamic surface control technique and MLP approach without constructing a hysteresis inverse. Unlike the existing results, the main innovation can be summarised as that the proposed algorithm requires less knowledge of the plant and independent of the P-I hysteresis operator, i.e. the hysteresis effect is unknown for the control design. Thus, the outstanding advantage of the corresponding scheme is that the control law is with a concise form and easy to implement in practice due to less computational burden. The proposed controller guarantees that the tracking error converges to a small neighbourhood of zero and all states of the closed-loop system are stabilised. A simulation example demonstrates the effectiveness of the proposed scheme.     

一类不确定运动系统的空间迭代学习控制

本文讨论了一类在有限空间区间内重复运行的不确定运动系统的跟踪控制问题.通过引入空间状态微分算子和空间复合能量函数,提出了一种空间周期的自适应迭代学习控制算法.首先利用空间状态微分算子,将系统从时间域转化到空间域形式.然后基于空间复合能量函数设计了控制器,利用含限幅作用的参数自适应律逼近系统中的不确定性,同时引入鲁棒项共同抑制非参数不确定性的影响.通过严格的数学分析,证明了在标准初始条件和随机有界初始误差两种情况下的跟踪误差收敛性.最后通过列车仿真进一步验证了该算法的有效性.     

Decentralized PD control for non-uniform motion of a Hamiltonian hybrid system

In this paper, a decentralized proportional-derivative （PD） controller design for non-uniform motion of a Hamiltonian hybrid system is considered. A Hamiltonian hybrid system with the capability of producing a non-uniform motion is developed. The structural properties of the system are investigated by means of the theory of Hamiltonian systems. A relationship between the parameters of the system and the parameters of the proposed decentralized PD controller is shown to ensure local stability and tracking performance. Simulation results are included to show the obtained non-uniform motion.     

Observer and observer-based H∞ control of generalized Hamiltonian systems

This paper deals with observer design for generalized Hamiltonian systems and its applications. First, by using the systems' structural properties, a new observer design method called Augment Plus Feedback is provided and two kinds of observers are obtained: non-adaptive and adaptive ones. Then, based on the obtained observer, H∞ control design is investigated for generalized Hamiltonian systems, and an observer-based control design is proposed. Finally, as an application to power systems, an observer and an observer-based H∞ control law are designed for single-machine infinite-bus systems.Simulations show that both the observer and controller obtained in this paper work very well.     

Output control design and separation principle for a class of port‐Hamiltonian systems

Using a structure preserving observer, a dynamic output controller is proposed for a class of port‐Hamiltonian systems. The core of this method is based on the notion of contractive port‐Hamiltonian systems. The proposed method utilizes an extended form of IDA‐PBC (interconnection and damping assignment passivity‐based control), a well‐known controller design method for port‐Hamiltonian systems and paves the way for using IDA‐PBC in output control design of challenging control objectives, such as output tracking for underactuated mechanical systems. In the line of output control design, a useful separation principle for a class of port‐Hamiltonian systems is achieved, which is valuable in the field of nonlinear systems. Some simulations on magnetic levitation and ball on wheel testbeds show the potency and applicability of the proposed method.     

Dissipative Hamiltonian realization and energy-based L/sub 2/-disturbance attenuation control of multimachine power systems

The note considers the L/sub 2/-disturbance attenuation of multimachine power systems via dissipative pseudo-Hamiltonian realization of the systems. First, the note expresses multimachine systems as a dissipative Hamiltonian system. Then, the note investigates the energy-based control design of L/sub 2/-disturbance attenuation of multimachine power systems and proposes a decentralized simple control strategy. Simulations on a six-machine system show that the achieved L/sub 2/-disturbance attenuation control strategy is very effective.     

Iterative learning control with Smith time delay compensator for batch processes

How to improve the control of batch processes is not an easy task because of modeling errors and time delays. In this work, novel iterative learning control (ILC) strategies, which can fully use previous batch control information and are attached to the existing control systems to improve tracking performance through repetition, are proposed for SISO processes which have uncertainties in modeling and time delays. The dynamics of the process are represented by transfer function plus pure time delay. The stability properties of the proposed strategies for batch processes in the presence of uncertainties in modeling and/or time delays are analyzed in the frequency domain. Sufficient conditions guaranteeing convergence of tracking error are stated and proven. Simulation and experimental examples demonstrating these methods are presented.     

非一致目标跟踪的混合自适应迭代学习控制

针对一类含有时变和时不变参数的高阶非线性系统,结合Backstepping方法,提出了一种新的自适应迭代学习控制方法,该方法由微分-差分型自适应率和学习控制率组成,保证对非一致目标的跟踪误差平方在一个有限区间上的积分渐近收敛于零,克服了传统的迭代学习控制(ILC)对目标轨线限制,可以跟踪非一致目标轨线.通过构造复合能量函数,给出了闭环系统收敛的一个充分条件,仿真结果说明了该方法的有效性和可行性.     

非正则分布参数系统的迭代学习控制

针对一类非正则分布参数系统的迭代学习控制问题进行讨论, 该类分布参数系统由抛物型偏微分方程构成. 基于非正则系统的特点, 使用D型学习律构建得到迭代学习控制律, 并基于压缩映射原理, 证明得到输出跟踪误差在??² 范数意义下沿迭代轴方向的收敛性结论. 仿真算例表明了所提出结论的有效性.