Earticle

The repeatability of system is a fundamental requirement for various iterative learning control methods, and is a necessary condition for the outcome of perfect tracking. This paper theoretically and numerically explains that how the history before the initial time of dynamic systems influences the current state and repeatability of the system. To this end, the convergence analysis of PD-type iterative learning control for initialized system is presented. A practical preconditioning strategy is added to accelerate the convergence speed, and the detailed discussions of initialization function and initialization response are shown as well. The minimum preconditioning time interval is achieved, and some unique properties of initialized system are illustrated to provide novel challenges for robust and adaptive controls. A number of numerical simulations exhibit that a simple preconditioning process can efficiently improve the performance of the initialized iterative learning control.

In this paper, the iterative learning control (ILC) scheme combined with the backstepping controller is applied to the Wiener system, which is a typical nonlinear and non-Lipschitz one. The ILC scheme as a feed forward control can improve the convergence speed, and the perfect tracking can be achieved as the system is repeatable. The backstepping control is a feedback control which improves the robustness of the control system, especially for the existence of non-repeatable noises. The backstepping part also guarantees the asymptotic stability, which further improves the convergence speed. The design of backstepping controller is based on the error information in the current control process, and the Lyapunov method. The convergence condition is achieved and the convergence speed is analyzed as well. It can be seen that the combination of ILC scheme and backstepping method can improve the system performance to a large extant. Numerical simulations validate the above conclusions.

With the rapid and bursting development of computer and control science, research on iterative learning control system is a hot topic. Random delays of control and measurement signals during transmission over wireless network seriously affect the convergence performance of iterative learning control (ILC) systems. System based on step random delay model, the transfer matrix is derived, which contains random delay impact factor. For different cases of random delay, characteristic value and other elements of the shifting of the lower triangular matrix are analyzed respectively determine the rate of convergence and strong convergence. Analysis shows that the convergence speed is reduced, robust convergence have also been affected. Especially, the impact of control signal delays on robust convergence is greater than that of measurement signal delays. Simulation results are provided to demonstrate correctness of the conclusion. Finally, some potential improvement of proposed method is pointed out.

In this paper, an iterative learning control topic for a kind of singular distributed parameter system with forgetting with time-delay which initial value can vary in a little space has been discussed. And a high level algorithm is extended and proven, using Green formula and Gronwall inequality, that it is suitable for the given system. Convergence of the proposed approach is analyzed and the uniform boundedness of tracking error is obtained in this paper. Numerical simulation is presented for a parabolic partial differential equations solved using ILC based on Eular difference format. At present, the application research of ILC method to the singular distributed parameter system with forgetting factor with time-delay is less. So this paper expands the scope of research for singular system and iterative learning control. Numerical example is given to illustrate the effectiveness of the proposed method.

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.

A control algorithm is proposed for nonlinear multi-input multi-output(MIMO) batch processes by combining quadratic iterative learning control(Q-ILC) with model predictive control(MPC). Both controls are designed based on output feedback and Kalman filter is incorporated for state estimation. Novelty of the proposed algorithm lies in the facts that, unlike feedback-only control, unknown sustained disturbances which are repeated over batches can be completely rejected and asymptotically perfect tracking is possible for zero random disturbance case even with uncertain process model.

This paper presents an approach to estimation of learning gain in iterative learning control for discrete-time affine nonlinear systems. In iterative learning control, to determine learning gain satisfying the convergence condition, we have to know the system model. In the proposed method, the input-output equation of a system is identified by neural network refered to as Piecewise Linearly Trained Network (PLTN). Then from the input-output equation, the learning gain in iterative learning law is estimated. The validity of our method is demonstrated by simulations.

This paper deals with Iterative Learning Control ILC schemes to solve the trajectory tracking problem of strictly unknown nonlinear systems subject to external disturbances, and performing repetitive tasks. Two ILC laws are presented, the first law is the high order, i.e., the information (error) of several iterations are used in the control law. The second law is the ILC with forgetting factor, i.e., the control of the preceding iteration is multiplied by a matrix of the gains. Indeed, the advantage of these algorithms, it is not only applicable for nonlinear systems with model uncertainty, but also for nonlinear systems with no data exists, neither in the structure model nor in the system parameters. In addition, the control design is very simple in the sense that there is no requirement on the choice of the learning gains. Furthermore, the convergence of our algorithms is independent of initial conditions. The asymptotic stability of the closed loop system is guaranteed. This proof is based upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed control schemes. Finally, simulation results on nonlinear system are provided to illustrate the effectiveness of the proposed controllers.

An iterative lear-rung control scheme is newly designed in tile frequency domain. Purposing for batch process control, a generic form of feedback-assisted first-order learning is considered first, and the inverse model-based learning algorithm is derived through convergence analysis in the frequency domain. To enhance the robustness of the proposed scheme, a filtered version is also presented. Performance of the proposed scheme is evaluated through numerical simulations.

In this paper, an approach to deal with model uncertainty using norm-optimal iterative learning control (ILC) is mentioned. Model uncertainty generally degrades the convergence and performance of conventional learning algorithms. To deal with model uncertainty, a worst-case norm-optimal ILC is introduced. The problem is then reformulated as a convex minimization problem, which can be solved efficiently to generate the control signal. The paper also investigates the relationship between the proposed approach and conventional norm-optimal ILC; where it is found that the suggested design method is equivalent to conventional norm-optimal ILC with trial-varying parameters. Finally, simulation results of the presented technique are given.

First of all, an adaptive iterative learning control strategy is developed for a class of nonlinearly parameterized systems with two unknown time-varying parameters and one unknown timevarying delay. The proposed control law includes a PID-type feedback term in time domain and an adaptive learning term used to estimate the unknown time-varying vector in iteration domain. By constructing a Lyapunov-Krasovskii-like composite energy function, we prove the stability of the closedloop system and the convergence of the tracking error. Then, the design idea is further extended to a broader class of systems with mixed parameters in which the unknown time-invariant vector is estimated by a PI-type learning law in time domain. The simulation results, for a time-delay chaotic system, confirm the effectiveness ofthe proposed control scheme.

A scheme of expert system based on iterative learning control is proposed. Iterative learning control can obtain control experience from the historical data to build the knowledge base of expert system. Considering some uncertain factors, a flexible measure is adopted in iterative learning control (ILC). Simulation proves the feasibility and effect of the air conditioning control expert system based on flexible iterative learning control (F-ILC). Finally, a feedback compensation unit is incorporated against irregular heavy disturbance.

In this paper, we discuss a design method of iterative learning control systems for parabolic linear distributed parameter systems(DPSs). First, we discuss some aspects of boundary control of the DPS, and then propose to employ the Karhunen-Loeve procedure to reduce the infinite dimensional problem to a low-order finite dimensional problem. An iterative learning control(ILC) for non-square transfer function matrix is introduced finally for the reduced order system.

Iterative learning control (ILC) is a simple and effective method for the control of systems that perform the same task repetitively. ILC algorithm uses the repetitiveness of the task to track the desired trajectory. In this paper, we propose a PID (proportional plus integral and derivative) type ILC update law for control discrete-time single input single-output (SISO) linear time-invariant (LTI) systems, performing repetitive tasks. In this approach, the input of controlled system in current cycle is modified by applying the PID strategy on the error achieved between the system output and the desired trajectory in a last previous iteration. The convergence of the presented scheme is analyzed and its convergence condition is obtained in terms of the PID coefficients. An optimal design method is proposed to determine the PID coefficients. It is also shown that under some given conditions, this optimal iterative learning controller can guarantee the monotonic convergence. An illustrative example is given to demonstrate the effectiveness of the proposed technique.

This paper proposes a necessary and sufficient condition of convergence in the $L_2$-norm sense for a feedback-based iterative learning control (ILC) system including a multi-input multi-output (MIMO) linear time-invariant (LTI) plant. It is shown that the convergence conditions for a nominal plant and an uncertain plant are equal to the nominal performance condition and the robust performance condition in the feedback control theory, respectively. Moreover, no additional effort is required to design an iterative learning controller because the performance weighting matrix is used as an iterative learning controller. By proving that the least upper bound of the $L_2$-norm of the remaining tracking error is less than that of the initial tracking error, this paper shows that the iterative learning controller combined with the feedback controller is more effective to reduce the tracking error than only the feedback controller. The validity of the proposed method is verified through computer simulations.

다양한 산업체에서 반복적인 특정업무를 수행하는 경우가 흔히 발생한다. 반복되는 오차의 경험치를 근거로 주어진 작업을 추진하는 과정에서 이들 업무의 정밀도제고를 추구함으로써 갖는 성능개선은 사업장의 품질관리와 직결된다. 학습제어의 본래 적용동기는 생산조립라인에 투입되어 반복적인 일을 수행하는 산업로봇의 정밀도 제고이다. 본 논문에서 분산이산시형시스템에서 출발하였으며, 이를 산업용로봇에 적용하기 위하여 수학적으로 모델링한 모의실험을 통하여 알고리즘의 안정성과 반복오차를 줄여가는 과정을 보여 주었다. 입출력정보가 상호간섭 하는 산업용로봇과 같은 복합구조물에서도 모든 시스템(링크)의 정밀도를 만족함을 보여 줌으로써 복합구조물에서 선형반복학습제어의 안정성을 증명하였다.

Comprehensive study on the control system design for a RTP process has been conducted. The purpose of the control system is to maintain maximum temperature uniformity across the silicon wafer achieving precise tracking for various reference trajectories. The study has been carried out in two stages: thermal balance modeling on the basis of a semi-empirical radiation model, and optimal iterative learning controller design on the basis of a linear state space model. First, we found through steady state radiation modeling that the fourth power of wafer temperatures, lamp powers, and the fourth power of chamber wall temperature are related by an emissivity-independent linear equation. Next, for control of the MIMO system, a state space modeland LQG-based two-stage batch control technique was derived and employed to reduce the heavy computational demand in the original two-stage batch control technique. By accommodating the first result, a linear state space model for the controller design was identified between the lamp powers and the fourth power of wafer temperatures as inputs and outputs, respectively. The control system was applied to an experimental RTP equipment. As a consequence, great uniformity improvement could be attained over the entire time horizon compared to the original multi-loop PID control. In addition, controller implementation was standardized and facilitated by completely eliminating the tedious and lengthy control tuning trial.

In this paper, a new scheme of iterative loaming control of a robot manipulator is presented. The proposed method uses a fuzzy sliding mode controller(FSMC), which is designed based on the similarity between the fuzzy logic control(FLC) and the sliding mode control(SMC), for the feedback. With this, the proposed method makes possible fDr fast iteration and has advantages that no linear approximation is used for the derivation of the learning law or in the stability proof Full proof of the convergence of the fuzzy sliding base learning scheme Is given.

A general dynamic iterative learning control scheme is proposed for a class of nonlinear systems. Relying on stabilizing high-gain feedback loop, it is possible to show the existence of Cauchy sequence of feedforward control input error with iteration numbers, which results in a uniform convergance of system state trajectory to the desired one.