Earticle

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.

This paper deals with a approximation and tracking control of hydraulic servo system using a real time recurrent neural networks (RTRN) with 2-dimensional iterative learning rule. And it was driven that 2-dimensional iterative learning rule in discrete time. In order to control the trajectory of position, two RTRN with same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm is able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two same RTRN was very effective to control trajectory tracking of electro-hydraulic servo system.

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.

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.

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.

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.

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.

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.

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.

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.

In this paper, the robustness property of 2nd-order iterative learning control(ILC) method for a class of linear and nonlinear discrete-time dynamic systems is studied. 2nd-order ILC method has the PD-type learning algorithm based on both time-domain performance and iteration-domain performance. It is proved that the 2nd-order ILC method has robustness in the presence of state disturbances, measurement noise and initial state error. In the absence of state disturbances, measurement noise and initialization error, the convergence of the 2nd-order ILC algorithm is guaranteed. A numerical example is given to show the robustness and convergence property according to the learning parameters.

This paper presents a stability analysis of the iterative learning control (ILC) problem for discrete-time systems when the plants are subject to output measurement data dropouts. It is assumed that data dropout occurs during the data transfers from the plant to the ILC controller, resulting in what is called intermittent ILC. Using the super-vector approach for ILC, the expectation of output error is used to develop conditions for stability of the first order ILC and high order ILC processes. Through the theoretical analysis, it is shown that the convergence of the intermittent ILC is guaranteed although some measurements are missing. The analysis is also supported by numerical examples.

This paper presents a system identification technique for continuous-time state-space system using the iterative learning control. The transfer function parameters are regarded as functions with respect to the state-space parameters which will be identified. The relationship between the state-space parameters and the response error is explicitly derived. An update law of the state-space parameters is proposed so as to improve the convergence speed. The effectiveness of the proposed identification technique is demonstrated by numerical examples.

In this paper, a parameter optimization based iterative learning control strategy is presented for permanent magnet synchronous motor control. This paper analyzes the mechanism of iterative learning control suppressing PMSM torque ripple and discusses the impact of controller parameters on steady-state and dynamic performance of the system. Based on the analysis, an optimization problem is constructed, and the expression of the optimal controller parameter is obtained to adjust the controller parameter online. Experimental research is carried out on a 5.2kW PMSM. The results show that the parameter optimization based iterative learning control proposed in this paper achieves lower torque ripple during steady-state operation and short regulating time of dynamic response, thus satisfying the demands for both steady state and dynamic performance of the speed regulating system.

A stochastic optimal power flow (S-OPF) model considering uncertainties of load and wind power is developed based on chance constrained programming (CCP). The difficulties in solving the model are the nonlinearity and probabilistic constraints. In this paper, a limit relaxation approach and an iterative learning control (ILC) method are implemented to solve the S-OPF model indirectly. The limit relaxation approach narrows the solution space by introducing regulatory factors, according to the relationship between the constraint equations and the optimization variables. The regulatory factors are designed by ILC method to ensure the optimality of final solution under a predefined confidence level. The optimization algorithm for S-OPF is completed based on the combination of limit relaxation and ILC and tested on the IEEE 14-bus system.

In this paper, we present an iterative learning control (ILC) framework for tracking problems with predefined data points that are desired points at certain time instants. To design ILC systems for such problems, a new ILC scheme is proposed to produce output curves that pass close to the desired points. Unlike traditional ILC approaches, an algorithm will be developed in which the control signals are generated by solving an optimal ILC problem with respect to the desired sampling points. In another word, it is a direct approach for the multiple points tracking ILC control problem where we do not need to divide the tracking problem into two steps separately as trajectory planning and ILC controller.The strength of the proposed formulation is the methodology to obtain a control signal through learning law only considering the given data points and dynamic system, instead of following the direction of tracking a prior identified trajectory. The key advantage of the proposed approach is to significantly reduce the computational cost. Finally, simulation results will be introduced to confirm the effectiveness of proposed scheme.

This paper develops a novel controller called iterative learning sliding mode (ILSM) to control linear and nonlinear fractional-order systems. This control applies a combination structures of continuous and discontinuous controller, conducts the system output to the desired output and achieve better control performance. This controller is designed in the way to be robust against the external disturbance. It also estimates unknown parameters of fractional-order systems. The proposed controller unlike the conventional iterative learning control for fractional systems does not need to apply direct control input to output of the system. It is shown that the controller perform well in partial and complete observable conditions. Simulation results demonstrate very good performance of the iterative learning sliding mode controller for achieving the desired control objective by increasing the number of iterations in the control loop.