Repetitive Learning Control Method Research Articles

Adaptive friction compensation for a class of mechanical systems based on LuGre model

AbstractThis paper deals with the problem of friction compensation for a class of uncertain mechanical systems based on LuGre model. The objective is to develop control laws such that the position variable of the system tracks a periodic desired signal or a constant desired signal. Since the friction behavior of the system with periodic desired signals is largely different from that of the system with constant desired signals, two different adaptive control methods are presented for the two cases, respectively. For the case of periodic desired signals, an adaptive repetitive learning control method is presented, and for the case of constant desired signals, an adaptive dynamic state feedback control method is presented. Both the control laws can guarantee that all the signals in the closed loop system are bounded and the tracking errors converge to zero. Several numerical simulations are carried out to show effectiveness of the proposed control algorithms.

Repetitive Learning Control for Control-Affine Systems With Time Delays

This article presents a repetitive learning control method, which deals with both parametric and nonparametric uncertainties involved in the control-affine systems with time delays. The desired control is taken as a time-varying parameter, and a learning algorithm is applied for estimation. The time-delay nonlinearity and the state-dependent input gain are handled as the nonparametric uncertainties, for which the adaptive bounding technique is adopted such that the requirement for the knowledge about the system undertaken can be reduced, and the troublesome time delays are effectively treated. The presented control method only needs the lower bound of the input gain is to be known as a priori. Furthermore, it is shown applicable to systems with multiple time delays. The fully saturated learning algorithms are utilized to solve the positive accumulation of estimates so that the possible divergence phenomenon can be avoided. The convergence of the tracking error is established, while the boundedness of the variables in the closed-loop system is assured. The numerical results are presented to verify the effectiveness of the proposed learning control method.

Adaptive fuzzy logic with self-tuned membership functions based repetitive learning control of robotic manipulators

With increasing demand for using robotic manipulators in industrial applications, controllers specific for performing repeatable tasks are required. These controllers must also be robust to model uncertainties. To address this research issue, a repetitive learning control method fused with adaptive fuzzy logic techniques is designed. Specifically, modeling uncertainties are first modeled with a fuzzy logic network and an adaptive fuzzy logic strategy with online tuning is designed. The stability is investigated via Lyapunov type techniques where global uniform ultimate boundedness of closed loop system is guaranteed. Numerical simulation results obtained from a two degree of freedom robot manipulator model and experiments performed on a robot manipulator demonstrate the efficacy of the proposed control methodology.

An Active Repetitive Learning Control Method for Lateral Suspension Systems of High-Speed Trains

This article presents a novel perspective to improve the ride quality of high-speed trains (HSTs), namely, by virtue of the periodicity of lateral dynamics to suppress the lateral vibration of HST. To resolve the contradiction between the complex HST model and the effective controller design, a simplified three-degrees-of-freedom (3-DOF) quarter-vehicle model is first employed for controller design, while a 17-DOF full-vehicle model is built for efficiency verification, where periodic and random track irregularities are considered, respectively. An active repetitive learning control (RLC) method is proposed to achieve the periodic tracking control, where the learning convergence is proved rigorously in a Lyapunov way. The configuration of RLC-based lateral suspensions is economical in the sense that only four actuators and six sensors are needed. It is verified by simulation that, compared with the dynamic matrix controller, the proposed RLC controller has greatly reduced the lateral vibration of a vehicle body, especially the lateral acceleration in the frequency range of (0, 3] Hz to which human body is strongly sensitive.

A Repetitive Learning Method Based on Sliding Mode for Robot Control With Actuator Saturation

This paper proposes a sliding mode based repetitive learning control method for high-precision tracking of robot manipulators with actuator saturation. Advantages of the proposed control include the absence of model parameter in the control law formulation and the ability to remove the possibility of actuator failure due to excessive torque input levels. Lyapunov's direct method is employed to prove semiglobal asymptotic tracking. Simulation results on a three degree-of-freedom (3DOF) robot illustrate the effectiveness and improved performance of the proposed scheme.

Robust adaptive repetitive learning control for a class of time-varying nonlinear systems with unknown control direction

A robust adaptive repetitive learning control method is proposed for a class of time-varying nonlinear systems. Nussbaum-gain method is incorporated into the control design to counteract the lack of a priori knowledge of the control direction which determines the motion direction of the system under any input. It is shown that the system state could converge to the desired trajectory asymptotically along the iteration axis through repetitive learning. Simulation is carried out to show the validity of the proposed control method.

Repetitive learning control for triangular systems with unknown control directions

A backstepping repetitive learning control method is proposed for a class of high-order non-linear systems with triangular structure with unknown control coefficients. The Nussbaum-gain method is incorporated into the control design to counteract the lack of a priori knowledge of the control directions. A differential-difference repetitive control law is presented to avoid the difficulties encountered in the derivation of the fictitious control. It is proved that the output of controlled system converges to the desired trajectory asymptotically along the iterative learning axis through repetitive learning. Simulation is carried out to show the validity of the proposed control method.

Repetitive Learning Control for Nonlinear Systems with Unknown Control Directions

AbstractA repetitive learning control method combined with backstepping technology is proposed for a class of high-order nonlinear systems with triangular structure with unknown control coefficients. The Nussbaum-gain method is incorporated into the control design to counteract the lack of a priori knowledge of the control directions. A differential-difference repetitive control law is presented to avoid the difficulties encountered in the derivation of the fictitious control. It is shown that the output of the controlled system could converge to the desired trajectory asymptotically through repetitive learning.

Repetitive learning control of nonlinear systems over finite intervals

Iterative learning control requires initial repositioning, while the time functions to be learned should be of periodicity in repetitive control. However, there are cases in practice where the time-varying unknowns are not periodic but repetitive, and repetitive learning control is applicable with avoidance of initial repositioning. In this paper, repetitive learning control designs are presented for a broader class of nonlinear systems over finite intervals. The Freeman formula is modified and used for stabilization of the nominal nonlinear time-varying system undertaken. The global stability of the learning system and asymptotic convergence of the tracking error are established through analysis of both partially and fully saturated learning algorithms, respectively. The repetitive learning control method is theoretically shown to be effective in dealing with time-varying parametric uncertainties.

Repetitive Learning Control of Nonlinear Continuous-Time Systems Using Quasi-Sliding Mode

In this brief, a quasi-sliding mode (QSM)-based repetitive learning control (RLC) method is proposed for tackling multi-input multi-output nonlinear continuous-time systems with matching perturbations. The proposed RLC method is able to perform rejection of periodic exogenous disturbances as well as tracking of periodic reference trajectories. It ensures a robust system stability when it is subject to nonperiodic uncertainties and disturbances. In this brief, an application to a robotic manipulator is used to illustrate the performance of the proposed QSM-based RLC method. A comparative study with the conventional variable structure control (VSC) technique is also included

A Repetitive Learning Method Based on Sliding Mode for Robot Control

In order to make a robot precisely track desired periodic trajectories, this work proposes a sliding mode based repetitive learning control method, which incorporates characteristics of sliding mode control into repetitive learning control. The learning algorithm not only utilizes shape functions to approximate influence functions in integral transforms, but also estimates inverse dynamics functions based on integral transforms. It learns at each sampling instant the desired input joint torques without prior knowledge of the robot dynamics. To carry out sliding mode control, a reaching law method is employed, which is robust against model uncertainties and external disturbances. Experiments are performed to validate the proposed method. [S0022-0434(00)02001-3]

Series and parallel transformer resonant DC–DC converter using optimal digital servo and repetitive learning control schemes

A digitally processed repetitive learning control approach is presented in combination with a type-1 optimal digital servo control scheme of the resonant DC-DC converter using IGBT modules for a medical X-ray power generator. This scheme is considered for achieving a rapid transient response without any overshoot in addition to high-voltage characteristics with low ripple in its steady-state within a wide setting range of output voltage. To improve nonlinear voltage regulation performance of the series capacitor compensated transformer parallel resonant inverter-type fixed frequency, phase-shifted PWM DC-DC converter with a high-voltage transformer link, the repetitive learning control method is introduced into medical X-ray power generator systems using a series and parallel resonant DC-DC converter with high voltage transformer matching. This makes use of transformer parasitic parallel resonance with a series-compensated resonant capacitor connected to a transformer parasitic leakage inductor. The effectiveness of the repetitive learning control for this converter is discussed. A digital servo control scheme with error compensation loops is implemented to eliminate periodic voltage ripple fluctuations and abrupt voltage changes in the rectified DC input of this converter. This scheme also aims to realise good reproducibility, which is an important requirement in an X-ray power generator. The practical effectiveness of the repetitive learning control method and the type-1 optimal digital servo control scheme are confirmed by simulation analysis and experiments.