Adaptive Iterative Learning Control Approach Research Articles

Feedforward-feedback-enhanced model-free adaptive iterative learning control with measurement disturbance and data dropout for an autonomous bus trajectory tracking system.

This article presents an innovative enhanced model-free adaptive iterative learning control approach suited for autonomous bus trajectory tracking systems that may experience measurement disruptions and random data dropouts. Data loss can occur independently and randomly at different times and in different iterations with varying probabilities, leading to successive data dropouts on both the time and iteration axes. The proposed enhanced model-free adaptive iterative learning control controller incorporates a data compensation mechanism to compensate for missing data, ensuring excellent control performance. This data-driven control strategy requires only input/output data for controller design. The convergence and effectiveness of the proposed approach are verified through rigorous mathematical analysis and simulation outcomes.

Iterative Learning Control for Output Tracking of Nonlinear Systems With Unavailable State Information.

This work presents a novel design framework of adaptive iterative learning control (ILC) approach for a class of uncertain nonlinear systems. By using the closed-loop reference model that can be viewed as an observer, the proposed adaptive ILC approach can be adapted to deal with the output tracking problem of nonlinear systems with unavailable system states. In the systems considered, two classes of uncertainties are taken into account, including parametric input disturbances and input distribution uncertainties. To facilitate the controller design and convergence analysis, the composite energy function (CEF) methodology is employed. The design framework in this brief is novel and widely applicable, which extends the CEF-based ILC approach to output tracking control of nonlinear systems without requiring full knowledge of state information and complicated observer design process. To show the effectiveness of the proposed design framework and control algorithms, two numerical examples are illustrated.

Frequency-Domain Data-Driven Adaptive Iterative Learning Control Approach: With Application to Wafer Stage

The feedforward control is becoming increasingly important in ultra-precision stages. However, the conventional model-based methods cannot achieve expected performance in new-generation stages since it is hard to obtain the accurate plant model due to the complicated stage dynamical properties. To tackle this problem, this article develops a model-free data-driven adaptive iterative learning approach that is designed in the frequency-domain. Explicitly, the proposed method utilizes the frequency-response data to learn and update the output of the feedforward controller online, which has benefits that both the structure and parameters of the plant model are not required. An unbiased estimation method for the frequency response of the closed-loop system is proposed and proved through the theoretical analysis. Comparative experiments on a linear motor confirm the effectiveness and superiority of the proposed method, and show that it has the ability to avoid the performance deterioration caused by the model mismatch with the increasing iterative trials.

A novel adaptive iterative learning control approach and human-in-the-loop control pattern for lower limb rehabilitation robot in disturbances environment

A novel adaptive iterative learning control approach and human-in-the-loop control pattern for lower limb rehabilitation robot in disturbances environment

Adaptive iterative learning control for a class of fractional-order nonlinear systems

Based on the discussions on the properties of fractional integral and Caputo fractional derivative, an adaptive iterative learning control approach is proposed for a class of fractional-order nonlinear system (FONS) with unknown time-varying parameter. With the design of learning controller and adaptive learning law for unknown parameter, the tracking error sequence converges to zero in the iteration domain while all the closed-loop signals remain bounded. Finally, a numerical example is given to verify the validity of the designed method.

An adaptive iterative learning control approach based on disturbance estimation for manipulator system

An adaptive iterative learning control approach based on disturbance estimation has been developed for trajectory tracking of manipulators with uncertain parameters and external disturbances. The external disturbances are estimated by the feedback iterative learning method, whereas the uncertain parameters are compensated by adaptive control. This approach which is based on the disturbance estimation technique provides a rapid convergence of trajectory tracking errors. According to the Lyapunov theory, the sufficient condition of the asymptotic stability has been developed for the 2-degrees of freedom (DOFs) manipulator system. The numerical results show that the adaptive iterative learning control approach based on disturbance estimation is feasible and effective for the 2-DOFs manipulator. A comparison of the adaptive iterative learning control method and the iterative learning control method is completed, which shows that the adaptive iterative learning control method performs a faster convergence of the disturbance to the steady state.

HONN-Based Adaptive ILC for Pure-Feedback Nonaffine Discrete-Time Systems With Unknown Control Directions.

Nearly all adaptive control techniques require that the control directions of dynamical systems are known in advance. In this paper, for a class of pure-feedback nonaffine discrete-time systems with unknown control directions (UCDs), a high-order neural network (HONN)-based adaptive iterative learning control (ILC) approach is presented to address a repetitive tracking control issue. The implicit function theorem is adopted to cope with the difficulty resulting from the nonaffine structure of control input. Employing a discrete Nussbaum-type function in the neural network weight adaptation law to suit the UCD, an HONN is used to iteratively estimate the ideal control signals. In addition, a novel dead-zone method is developed in the HONN-based adaptive ILC algorithm to enhance its robustness against nonrepetitive desired trajectories and random uncertainties in iterative initial errors and external disturbance. Consequently, the system output, except at the initial n time instants, is demonstrated to asymptotically converge to an adjustable range of the desired trajectory along the iteration axis, while all of the system signals remain bounded during the entire ILC process. Two simulation examples show the feasibility of the adaptive ILC approach.

Adaptive Iterative Learning Control of Robot Manipulators for Friction Compensation

Iterative learning control (ILC) has been well recognized for its ability to improve the tracking performance of systems that perform repetitive tasks. Its achievable performance, however, can be significantly degraded by the presence of non-repetitive disturbances which vary every iteration. Such may be a case for high-precision robot manipulators which are commonly subject to nonlinear and velocity-dependent friction forces. With a traditional PD-type ILC scheme, as joint velocities change along iterations, friction forces can vary substantially and deteriorate the performance of ILC. To address this problem, we propose an adaptive ILC algorithm, in which an adaptive friction compensation signal is introduced with ILC to adaptively identify the friction model over multiple iterations. Theoretical convergence analysis is provided with simulation verification on a 3-degree-of-freedom (DOF) planar manipulator. Experimental verification is also performed on a 5-DOF robot for silicon wafer handling. The verification results show that the proposed adaptive ILC approach can achieve significantly better tracking performance than traditional ILC methods.

Adaptive ILC for tracking non-repetitive reference trajectory of 2-D FMM under random boundary condition

Almost all of the existing research achievements in Iterative Learning Control (ILC) hitherto have been focused on One-Dimensional (1-D) dynamical systems. Few ILC researches are related to Two-Dimensional Fornasini Marchesina Model (2-D FMM). In this paper, an adaptive ILC approach is proposed for 2-D FMM system with non-repetitive reference trajectory under random boundary condition. The proposed adaptive ILC algorithm learns the coefficient matrices of the system and updates the control input iteratively. As the times of iteration goes to infinity, the ILC tracking error outside the boundary tends to zero and all system signals keep bounded in the whole ILC process. Illustrative examples are provided to verify the validity of the proposed adaptive ILC algorithm.

Robust adaptive iterative learning control for discrete‐time nonlinear systems with both parametric and nonparametric uncertainties

SummaryA new robust adaptive iterative learning control approach is proposed for discrete‐time nonlinear systems with both parametric and nonparametric uncertainties. By virtue of a well‐designed dead‐zone function, the learning of the parametric and nonparametric uncertainties can be performed concurrently. Rigorous Lyapunov function‐based analysis ensures that the effect of system uncertainties can be fully compensated, and the tracking error will converge to zero asymptotically in the iteration domain, even under random initial conditions and iteration‐varying reference trajectories. The efficacy of the proposed controller is demonstrated by simulating a single‐link robot manipulator with unknown frictions. Copyright © 2015 John Wiley & Sons, Ltd.

ON ADAPTIVE ITERATIVE LEARNING CONTROL ALGORITHM FOR DISCRETE-TIME SYSTEMS WITH PARAMETRIC UNCERTAINTIES SUBJECT TO SECOND-ORDER INTERNAL MODEL

The adaptive iterative learning control methods is used to control discrete-time systems with time-iteration-varying uncertainties. By exploiting second-order internal model, the time-iteration varying law of uncertainties are derived. A robust adaptive iterative learning control approach is presented for discrete-time systems with exogenous disturbance. The tracking error of the designed adaptive iterative learning control law converges to zero asymptotically as iteration number approaches to infinity even if reference trajectories varying from iteration to iteration. Numerical example is given to validate the adaptive iterative learning control technique.

Adaptive iterative learning control for coordination of second‐order multi‐agent systems

SUMMARYIn this paper, an efficient framework is proposed to the consensus and formation control of distributed multi‐agent systems with second‐order dynamics and unknown time‐varying parameters, by means of an adaptive iterative learning control approach. Under the assumption that the acceleration of the leader is unknown to any follower agents, a new adaptive auxiliary control and the distributed adaptive iterative learning protocols are designed. Then, all follower agents track the leader uniformly on [0,T] for consensus problem and keep the desired distance from the leader and achieve velocity consensus uniformly on [0,T] for the formation problem, respectively. The distributed multi‐agent coordinations performance is analyzed based on the Lyapunov stability theory. Finally, simulation examples are given to illustrate the effectiveness of the proposed protocols in this paper.Copyright © 2013 John Wiley & Sons, Ltd.

Adaptive iterative learning control of non-linear MIMO continuous systems with iteration-varying initial error and reference trajectory

In this article, an adaptive iterative learning control (ILC) approach is presented to deal with a class of non-linear multi-input multi-output (MIMO) continuous systems with parametric uncertainty. Unlike general ILC techniques, the proposed adaptive ILC approach allows that both the initial error and the reference trajectory are iteration-varying in the ILC process. The designed ILC tracking strategy is to set out a very small initial time interval, and track the iteration-varying reference trajectory beyond the initial time interval. The reference trajectory tracking error beyond the initial time interval can be driven to zero. While the proposed adaptive ILC technique is applied to the repetitive learning control of non-linear MIMO continuous systems, a complete reference trajectory tracking over the whole time interval can be achieved.

Adaptive iterative learning control for nonlinearly parameterized systems with unknown time-varying delay and unknown control direction

This paper proposes a new adaptive iterative learning control approach for a class of nonlinearly parameterized systems with unknown time-varying delay and unknown control direction. By employing the parameter separation technique and signal replacement mechanism, the approach can overcome unknown time-varying parameters and unknown time-varying delay of the nonlinear systems. By incorporating a Nussbaum-type function, the proposed approach can deal with the unknown control direction of the nonlinear systems. Based on a Lyapunov-Krasovskii-like composite energy function, the convergence of tracking error sequence is achieved in the iteration domain. Finally, two simulation examples are provided to illustrate the feasibility of the proposed control method.

Discrete‐time Adaptive ILC for Non‐parametric Uncertain Nonlinear Systems with Iteration‐Varying Trajectory and Random Initial Condition

AbstractThis paper presents a new discrete‐time adaptive iterative learning control approach (AILC) for a class of time‐varying nonlinear systems with nonparametric uncertainties and non‐repeatable external disturbances by incorporating a novel iterative estimate scheme. A major distinct feature of the presented approach is that uncertainties can be completely compensated for, using only I/O data. Another distinct feature is that the pointwise convergence is achieved over a finite time interval without requiring the matching condition on initial states and reference trajectory. Rigorous mathematical analysis is developed, and simulation results illustrate the effectiveness of the proposed approach.

Adaptive Iterative Learning Control for Nonlinear Systems With Unknown Control Gain1

An adaptive iterative learning control approach is proposed for a class of single-input single-output uncertain nonlinear systems with completely unknown control gain. Unlike the ordinary iterative learning controls that require some preconditions on the learning gain to stabilize the dynamic systems, the adaptive iterative learning control achieves the convergence through a learning gain in a Nussbaum-type function for the unknown control gain estimation. This paper shows that all tracking errors along a desired trajectory in a finite time interval can converge into any given precision through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.