Improvement of trajectory tracking control performance by using ILC

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.

A novel design strategy for iterative learning and repetitive controllers of systems with a high modal density: Theoretical background

In this article, we discuss iterative learning control (ILC) for systems with input/output (i/o) basis functions. First, we show that various different ILC formulations in the literature can be captured by a common system representation involving i/o basis functions. Analysis of ILC control objectives in this framework yields ILC controller conditions which are required for convergence and optimal performance. Furthermore, analysis reveals how different ILC objectives (monotonic convergence, performance, minimisation of input amplitudes) can be reached by designing separate parts of the ILC controller. The analysis is subsequently expanded by studying the effects of trial-varying disturbances on performance, which results in suggestions for the compensation of these effects. Finally, we use these results to systematically design ILC controllers for the representation under study, and we show that the found results are applicable to existing ILC problem formulations with i/o basis functions, and problem formulations which can be interpreted as such.

In this paper, a modified learning gain for Iterative Learning Control (ILC) approach is proposed for linear Multi-Input-Multi-Output repetitive systems. The control law synthesis is based on the resolution of a quadratic criterion which minimizes the error between the setpoint references and the system outputs at each iteration for each trial. The resolution of the control problem leads to a new gain which avoids matricial inversion problems appeared with classical ILC algorithms such as direct model inversion (I-ILC) and optimal ILC (Q-ILC). The modified type ILC approach improves the learning convergence significantly compared to I-ILC and Q-ILC algorithms. Furthermore, a sufficient and necessary stability condition and convergence properties are established. Simulations with a MIMO Mass-spring damper system show the effectiveness of the proposed method.

Two-dimensional Iterative Learning Control for Batch Processes With State Delay and Time-varying Uncertainties

The problem of iterative learning control (ILC) is considered for a class of time-varying systems with random packet dropouts. It is assumed that an ILC scheme is implemented via a remote control system and that packet dropout occurs during the packet transmission between the ILC controller and the actuator of remote plant. The packet dropout is viewed as a binary switching sequence which is subject to the Bernoulli distribution. In order to eliminate the effect of packet dropouts on the convergence property of output error, the hold-input scheme is adopted to compensate the packet dropout at the actuator. It is shown that the hold-input scheme with average ILC can achieve asymptotical convergence along the iteration axis for discrete time-varying linear system. Numerical examples are provided to show the effectiveness of the proposed method.

In this brief, a novel spatial iterative learning control (ILC) method is proposed for a class of high-order nonlinear motion systems in the presence of both parametric and nonparametric uncertainties. Since the system performs the tracking task iteratively in the spatial domain and the uncertainties are functions of spatial coordinates, the traditional ILC that updates the control signals in the time domain is not applicable. The proposed spatial ILC aims at improving the system tracking performance by making full use of the spatial periodicity. First, the dynamics of the motion systems are converted from the temporal domain to the spatial domain via feedback linearization. Then, the learning law is designed to tackle the system uncertainties. By utilizing a newly defined space-based barrier composite energy function, the boundedness and the convergence of state tracking error are ensured rigorously. In the end, an illustrative example is shown to demonstrate the efficacy of the proposed ILC scheme.

This paper proposes a novel Iterative Learning Control (ILC) framework for spatial tracking. Spatial tracking means that the temporal component is not fixed which violates the standing assumption on time intervals in traditional ILC. The proposed framework allows for the length of the time interval to change with each iteration. To relate the spatial information from the past to the present iteration, the concept of spatial projection is proposed. A class of nonlinear uncertain systems with input saturation is chosen for demonstration. An a appropriate ILC control law, exploiting the spatial projection idea, is proposed and the corresponding convergence analysis, based on the Composite Energy Function, is carried out. It is shown that spatial tracking is achieved under appropriate assumptions related to spatial projection and provided that the desired trajectory is realizable within the saturation bound. Finally, simulation results illustrate the predicted convergence.

In this paper, a novel iterative learning control (ILC) algorithm is presented for a class of joint position constrained robot manipulator systems. Unlike the traditional ILC probelms, where the reference trajectory is iteration invariant, the reference trajectory in this work can be non-repetitive over the iteration domain. A tan-type time-varying Barrier Lyapunov Function (BLF) is proposed to deal with the constraint requirements which can be both time and iteration varying. We show that under the proposed ILC scheme, uniform convergence of the full state tracking error beyond a small time interval in each iteration can be guaranteed over the iteration domain, while the constraints on the joint position vector will not be violated during operation. An illustrative example is presented in the end to demonstrate the effectiveness of the proposed control scheme.

In this chapter we give an overview of the field of iterative learning control (ILC). We begin with a detailed description of the ILC technique, followed by two illustrative examples that give a flavor of the nature of ILC algorithms and their performance. This is followed by a topical classification of some of the literature of ILC and a discussion of the connection between ILC and other common control paradigms, including conventional feedback control, optimal control, adaptive control, and intelligent control. Next, we give a summary of the major algorithms, results, and applications of ILC given in the literature. This discussion also considers some emerging research topics in ILC. As an example of some of the new directions in ILC theory, we present some of our recent results that show how ILC can be used to force a desired periodic motion in an initially non-repetitive process: a gas-metal arc welding system. The chapter concludes with summary comments on the past, present, and future of ILC.

This paper utilizes the improved particle swarm optimization (IPSO) technique for adjusting the gains of PID controller, iterative learning control (ILC) and the bandwidth of Butterworth filter of the ILC. The conventional ILC learning process has the potential to excite rich frequency contents and to learn the error signals. However the learnable and unlearnable error signals should be separated for b ettering control process along with the repetitions. Since producing a high frequency error condition should be avoided before the phase margin cause any trouble. Learnable error signals through a bandwidth tuning mechanism should be adaptively injected into learning control laws and thus reduce the tracking error effectively at every repetition. The filter bandwidth should be changed at every repetition for the shape of compensated errors at frequency response thinking. Thus adaptive bandwidth in the ILC controller with the aid of IPSO tuning is proposed here. Simulation results show the new controller can cancel the errors efficiently as the process is repeated. The correlation coefficient that validates the learnable compensated error signal for the trajectory is adaptively decomposed from previous error history via the bandwidth tuning mechanism in the next repetition. The learnable error signals of the intrinsic mode functions through the empirical mode decomposition correlate efficiently with reduced tracking error with further repetitions. Simulation results validate the effectiveness of the IPSO-ILC for precision motion control of a robot arm link performing high speed maneuvering or computer numerical control commanding axis positioning.

When robots are used to automate repetitive production tasks, the productivity of the manufacturing system crucially depends on the robot's task execution speed. An out-of-the-box solution is typically slow, whereas achieving shorter cycle times typically requires large efforts with respect to controller design and tuning. This dilemma can be resolved by learning control algorithms that autonomously improve performance without requiring any system-specific tuning. In the present work, we propose a novel learning control scheme that autonomously reduces the execution times of robotic systems that perform repetitive manufacturing tasks. To this end, we combine an Iterative Learning Control (ILC) approach with a trial-varying reference adaptation. The reference trajectory is slowly adapted to ensure that the given task is performed successfully on every single iteration without constraint violations. Therefore, the learning process can be carried out during operation. We validate the practical applicability of the method by real-world experiments on a 6-axis robot that performs a linear motion and a contact-force task. Despite the fundamentally different characteristics of these two tasks, the proposed algorithm achieves a remarkable reduction of cycle times, namely, by a factor of 4 in the linear motion task and a factor of 10 in the contact-force task. These results provide an important step toward robotic manufacturing systems that autonomously optimize their own performance during operation.

An estimation-based iterative learning control (ILC) algorithm is applied to a realistic industrial manipulator model. By measuring the acceleration of the end-effector, the arm angular position accuracy is improved when the measurements are fused with motor angle observations. The estimation problem is formulated in a Bayesian estimation framework where three solutions are proposed: one using the extended Kalman filter (EKF), one using the unscented Kalman filter (UKF), and one using the particle filter (PF). The estimates are used in an ILC method to improve the accuracy for following a given reference trajectory. Since the ILC algorithm is repetitive no computational restrictions on the methods apply explicitly. In an extensive Monte Carlo simulation study it is shown that the PF method outperforms the other methods and that the ILC control law is substantially improved using the PF estimate.

In this article, an adaptive robust iterative learning control is developed to solve the trajectory tracking problem of a parallel Delta robot performing repetitive tasks and subjected to external disturbances. The proposed control scheme is composed of an adaptive proportional–derivative controller to increase the convergence rate, a proportional–derivative-type iterative learning control to enhance the tracking performances through the repetitive trajectory as well as a robust term to compensate the repetitive and nonrepetitive disturbances. The practical assumption of alignment condition is introduced instead of the classical assumption of resetting conditions. The asymptotic convergence is proved using Lyaponuv analysis, and it is shown that the tracking error decreases through the iterations. Simulation and experiments are performed on a Delta robot to demonstrate the effectiveness and the superiority of the proposed controller over the traditional iterative learning control.

A new iterative learning control (ILC) approach combined with an open-closed-loop PD scheme is presented for a flexible manipulator with a repeatable motion task in the case that only the endpoint pose of the flexible link is measurable. This approach takes advantage of the fact that the ILC performance is independent of the model used, thereby overcoming the drawback of the heavy reliance of PD controllers on the modeling accuracy. The open-closed-loop PD controller is mainly used to simultaneously reduce the effects of the modeling error and disturbances to enhance the controller's robustness. Meanwhile, an angular correction term is introduced by using the angular relationship of the system outputs to reward or penalize the ILC law. Specifically, when the current output tends toward the expected trajectory, the ILC law is enhanced accordingly; otherwise, it is penalized. The convergence conditions for the proposed approach are obtained through theoretical analysis, and experiments using a real flexible manipulator are presented. The results show that the proposed ILC scheme can overcome the impact of the endpoint error caused by link flexibility and has a good control effect.