Iterative Learning Control Method Research Articles

Multivariable Iterative Learning Control Design for Precision Control of Flexible Feed Drives.

Advancements in machining technology demand higher speeds and precision, necessitating improved control systems in equipment like CNC machine tools. Due to lead errors, structural vibrations, and thermal deformation, commercial CNC controllers commonly use rotary encoders in the motor side to close the position loop, aiming to prevent insufficient stability and premature wear and damage of components. This paper introduces a multivariable iterative learning control (MILC) method tailored for flexible feed drive systems, focusing on enhancing dynamic positioning accuracy. The MILC employs error data from both the motor and table sides, enhancing precision by injecting compensation commands into both the reference trajectory and control command through a norm-optimization process. This method effectively mitigates conflicts between feedback control (FBC) and traditional iterative learning control (ILC) in flexible structures, achieving smaller tracking errors in the table side. The performance and efficacy of the MILC system are experimentally validated on an industrial biaxial CNC machine tool, demonstrating its potential for precision control in modern machining equipment.

Adaptive neural network iterative learning control of long-stroke hybrid robots with initial errors and full state constraints

Research on initial errors and constraint restrictions is one of the main research directions in the field of control of uncertain robotic systems. An adaptive iterative learning control (AILC) method based on radial basis function (RBF) neural network is proposed to address the trajectory tracking problem of the long-stroke hybrid robot system with random initial errors and full state constraints. The RBF neural network is used to approximate the unknown nonlinear terms, and the network weights are updated using an iterative learning law that incorporates a projection mechanism. Additionally, a robust learning strategy is used to compensate for both the approximation error of the neural network and the external disturbances that vary with each iteration. To relax the requirement of traditional iterative learning control (ILC) for identical initial condition, an equivalent error function is constructed based on the time-varying boundary layer. The tangent-type barrier Lyapunov function (BLF) is designed to ensure that the joint position and speed of the robot system are bounded within a predetermined range. Through stability analysis based on barrier composite energy function (BCEF), it can be proved that the boundedness of all signals in the closed-loop system and the tracking error of the robot system will converge to an adjustable residual set asymptotically. Finally, through simulation experiments conducted on the MATLAB platform, the results demonstrate that the method overcomes the random initial errors of the system effectively, ensures that the system satisfies the full-state constraints, and realizes high-precision trajectory tracking.

Iterative learning control with inverse model updated with quadratic optimization

This paper discusses a new iterative learning control method which employs inverse model of the system updated every iteration with quadratic optimization. The proposed method constructs an inverse model without estimating forward model, and the inverse model is described by finite impulse response model. These points enable the proposed method to guarantee the convergence and monotonic improvement of the inverse model. A numerical example and a practical experiment with a motor are shown to demonstrate the effectiveness of the proposed method.

Iterative learning control of an Euler-Bernoulli beam with time-varying boundary disturbance

This paper investigates the stabilization of Euler-Bernoulli beam systems modeled by a partial differential equation (PDE) with unknown time-varying disturbance. An iterative learning controller is designed using only boundary state feedback to realize the vibration control subject to unknown boundary disturbance. The well-posedness for the closed-loop system is given by the operator semigroup theory. Furthermore, the exponentially stable for the closed-loop system is proved by the Lyapunov method. The comparisons with existing results are made to demonstrate the effectiveness and advantages of the proposed boundary iterative learning control method.

Finite-time model-free robust synchronous control of multi-lift overhead cranes based on iterative learning

A model-free control method based on iterative learning law combined with adaptive super-twisting is proposed to realize the synchronous coordination control of multi-lift overhead crane system for the problems of inaccurate modeling, system parameter variation, and disturbance uncertainty that exist in multi-lift overhead crane system. First, a load-coupling model of the double-container overhead crane considering the deformation tangential force in the interlocking mode is established. Second, a time-varying sliding mode surface (TSMC) designed using nonlinear functions effectively improves the convergence speed of the system state. The method of iterative learning control is introduced to compensate the system dynamics to achieve model-free control, and the dynamic iterative learning control (DILC) is designed to improve the convergence speed of the error of the system and the steady-state performance. To suppress uncertainty disturbances and avoid control gain overestimation, an adaptive gain is added to the generalized super-twisting algorithm, which has the advantages of both finite-time convergence and chattering suppression, and improves the robustness and tracking performance of the multi-lift overhead crane system. The stability of the controlled system is analyzed using Lyapunov stability theory. The simulation experiments illustrate the effectiveness of the proposed synchronization control scheme.

Event-triggered based predictive iterative learning control with random packet loss compensation for nonlinear networked systems

In this paper, a novel event-triggered predictive iterative learning control (ET-PILC) method with random packet loss compensation (RPLC) mechanism is proposed for unknown nonlinear networked systems with random packet loss (RPL). First, a new RPLC mechanism is designed by utilizing both the historical and predictive data information to avoid the deterioration of control performance due to RPL. Then, a new event-triggered condition is designed based on the proposed RPLC mechanism to save communication resources and reduce computational burden. Moreover, the convergence of the modeling error and tracking control error are analyzed theoretically, and simulation results are given to demonstrate the effectiveness of the proposed method further.

Development of a Hydraulic Actuator for MRI- and Radiation-Compatible Medical Applications

This paper presents methods for the actuation, measurement, and control of a magnetic resonance imaging- and radiation-compatible single-axis translatory actuation system. As an exemplary demanding use case, the axis is developed for a robotic phantom for evaluating emitted radiation doses of radiotherapy devices. For this, the robot has to follow given three-dimensional trajectories of patients’ movements with an accuracy of 200 µm. For enabling use of magnetic resonance imaging, actuation of the robot is realized by hydraulic transmission without any metal parts or electrical components at the imaging side. The hydraulic axis is developed, built-up, and tested. In order to compensate for deviations from the targeted actuation trajectory resulting from tolerances, friction, and non-linearities in the system, a combination of photogrammetric measurement and iterative learning control is applied. The developed photogrammetric system is capable of determining the robot’s position with systematic errors of 35 µm and stochastic errors of 0.3 µm. Different types of iterative learning control methods are applied, parameterized, and tested. With this, the hydraulically actuated axis is able to follow given trajectories with maximum errors below 130 µm.

PD-Based Iterative Learning Control for the Nonlinear Low-Speed-Jitter Vibration of a Wind Turbine in Yaw Motion

Aiming at the nonlinear low-speed-jitter (LSJ) vibration suppression for a yaw system of a megawatt wind turbine, a kinematics mechanism of the yaw system is investigated from the perspective of tribology, and a kinematics model of the yaw system based on an equilibrium position is established. On the basis of the dynamic modeling of the yaw system, a nonlinear mathematical model of the LSJ system is deduced. Based on the two lead motors’ driving of the conventional yaw motion, an innovative design with a special installation of two auxiliary motors for yaw transmission is carried out, which is integrated with a matching centralized lubrication system (CLS). Based on open-loop proportional-derivative (PD) control and the iterative learning control methods of the time-varying continuous system, the stability control and jitter amplitude suppression of the yaw system are realized by using a combined driving torque provided by the lead and auxiliary gears. From the stability and convergence of the time-domain response and the convergence of the iterative error, the effectiveness of the iterative learning control method with the PD-based regulation is verified, and its advantages for engineering applications are shown based on the algorithm solver improvement. The feasibility of the physical realization and engineering application of the control methodology is verified by using controller-hardware-in-the-loop (C-HITL) simulation technology.

Sampling-based event-triggered iterative learning control in nonlinear hyperbolic distributed parameter systems

This article explores a sampling-based event-triggered iterative learning control (SETILC) method for achieving precise control in nonlinear hyperbolic distributed parameter systems. Firstly, by designing a Lyapunov function regarding the system output error, reasonable triggering conditions are derived to selectively update the system control input. Subsequently, a sampling-based event-triggered iterative learning control algorithm is proposed to further enhance the control performance of the system while avoiding the occurrence of Zeno phenomena. A condition sufficient for ensuring the convergence of the system’s tracking error to zero is presented, and its convergence is proven. Finally, the proposed method’s effectiveness is verified through numerical simulations.

Precise control of magnetic soft microrobot in flowing environment

Microrobots have demonstrated attractive advantages for biomedical use, ranging from cargo delivery to invasive therapies. However, it remains a challenge to develop a universal control method for various microrobots to guarantee the precision of microrobot-based executions and further spread their applications. Herein, we propose an iterative learning control (ILC) method that can achieve precise position control for microrobots in a bionic flowing environment. Although the kinematics system of the microrobots and flowing environment has severe nonlinearities and uncertainties, the model- free control method, ILC, can optimize the input gradually to accurately accomplish microrobots’ arrangements, including position control and drug release. Oil-droplet-based magnetic soft microrobots are utilized as control objects to prove the advantages of the proposed control method. Two electro- magnets are set up as actuation devices, and a microchannel with a flowing medium mimics the vessel environment. By rigorous mathematical analysis and numerous experiments, the proposed method is verified to be effective, and the results show that the microrobots can be precisely controlled to the desired position in the flowing environment. These results pave a novel and accessible path to achieve precise position control for various microrobots.

Robust adaptive iterative learning control for high-speed trains under non-strictly repeated conditions

In this paper, the speed and position control of high-speed trains (HSTs) under non-strictly repeated conditions is investigated. Considering the modeling accuracy and control cost, a multi-particle dynamics model is established in a single coordinate system. Based on this model, a novel robust adaptive iterative learning control (RAILC) method is proposed. The Lyapunov function is established to prove that the system state converges in a finite time, and the composite energy function (CEF) is constructed to prove that the control error approaches zero along the iteration axis. The application value in engineering practice of the proposed RAILC is verified by two simulation tests. Existing adaptive iterative learning control (AILC) and adaptive terminal sliding mode control (ATSMC) are introduced as control groups to show the system convergence of the RAILC on the iteration axis and time axis.

Iterative learning control with parameter estimation for non-repetitive time-varying systems

This paper presents an extension method of iterative learning control (ILC) to address the applications associated with non-repetitive time-varying systems (NTVSs). Conventional ILC approaches employ fixed nominal system models, but non-repetitive time-varying models may lead to accumulated model uncertainties, which fails to satisfy the robust convergence conditions. To tackle this issue, a novel ILC algorithm with parameter estimation is proposed using back propagation neural network. This algorithm incorporates an approach that utilizes Bayesian regularization training mechanism to accurately estimate non-repetitive time-varying parameters. Through comprehensive experiment on Monolithic XY Stage, the performance of proposed algorithm is validated to demonstrate its feasibility and effectiveness while handling tasks on NTVSs.

Distributed Iterative Learning Control of Nonlinear Multiagent Systems Using Controller-Based Dynamic Linearization Method.

When applied to the consensus tracking of repetitive leader-follower multiagent systems (MASs), most of existing distributed iterative learning control (DILC) methods assume that the dynamics of agents are exactly known or up to the affine form. In this article, we study a more general case where the dynamics of agents are unknown, nonlinear, nonaffine, and heterogeneous, and the communication topologies can be iteration-varying. More specifically, we first apply the controller-based dynamic linearization method in the iteration domain to obtain a parametric learning controller using only the local input-output data collected from neighboring agents in a directed graph, and then propose a data-driven distributed adaptive iterative learning control (DAILC) method through the parameter-adaptive learning methods. We show that for each time instant, the tracking error is ultimately bounded in the iteration domain for both of the cases with iteration-invariant and iteration-varying communication topologies. The simulation results show that the proposed DAILC method has faster convergence speed, higher tracking accuracy, and more robust learning and tracking in comparison with a typical DAILC method.

Zero-Pole Cancellation Based Iterative Learning Control Method for Speed Ripple Suppression of Single Rolling Rotor Compressor

Zero-Pole Cancellation Based Iterative Learning Control Method for Speed Ripple Suppression of Single Rolling Rotor Compressor

Cross-coupling indirect iterative learning control method for batch processes with time-varying uncertainties

For batch time-varying processeswith non-repetitive disturbances, a cross-coupling indirect iterative learning control (CC-iILC) is proposed. The set trajectory of the system on the single axis is accurately tracked by an indirect iterative learning control strategy with a PI controller for its time direction and a closed-loop feedback control strategy for the batch direction. Under the asymptotically stable condition of the two-dimensional (2D) dynamic model, the optimal control law is obtained by optimizing the H-infinity control function. To avoid the contour error during coupling, the cross-coupling technique is used to distribute the contour error to each axis for compensation. The stability condition of the indirect iterative learning controller is analyzed by a two-dimensional Fornasini-Marchesini (FM) batch dynamics model and two-dimensional robust H-infinity control theory. The feasibility and superiority of the proposed control method are verified by numerical simulation and experimental tests.

A two-dimensional model predictive iterative learning control based on the set point learning strategy for batch processes

Although conventional two-dimensional model predictive iterative learning control (2D-MPILC) based on an extended non-minimum state space (ENMSS) model can avoid designing an observer, it only relies on feedback to passively deal with time delay. This passive treatment for time delay hinders the further improvement of control performance. To address this shortcoming, a two-dimensional model predictive iterative learning control strategy based on the set point learning (2D-SPL-MPILC) is proposed. Firstly, a set point learning strategy is developed to improve the ability to deal with time delay. Based on the error of the previous batch, the set point learning strategy perceives the system dynamics in advance, and then outputs this advance perception in the form of the predictive set point. Secondly, a novel ENMSS model is constructed on the basis of the predictive tracking error between the predictive set point and the actual output. Since the predictive tracking error integrates the predictive set point, this novel ENMSS model has certain predictive ability. Finally, based on this novel ENMSS model, a novel two-dimensional model predictive iterative learning control (2D-MPILC) method, which is called 2D-SPL-MPILC method, is designed. Because this controller contains the perception of future process dynamics, it can reduce the impact of time delay and improve the control performance. The case studies on the packing pressure control in injection molding process and batch reactors demonstrate the effectiveness of the presented 2D-SPL-MPILC strategy.

A novel iterative learning control method for longitudinal trajectory tracking of aerospace vehicles

AbstractConsidering the precise model information of the aerospace model is not always available under the effect of external disturbance and model uncertainty, a new data‐driven point‐to‐point iterative learning control (PTPILC) method is proposed for aerospace vehicles to track the predetermined trajectory. Firstly, the system of the aerospace vehicle is converted into a discrete form without any omission, which reflects the dynamics of the original system by input and output data. Then, the objective function of the tracking error is proposed in the quadratic form to facilitate the application of the conjugate gradient algorithm, which develops an improved data‐driven PTPILC method. And the convergence analysis of the proposed method is presented. Furthermore, the control parameters are optimized to make the proposed method more robust against different parameter deviations. Finally, the effectiveness of the proposed method is illustrated by different simulations.

Robust adaptive event‐triggered ILC scheme design for discrete‐time non‐repetitive nonlinear systems

AbstractIn this study, a robust adaptive event‐triggered iterative learning control (ET‐ILC) method is developed for discrete‐time non‐repetitive nonlinear systems with both time‐varying system parameters and time‐iteration‐varying non‐parametric uncertainty. The proposed method utilizes an iteration‐varying dead‐zone function to estimate both parametric and non‐parametric uncertainties simultaneously. Moreover, an event‐triggered control mechanism including a mixed triggering strategy is employed to minimize the unnecessary usage of communication resource. This strategy updates the controller only when the triggering condition is met, thereby reducing the frequency of controller actions. The results are also extended to a class of multiple‐input–multiple‐output (MIMO) non‐repetitive nonlinear systems. The designed robust adaptive ET‐ILC algorithm not only guarantees perfect learning convergence in the iteration domain but also contributes to saving the system resources. The effectiveness of the presented method is demonstrated by virtue of the rigorous theoretical analysis and the illustrative simulation examples.

Development of an ILC controller for the Droplet-on-Demand generation system

The droplet-on-demand systems have been increasingly attracting attention from many research groups. The droplet-on- demand system is a promising platform for many biochemical applications, especially for drug delivery applications, where the matter quantity requires very high accuracy, in microliter- scale or even in nanoliter-scale, for enhancing the efficiency, reducing the toxicity as well as the adverse effects of medicines. In order to generate microdroplets, microfluidic technology is emerged as one of the most effective methods. In this paper, we propose a droplet-on-demand generation system based on the Y-junction microfluidic structure with the generated droplet sizes are adjusted by iterative learning control (ILC) technique. The ILC controller can modify the current control input based on the error information measured during earlier experimental procedures, resulting in the desired droplet size in the proposed Y-junction microfluidic channel. The effectiveness of the ILC method was validated by experiments. The obtained results show that the proposed system can generate the required droplet size after 6 to 7 iterations. The results also prove the potential of integrating the ILC technique in the microdroplet generation system. The propose system can be extended to apply in the drug delivery systems for patients in hospitals or chemical mixing systems.

An enhanced PI controller based on adaptive iterative learning control

SummaryThis paper presents a PI‐type adaptive iterative learning control (PI‐AILC) method for nonlinear processes, which targets enhancing system tracking capabilities by adapting setpoints of the PI controller. First, the proposed method employs compact form dynamic linearization technology to obtain a local linear representation of unknown nonlinear systems. Subsequently, the iterative learning controller gain is adaptively updated using the local linear expression to ensure the optimality of the setpoints. Finally, a pre‐learning mechanism for offline data is introduced to augment the efficiency of the iterative mechanism further. The proof of strict convergence for PI‐AILC is established. Experimental results substantiate the efficacy of PI‐AILC.