Iterative Learning Control Controller Research Articles

Method for predicting the lost input of iterative learning control systems over AWGN channels

This paper researches the method for predicting the lost input of iterative learning control (ILC) systems over additive white Gaussian noise (AWGN) channels. It directly takes into account a proportional ILC controller, which guarantees the convergence of objects under ideal communication conditions. By employing prior information on ILC controllers and AWGN channels, a state space model is built. According to this model and the innovation analysis theory, a predictor is developed for actuators to calculate the input transmitted by ILC controllers but lost in AWGN channels. Compared with the iteration compensation in existing literature, the proposed prediction compensation simultaneously guarantees the convergence accuracy and speed of ILC systems over AWGN channels. Numerical simulation demonstrates the usefulness of the developed prediction method.

CBF‐guided iterative learning control performance optimization for switched systems

AbstractThis article studies the control barrier function (CBF) guided tracking performance optimization for switched systems under iterative learning control (ILC). CBF is an effective method for handling control system state safety. If the safe set is designed as expected performance, and the unsafe set is not expected, then this may be a good choice for optimizing control system performance. We propose a tracking performance optimization method based on CBF to speed up the convergence of the tracking error. A safe set is used to describe the expected tracking performance, by utilizing CBF, safety is further transformed into a set of inequality conditions that constrain ILC controller gains, which ensures that the tracking error is always within the safe set. In addition, an event‐triggering mechanism (ETM) is considered to reduce unnecessary data transmission, and sufficient conditions for exponential stability of the tracking error of switched systems and ILC controllers design are obtained. Finally, a simulation example is given to verify the feasibility of the proposed method.

Optimal state filters for networked iterative learning control systems with data losses and noises

SummaryData losses and noises in both forward and feedback channels significantly impact the convergence of networked iterative learning control (ILC) systems. To address this issue, this article considers a class of linear time‐invariant objects controlled by proportional ILC controllers, an optimal state filter is then designed at the ILC controller side that aims to guarantee the convergence of the input transmitted by ILC controllers. First, two data transmission processes are introduced to account for the effects of data losses and noises. Second, a filtering model is established utilizing only the object information and the aforementioned data transmission processes. Third, the optimal state filter is designed on the basis of the orthogonal projection principle. This filtered state facilitates the acquisition of actual output errors, thus improving the convergence of the input transmitted by ILC controllers. Simulation results demonstrate the effectiveness of the proposed state filters.

Adaptive fuzzy iterative learning control based neurostimulation system and in-silico evaluation.

Closed-loop neural stimulation has been an effective treatment for epilepsy patients. Currently, most closed-loop neural stimulation strategies are designed based on accurate neural models. However, the uncertainty and complexity of the neural system make it difficult to build an accurate neural model, which poses a significant challenge to the design of the controller. This paper proposes an Adaptive Fuzzy Iterative Learning Control (AFILC) framework for closed-loop neural stimulation, which can realize neuromodulation with no model or model uncertainty. Recognizing the periodic characteristics of neural stimulation and neuronal firing, Iterative Learning Control (ILC) is employed as the primary controller. Furthermore, a fuzzy optimization module is established to update the internal parameters of the ILC controller in real-time. This module enhances the anti-interference ability of the control system and reduces the influence of initial controller parameters on the control process. The efficacy of this strategy is evaluated using a neural computational model. The simulation results validate the capability of the AFILC strategy to suppress epileptic states. Compared with ILC-based closed-loop neurostimulation schemes, the AFILC-based neurostimulation strategy has faster convergence speed and stronger anti-interference ability. Moreover, the control algorithm is implemented based on a digital signal processor, and the hardware-in-the-loop experimental platform is implemented. The experimental results show that the control method has good control performance and computational efficiency, which provides the possibility for future application in clinical research.

Two-dimensional iterative learning control with deep reinforcement learning compensation for the non-repetitive uncertain batch processes

Iterative learning control (ILC) is an advantage control strategy widely used in batch systems. Nevertheless, designing an effective iterative learning control scheme remains crucial for complex batch systems with model mismatch and non-repetitive nature. In this paper, we propose a two-dimensional iterative learning control-reinforcement learning (2D ILC-RL) control scheme composed of a two-dimensional ILC controller and a two-dimensional DRL compensator. Based on the 2D system theory, the 2D ILC controller is proposed to ensure the primary control performance and its stability and convergence are verified. Meanwhile, the DRL compensator counteracts the negative impact of the model mismatch and the non-repetitive nature. In addition, we proposed a real-time implementation scheme to guarantee the safety of the practical batch systems compared to the conventional online training method. Finally, the simulation results in the injection molding batch process and the nonlinear continuously stirred tank reactor demonstrate the proposed control scheme’s effectiveness, significant control performance, and strong robustness.

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.

Input predictors for networked iterative learning control systems with data dropouts and time delays

Hold-up compensation decelerates the convergence of iterative learning control (ILC) systems with data dropouts and time delays. Only depending on the prior knowledge of both ILC controllers and transmission channels, this paper develops a predictor to calculate the input not received on time due to data dropouts and time delays. First, a controller adopting the proportional learning strategy is considered directly, which is appropriate for objects in ideal communication conditions. After that, two data-receiving equations are given to describe the effect of data dropouts and one-step time delays. Finally, a predictor is designed according to the innovation analysis approach. Since the prediction uses all historical input at the identical time index in previous iterations, the predicted input is more approximate to the one not received on time than the input held up for compensation. Simulation results show the object with prediction compensation tracks the expected trajectory faster than that with input-hold compensation.

Frequency Response Function-Based Learning Control: Analysis and Design for Finite-Time Convergence

Iterative learning control (ILC) enables substantial performance improvement by using past operational data in combination with approximate plant models. The aim of this article is to develop an ILC framework based on nonparametric frequency response function (FRF) models that requires very limited modeling effort. These FRF models describe the behavior of a system in periodic steady state, yet are employed for the control of arbitrary finite-length tasks. A detailed analysis and design framework is developed to construct noncausal learning filters directly from uncertain FRF models, that achieve ILC convergence for arbitrary tasks. The resulting framework provides a unification between ILC and iterative inversion-based control, where the latter is a learning method specifically developed for periodic tasks.

Stability of Sliding Mode ILC Design for a Class of Nonlinear Systems With Unknown Control Input Delay.

This article studied the stability and convergence of a robust iterative learning control (ILC) design for a class of nonlinear systems with unknown control input delay. First, the iterative integral sliding mode (IISM) design was proposed, which comprised iterative actions. The iterative action made the convergence of the tracking error under the ideal sliding mode. Then, a suitable iterative update law was provided for the IISM-based robust ILC controller. The controller had the capability of both minimizing the steady tracking error and suppressing the unrepeatable disturbance. Using the controller, the closed-loop system stability was analyzed, and the stability conditions were given. Consequently, the sliding mode convergence in the iteration domain was proved by a composite energy function (CEF). In addition, by analyzing the influence of affection on the tracking error, several measures were taken to solve the chattering problem of the sliding mode control. Finally, a one-link robotic manipulator and a vertical three-tank system were used to verify the control design. The application simulations validated the performance of the proposed sliding mode iterative learning control (SMILC) design, which achieved the stability of the nonlinear system and overcame the control input time delay.

Implementation of Iterative Learning Control on a Pneumatic Actuator

Pneumatic actuators demonstrate various nonlinear and uncertain behavior, and as a result, precise control of such actuators with model-based control schemes is challenging. The Iterative Learning Control (ILC) algorithm is a model-free control method usually used for repetitive processes. The ILC uses information from previous repetitions to learn about a system’s dynamics for generating a more suitable control signal. In this paper, an ILC method to overcome the nonlinearities and uncertainties in a pneumatic cylinder-piston actuator is suggested. The actuator is modeled using MATLAB SimScape blocks, and the ILC scheme has been expanded for controlling nonlinear, non-repetitive systems so that it can be used to control the considered pneumatic system. The simulation results show that the designed ILC controller is capable of tracking a non-repetitive reference signal and can overcome the internal and payload uncertainties with the precision of 0.002 m. Therefore, the ILC can be considered as an approach for controlling the pneumatic actuators, which is challenging to obtain their mathematical modeling.

A unified adaptive control approach of nonlinear continuous non‐parameterized systems with asymmetric control gains for trajectory tracking in different domains

AbstractIn this article, for nonlinear continuous non‐parameterized (NCNP) systems, an adaptive iterative learning control (ILC) algorithm, which can adjust the adaptive parameters in both iteration‐domain and time‐domain, is first proposed to track different reference trajectories repetitively over a finite time interval. As the NCNP system is required to asymptotically track reference trajectory in infinite time‐domain, by virtue of partitioning the reference trajectory and system signals with a fixed time interval, the proposed adaptive ILC controller is then extended to handle the asymptotic tracking issue in infinite time‐domain. Therefore, a unified adaptive control approach is practically presented for NCNP systems to track reference trajectories in different domains (the infinite iteration‐domain and the infinite time‐domain). A prominent feature of the unified adaptive control approach is that the unknown control gain matrices in the NCNP systems are assumed to be invertible only. As a result, the general requirement in conventional adaptive control and adaptive ILC that the control gain matrices of plants are real symmetric and positive‐definite (or negative‐definite) is greatly relaxed. In addition, only three adaptive variables are designed for adjusting or updating in the proposed unified adaptive control approach such that the structure of controller is very simple and the memory space for computing is saved.

Learning of Iterative Learning Control for Flexible Manufacturing of Batch Processes

Flexible manufacturing as an essential component of smart manufacturing implements the customized production mode, thereby requesting fast controller adaptation for producing different goods but still with high precision. This problem becomes even more acute for batch processes. Here we present a solution called learning of iterative learning control (ILC) based on neural networks. It is able to recommend control parameters for ILC controllers accordingly, so as to yield fast tracking error convergence and smaller steady-state error for disparate set-point profiles, which is deemed an abstraction of different production needs. The method substantially outperforms a benchmark ILC on a variety of systems and cases, thereby showing its potential for deployment in the industrial Internet of Things.

Iterative learning control for intermittently sampled data: Monotonic convergence, design, and applications

The standard assumption that a measurement signal is available at each sample in iterative learning control (ILC) is not always justified, e.g., when exploiting time-stamped data from incremental encoders or in systems with data dropouts. The aim of this paper is to develop a computationally tractable ILC framework that is capable of exploiting intermittent data while maintaining favourable properties, including monotonic convergence. A controllability and observability analysis of the intermittent ILC framework leads to appropriate monotonic convergence conditions which allow for missing data. These conditions lead to a new explicit ILC controller design independent of the sampling instances, which is reminiscent of gradient-descent ILC. The approach is demonstrated on both an intuitive example and a practically relevant example which exploits time-varying timestamped data from an incremental encoder.

Towards a process fault-tolerant iterative learning control for dynamic systems*

The paper presents further work on combining Iterative Learning Control (ILC) with Fault-Tolerant Control. Main goals here are to drive the considered plant to the prescribed reference with simultaneous elimination of the effect of possible process faults. The first result presented is about designing the fault estimator and linking it with the original plant. Next, for a such an extended system the ILC scheme is provided. Since the system is occupied by the process and measurement uncertainties, the fault observer along with the ILC controller are designed under the H∞ regime. The proposed control strategy is verified with application to the DC servo motor.

On Robustness in Optimization-Based Constrained Iterative Learning Control

Iterative learning control (ILC) is a control strategy for repetitive tasks wherein information from previous runs is leveraged to improve future performance. Optimization-based ILC (OB-ILC) is a powerful design framework for constrained ILC where measurements from the process are integrated into an optimization algorithm to provide robustness against noise and modelling error. This paper proposes a robust ILC controller for constrained linear processes based on the forward-backward splitting algorithm. It demonstrates how structured uncertainty information can be leveraged to ensure constraint satisfaction and provides a rigorous stability analysis in the iteration domain by combining concepts from monotone operator theory and robust control. Numerical simulations of a precision motion stage support the theoretical results.

A comparative study between feedforward control and iterative learning control for trajectory tracking of Delta robot

This paper presents a comparative study between feedforward control (FF) and iterative learning control (ILC) with application to a parallel Delta robot performing repetitive trajectory. In order to improve the tracking trajectory of the Delta robot, a model-based feedforward compensation combined with the proportional derivative (PD) controller is introduced. As the Delta robot is affected by important frictions that are not taken into account in the dynamic model, the performance of the FF can be degraded considerably. To overcome these issues, a model-free control represented by the PD-type ILC controller is used instead the FF compensation. Experimental results show that the two strategies can ensure good tracking performance with better accuracy of ILC.

Iterative learning control of air pressure variation inside a high-speed train under the excitation of the tunnel pressure wave with a fixed-morphologic form

Based on the characteristics that the tunnel pressure wave has a fixed-morphologic form when the same train passes through the same tunnel, an applicational approach based on the iterative learning control (ILC) is developed, aiming at overcoming the drawbacks of the traditional strategy for controlling the air pressure variation inside a high-speed train carriage. To achieve the goal, the control system is mathematically modelled. Then, the problem is formulated. The task of suppressing the influence of the tunnel pressure wave on the air pressure inside the carriages is shifted as an ILC problem of tracking the comfort index with varying trial length. The algorithm of refreshing the control signal from trial to trial is determined and the process of ILC control is designed. Next, the convergence of the newly-developed applicational ILC algorithm is discussed and the algorithm is simulated by the simulation signal and field-test signal. Results show that the applicational ILC algorithm be more adaptable in handling the control of the air pressure inside carriage under the excitation of varying-amplitude, varying-scale and varying-initial-states tunnel pressure wave. Meanwhile, the matching with tunnel pressure wave makes the applicational ILC algorithm will take both the riding comfort and fresh air into consideration, which upgrades the performances when the high-speed train passing through long tunnels.

Robust static output feedback based iterative learning control design with a finite‐frequency‐range two‐dimensional specification for batch processes subject to nonrepetitive disturbances

AbstractFor industrial batch processes subject to time‐varying uncertainties and nonrepetitive disturbances, this article proposes a robust static output feedback (SOF) based iterative learning control (ILC) design with a finite‐frequency‐range two‐dimensional (2D) performance specification. An important advantage of the proposed design lies in its simple structure and easy implementation, with no need to store the state information of the learning controller as used in the existing 2D dynamic output feedback based ILC methods. Based on a 2D Roesser system setting, sufficient conditions are established in terms of nonlinear matrix inequalities to guarantee robust stability of the resulting closed‐loop ILC systems along both the time and batch directions, by using the matrix dilatation technique. Moreover, a finite‐frequency‐range 2D performance specification for attenuating nonrepetitive disturbances is introduced for control synthesis. To derive the feasible SOF based ILC controller gains, a two‐stage heuristic approach is developed for iterative computation based on predesigning the corresponding state feedback based ILC gains. An illustrative example is used to demonstrate the effectiveness and merits of the proposed method.

Convergence Analysis of Iterative Learning Control for Two Classes of 2-D Linear Discrete Fornasini–Marchesini Model

This paper first investigates convergent property of two iterative learning control (ILC) laws for two kinds of two-dimensional linear discrete systems described by the first Fornasini–Marchesini model (2-D LDFFM with a direct transmission from inputs to outputs and 2-D LDFFM with input delay). Different from existing ILC results for 2-D LDFFM, this paper provides convergence analysis in a three-dimensional (3-D) framework. By using row scanning approach (RSA) or column scanning approach (CSA), it is theoretically proved no matter which method is adopted, perfect tracking on the desired reference surface is accomplished. In addition, linear matrix inequality (LMI) technique is utilized to computer the learning gain of the ILC controller. The effectiveness and feasibility of the designed ILC law are illustrated through numerical simulation on a practical thermal process.

Output Feedback Based Iterative Learning Control with Finite Frequency Range Specifications via a Heuristic Approach for Batch Processes with Polytopic Uncertainties

For robust control and iterative optimization of industrial batch processes with polytopic uncertainties, this paper proposes a robust output feedback based iterative learning control (ILC) design in terms of finite frequency range stability specifications. Robust stability conditions for the closed-loop ILC system along both time and batch directions are first established based on the generalized Kalman-Yakubovich-Popov lemma and linear repetitive system theory. To facilitate the ILC controller design with respect to process uncertainties described in a polytopic form, extended sufficient conditions for the system stability are then derived in terms of matrix inequalities. Correspondingly, a two-stage heuristic approach is developed to iteratively compute feasible ILC controller gains for implementation. An illustrative example is given to demonstrate the effectiveness of the proposed control design.