Iterative Learning Control Controller Research Articles

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.

Iterative learning control-based adaptive beam loading compensation implementations in the J-PARC LINAC

Abstract The Japan Proton Accelerator Research Complex (J-PARC) is a multi-purpose high-intensity proton accelerator facility that consists of a 400 MeV linear accelerator (LINAC), a 3 GeV rapid-cycling synchrotron (RCS), a 30 GeV main ring synchrotron (MR), and experimental facilities. In 2018, to achieve the goal of a 1 MW beam power at the RCS, the beam current of the LINAC was increased from 30 to 50 mA. Based on the beam loading effect, such a strong beam current can cause a significant drop in the accelerating gradients. Although both feedback and normal static feedforward control schemes were used in the low-level radio frequency (LLRF) system to suppress the beam loading effect, the peak-to-peak stability of the RF field still does not meet the requirements of the LINAC (i.e., ± 0.5% amplitude and ± 0 . 5 ° phase). To solve this problem, an iterative learning control (ILC) scheme was studied and implemented in the J-PARC LINAC. The beam loading compensation experiments demonstrate that the inclusion of the ILC controller improves the performance of the control system significantly. As the number of iterations increase, the tracking error of the system decreases monotonously. For the accelerating field with beam operation, compared to the performance with static feedforward control, the peak-to-peak stability of amplitude improves from greater than ± 1% to less than ± 0.4%, and the peak-to-peak stability of phase improves from ± 1°to ± 0 . 2 ° .

Iterative Learning Control Design of Switched Systems With Markovian Jump Parameters via Fuzzy Approach

In this paper, based on Takagi-Sugeno (T-S) approach the work is concerned with the iterative learning control (ILC) problem for a class of switched systems with data packet dropouts. The designed scheme is described by a class of Markovian switching systems with transition probabilities which are time-variant in a network environment. The links of network communications between controller-to-actuator (C/A) and sensor-to-controller (S/C) are unreliable. In terms of the construction of two-dimensional (2D) T-S model, a novel composite strategy of 2D fuzzy iterative learning output feedback control is proposed. The sufficient conditions on stochastic stability are obtained by the 2D Lyapunov stability theory. Furthermore, the dynamics of the closed-loop are guaranteed to be stochastically stable and the desired $H_{\infty }$ performance is also provided. The solutions of the ILC controller are derived by the application of the cone complementarity linearisation (CCL) procedure. Finally, a numerical simulation is illustrated to show the validity of the design.

Novel Fuzzy PID-Type Iterative Learning Control for Quadrotor UAV.

Due to the under-actuated and strong coupling characteristics of quadrotor aircraft, traditional trajectory tracking methods have low control precision, and poor anti-interference ability. A novel fuzzy proportional-interactive-derivative (PID)-type iterative learning control (ILC) was designed for a quadrotor unmanned aerial vehicle (UAV). The control method combined PID-ILC control and fuzzy control, so it inherited the robustness to disturbances and system model uncertainties of the ILC control. A new control law based on the PID-ILC algorithm was introduced to solve the problem of chattering caused by an external disturbance in the ILC control alone. Fuzzy control was used to set the PID parameters of three learning gain matrices to restrain the influence of uncertain factors on the system and improve the control precision. The system stability with the new design was verified using Lyapunov stability theory. The Gazebo simulation showed that the proposed design method creates effective ILC controllers for quadrotor aircraft.

Improved ADRC With ILC Control of a CCD-Based Tracking Loop for Fast Steering Mirror System

In order to improve the tracking accuracy of the fine tracking system of free-space optical communications, a proposed active disturbance rejection control (ADRC) with iterative learning control (ILC) strategy is designed to regulate the fast steering mirror (FSM) system. First, based on the open-loop frequency response results of the system, we can approximately confirm the system transfer function and prepare for the design of the controller. Then, an improved ADRC that using sliding mode control instead of the traditional nonlinear state error feedback method is proposed to attenuate the influence of the unknown and unmolded parts. In addition, the ILC method is used to compensate the hysteresis of piezoelectric actuators and improve the overall system performance. The experimental results indicate that the tracking accuracy of the system is 1 μrad at 1k sampling frequency. In addition, the recommended method shows the better disturbance suppression performance than traditional ADRC in simulation and experimental investigations. This method has a very high application value in the field of visual axis stability, adaptive optics, and other fields.

Improved control strategy for power quality enhancement in standalone systems based on four‐leg voltage source inverters

Standalone power supplies using renewable energy resources become a key solution to address both energy consumption and environmental restrictions in remote locations. These standalone systems are weak low-voltage networks, wherein the presence of heavy non-linear and/or unbalanced loads may adversely affect the output voltage quality. Accordingly, four-leg voltage-source inverters play an important role by providing both balanced and unbalanced loads with clean power and balanced output voltage. The reported work proposes a sliding mode control (SMC) along with an iterative learning control (ILC) for harmonic compensation in the presence of critical load conditions such as unbalanced non-linear loads. Indeed, under substantial disturbances, the ILC harmonic compensator smoothly adjusts the input control signal of the SM controller in such a manner to properly reject the disturbing harmonics. The design methodology of the proposed SMC–ILC control scheme is detailed and its effectiveness is validated through simulation and experimental results.

Eigenspectrum-Based Iterative Learning Control for a Class of Distributed Parameter System

In many practical distributed parameter systems (DPSs), the states of the systems are required to track a desired trajectory repeatedly over a finite duration. It is hard to design the controllers for the systems to obtain uniformity and repetitiveness of the states simultaneously. In this paper, iterative learning control (ILC) based on the eigenspectrum of the system is proposed for a class of parabolic DPS to tackle this control problem. First, Galerkin's method with the eigenspectrum as the base is applied to the original system to obtain a reduced-order model which exhibits the dominant dynamics of the system. Then, an ILC controller based on the reduced-order model is developed. The stability and convergence of the proposed approach are analyzed and guaranteed under the framework of functional analysis. Finally, simulations on the temperature profile control of a rapid thermal processing system are performed to demonstrate the effectiveness of the developed ILC scheme.

Unbalance Compensation of a Full Scale Test Rig Designed for HTR-10GT: A Frequency-Domain Approach Based on Iterative Learning Control

Unbalance vibrations are crucial problems in heavy rotational machinery, especially for the systems with high operation speed, like turbine machinery. For the program of 10 MW High Temperature gas-cooled Reactor with direct Gas-Turbine cycle (HTR-10GT), the rated operation speed of the turbine system is 15000 RPM which is beyond the second bending frequency. In that case, even a small residual mass will lead to large unbalance vibrations. Thus, it is of great significance to study balancing methods for the system. As the turbine rotor is designed to be suspended by active magnetic bearings (AMBs), unbalance compensation could be achieved by adequate control strategies. In the paper, unbalance compensation for the Multi-Input and Multi-Output (MIMO) active magnetic bearing (AMB) system using frequency-domain iterative learning control (ILC) is analyzed. Based on the analysis, an ILC controller for unbalance compensation of the full scale test rig, which is designed for the rotor and AMBs in HTR-10GT, is designed. Simulation results are reported which show the efficiency of the ILC controller for attenuating the unbalance vibration of the full scale test rig. This research can offer valuable design criterion for unbalance compensation of the turbine machinery in HTR-10GT.

Robust iterative learning control for batch processes with input delay subject to time‐varying uncertainties

A robust iterative learning control (ILC) method is proposed for industrial batch processes with input delay subject to time-varying uncertainties, based on a two-dimensional (2D) system description of batch process operation. To compensate the input delay, a 2D state predictor is established to predict the augmented system states, such that a 2D ILC design is developed for the ‘delay-free’ 2D system based on using only the measured output errors of current and previous cycles. Delay-dependent stability conditions for the resulting 2D system are established in terms of matrix inequalities by defining a comprehensive 2D Lyapunov–Krasovskii functional candidate along with free-weighting matrices. By solving these matrix inequalities using a cone complementarity linearisation method, the ILC controller is explicitly derived together with an adjustable H infinity performance index. An important merit is that perfect tracking can be realised for a batch process with arbitrarily long input delay if the delay-free part of the 2D system can be stabilised, in no presence of time-varying uncertainties. Moreover, the time integral of tracking error can be added as an extended 2D system state for ILC design to eliminate steady-state output error for all batches. An illustrative example of injection moulding process is given to demonstrate the effectiveness of the proposed method.

Inferential Iterative Learning Control: A 2D-system approach

Certain control applications require that performance variables are explicitly distinguished from measured variables. The performance variables are not available for real-time feedback. Instead, they are often available after a task. This enables the application of batch-to-batch control strategies such as Iterative Learning Control (ILC) to the performance variables. The aim of this paper is first to show that the pre-existing ILC controllers may not be directly implementable in this setting, and second to develop a new approach that enables the use of different variables for feedback and batch-to-batch control. The analysis reveals that by using pre-existing ILC methods, the ILC and feedback controllers may not be stable in an inferential setting. Therefore, the complete closed-loop system is cast in a 2D framework to analyze stability. Several solution strategies are outlined. The analysis is illustrated through an application example in a printing system. Finally, the developed theory also leads to new results for traditional ILC algorithms in the common situation where the feedback controller contains a pure integrator.

Iterative Learning Control With Predictive Trial Information: Convergence, Robustness, and Experimental Verification

Iterative learning control (ILC) is a control design method for high-performance trajectory tracking. Most existing results achieve this by learning from information collected over the past executions of the task (named trials). This brief proposes a novel ILC design framework that updates the control input by learning not only from the past trials but also from the predicted future trials using knowledge of the plant model. It is shown that by including information from the predicted future trials, the designed ILC controller is less short sighted, and therefore better performance can be achieved. Analysis of the algorithm’s properties reveals potentially substantial benefit in terms of convergence speed; the proposed algorithm also possesses distinct robustness features with respect to model uncertainty. Both numerical simulations and experimental results using a nonminimum phase test facility are provided to demonstrate the effectiveness of the proposed method.

Iterative learning control algorithm for spiking behavior of neuron model

Controlling neurons to generate a desired or normal spiking behavior is the fundamental building block of the treatment of many neurologic diseases. The objective of this work is to develop a novel control method–closed-loop proportional integral (PI)-type iterative learning control (ILC) algorithm to control the spiking behavior in model neurons. In order to verify the feasibility and effectiveness of the proposed method, two single-compartment standard models of different neuronal excitability are specifically considered: Hodgkin–Huxley (HH) model for class 1 neural excitability and Morris–Lecar (ML) model for class 2 neural excitability. ILC has remarkable advantages for the repetitive processes in nature. To further highlight the superiority of the proposed method, the performances of the iterative learning controller are compared to those of classical PI controller. Either in the classical PI control or in the PI control combined with ILC, appropriate background noises are added in neuron models to approach the problem under more realistic biophysical conditions. Simulation results show that the controller performances are more favorable when ILC is considered, no matter which neuronal excitability the neuron belongs to and no matter what kind of firing pattern the desired trajectory belongs to. The error between real and desired output is much smaller under ILC control signal, which suggests ILC of neuron’s spiking behavior is more accurate.

Velocity Tracking Control of Wheeled Mobile Robots by Iterative Learning Control

This paper presents an iterative learning control (ILC) strategy to resolve the trajectory tracking problem of wheeled mobile robots (WMRs) based on dynamic model. In the previous study of WMRs’ trajectory tracking, ILC was usually applied to the kinematical model of WMRs with the assumption that desired velocity can be tracked immediately. However, this assumption cannot be realized in the real world at all. The kinematic and dynamic models of WMRs are deduced in this chapter, and a novel combination of D-type ILC algorithm and dynamic model of WMR with random bounded disturbances are presented. To analyze the convergence of the algorithm, the method of contracting mapping, which shows that the designed controller can make the velocity tracking errors converge to zero completely when the iteration times tend to infinite, is adopted. Simulation results show the effectiveness of D-type ILC in the trajectory tracking problem of WMRs, demonstrating the effectiveness and robustness of the algorithm in the condition of random bounded disturbance. A comparative study conducted between D-type ILC and compound cosine function neural network (NN) controller also demonstrates the effectiveness of the ILC strategy.

Iterative learning control bandwidth tuning using the particle swarm optimization technique for high precision motion

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.

An iterative learning control design approach for networked control systems with data dropouts

SummaryIn network‐based iterative learning control (ILC) systems, data dropout often occurs during data packet transfers from the remote plant to the ILC controller. This paper considers the problem of controller design for such ILC processes. Packet missing is modeled by stochastic variables satisfying the Bernoulli random binary distribution, which renders such an ILC system to be a stochastic one. Then, the design of ILC law is transformed into the stabilization of a 2‐D stochastic system described by the Roesser model. A sufficient condition for mean‐square asymptotic stability is established by means of a linear matrix inequality technique, and formulas can be given for the control law design simultaneously. This result is further extended to more general cases where the system matrices also contain uncertain parameters. The effectiveness and merits of the proposed method are illustrated by a numerical example. Copyright © 2015 John Wiley & Sons, Ltd.

Improvement of trajectory tracking control performance by using ILC

This paper presents an iterative learning control (ILC) approach for tracking problems with specified data points that are desired points at certain time instants. To design ILC systems for such problems, unlike traditional ILC approaches, an algorithm which updates not only the control signal but also the reference trajectory at each trial will be developed. The relationship between the reference trajectory and ILC control in tracking problems where there are specified data points through which the system should pass is investigated as the rate of convergence. In traditional ILC, the desired data is stored in a tracking profile file. Due to the huge size of the data file containing the target points, it is important to reduce the computational cost. Finally, simulation results of the presented technique are mentioned and compared to other related works to confirm the effectiveness of proposed scheme.

A study on the optimal tracking problems with predefined data by using iterative learning control

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.

An Iterative Learning Control Technique for Point-to-Point Maneuvers Applied on an Overhead Crane

An iterative learning control (ILC) strategy is proposed, and implemented on simple pendulum and double pendulum models of an overhead crane undergoing simultaneous traveling and hoisting maneuvers. The approach is based on generating shaped commands using the full nonlinear equations of motion combined with the iterative learning control, to use as acceleration commands to the jib of the crane. These acceleration commands are tuned to eliminate residual oscillations in rest-to-rest maneuvers. The performance of the proposed strategy is tested using an experimental scaled model of an overhead crane with hoisting. The shaped command is derived analytically and validated experimentally. Results obtained showed that the proposed ILC control strategy is capable of eliminating travel and residual oscillations in simple and double pendulum models with hoisting. It is also shown, in all cases, that the proposed approach has a low sensitivity to the initial cable lengths.

Active flow separation control by a position-based iterative learning control algorithm with experimental validation

A novel iterative learning control (ILC) algorithm was developed and applied to an active flow control problem. The technique uses pulsed air jets to delay flow separation on a two-element high-lift wing. The ILC algorithm uses position-based pressure measurements to update the actuation. The method was experimentally tested on a wing model in a 0.9 m × 0.6 m low-speed wind tunnel at the University of Southampton. Compressed air and fast switching solenoid valves were used as actuators to excite the flow, and the pressure distribution around the chord of the wing was measured as a feedback control signal for the ILC controller. Experimental results showed that the actuation was able to delay the separation and increase the lift by approximately 10%–15%. By using the ILC algorithm, the controller was able to find the optimum control input and maintain the improvement despite sudden changes of the separation position.

On Iterative Learning Control for Remote Control Systems with Packet Losses

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.