A Robust Iterative Learning Control with Neural Networks for Robot

Here the authors concerned with robust iterative learning control (ILC) for uncertain time-delay systems. They demonstrate that the design of a robust ILC is straightforward based on a performance index for the error system. The ILC algorithms under consideration are rather simple. They show that after the two-dimensional analysis of ILC, a Lyapunov-like approach can be used to directly obtain a stable algorithm that achieves monotonic convergence of the control input error. Sufficient stability conditions are provided in terms of linear matrix inequalities, which can determine learning gains as well. They also show that the Lyapunov-like approach can be applied to design robust ILC for uncertain systems with time-varying delay or multiple time delays. Numerical simulation results are presented to illustrate the effectiveness of the proposed ILC approach.

In this paper is proposed a robust higher-order iterative learning control (ILC) algorithm for discrete-time systems. In contrast to conventional discrete-time learning methods, the proposed learning algorithm is constructed based on both time-domain performance and iteration-domain performance. Also, the proposed learning algorithm use more than one past error in the iteration-domain. It is proved that the proposed method has robustness in the presence of external disturbances and, in absence of all disturbances, the convergence of the proposed learning algorithm is guaranteed. A numerical example is given to show the robustness in the presence of state disturbance and convergence property according to parameters change.

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.

In this paper, a robust adaptive iterative learning control (ILC) scheme is developed for the high-precision attitude tracking control of spacecraft in the presence of parametric uncertainties and external disturbances. The proposed robust adaptive ILC law consists of three parts, i.e., the classic proportional-derivative (PD) feedback control term, the PD-type feedforward learning term, and the robust term. The adaptive updating laws are designed for the gain matrices of both the classic PD feedback control term and the PD-type feedforward learning term. The asymptotic stability of the whole closed-loop system is proved through the Lyapunov function–based convergence analysis. The proposed robust adaptive ILC scheme can not only compensate for the parametric uncertainties and repetitive disturbance, but also handle the nonrepetitive disturbance owing to the robust control concept. Moreover, the proposed robust adaptive ILC scheme can achieve the fast convergence speed benefiting from the adaptive technique. Numerical simulations illustrate the effectiveness and superiority of the proposed ILC scheme.

Convergence – an important task in the design and application of iterative learning control (ILC) cyclic processes. This article presents a new method for the synthesis of robust iterative learning control. We give necessary and sufficient conditions for stability control system with low flow limit input and output signals (chains) in constructing optimal ILC, the task of which is arbitrarily limited tracking signal problem.For cyclical processes of ILC iteration to iteration improves the accuracy of control within two time dimension, the first of which is denoted by the symbol and describes the transition from one cycle to the next, and the second index identifies the current time during each individual cycle work. The main task of the ILC is to create such an algorithm that generates the control signals in a way that improves quality control by one cycle operation (iteration) to the next.The algorithm of iterative learning control for facilities operating in cyclic mode. This algorithm takes into account uncertainty in the assessment of the initial state of the control system and external disturbances acting on the system. ILC-developed algorithm that would take into account the uncertainty existing in the object control disturbances and changes in the initial state of the object from cycle to cycle works. A study of the robustness and convergence of the proposed algorithm. Entering setpoint weight matrices in quadratic quality criteria of the system increased its effectiveness in terms of initial uncertainties and the effect of external disturbances. The paper offered a ILC algorithm that combines the standard scheme of this method and optimal feedback built using Riccati equation, leading to guaranteed convergence algorithm exponentially. Studies have shown that this algorithm is much more efficient than the standard that enhances its practical implementation. It was determined that the use of the modified algorithm ILC-enabled to eliminate fluctuations in output, provide fast convergence to the desired trajectory by increasing system stability and control.The method of choosing the weight matrix setting process of accurately defined initial state and the current disturbances. Effects-ness of the proposed algorithm is studied as an example.

We address the tracking control problem via state feedback for uncertain current-fed permanent magnet step motors with non-sinusoidal flux distribution: a periodic reference signal (of known period) for the rotor position is required to be tracked. We design a robust iterative learning control algorithm which, for any motor initial condition and without requiring any resetting procedure, guarantees, despite system uncertainties: exponential convergence of the rotor position tracking error to a residual ball (centered at the origin) whose radius can be made arbitrarily small by properly setting the learning gain; asymptotic convergence of the rotor position tracking error to zero. Robustness with respect to a finite memory implementation of the control algorithm based on the piecewise linear approximation theory is shown to be guaranteed.

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.

This paper addresses robust performance analysis and synthesis of lifted system iterative learning control (ILC). By applying the full block S-procedure, sufficient conditions for the robust performance of ILC with both unstructured and structured uncertainty are derived. In the synthesis problem, we consider the design of the learning filter Q for modelinversion based ILC. The problem is reformulated as a convex optimization problem such that the converged tracking error is minimized subject to the monotonic convergence condition. This synthesis approach enables full automation of the ILC design since neither an uncertainty model has to be identified nor a robustness filter has to be chosen. The advantages of this novel robust performance ILC technique are demonstrated on an experimental linear motor test setup.

Iterative learning control (ILC) algorithms appeared in connection with the problems of increasing the accuracy of performing repetitive operations by robots. They use information from previous repetitions to adjust the control signal on the current repetition. Most often, information from the previous repetition only is used. ILC algorithms that use information from several previous iterations are called higher-order algorithms. Recently, interest in these algorithms has increased in the literature in connection with robotic additive manufacturing problems. However, in addition to the fact that these algorithms have been little studied, there are conflicting estimates regarding their properties. This paper proposes new higher-order ILC algorithms for linear discrete and differential systems. The idea of these algorithms is based on an analogy with multi-step methods in optimization theory, in particular, with the heavy ball method. An example is given that confirms the possibility to accelerate convergence of the learning error when using such algorithms.

In this paper, we studied the Position&Force servo control of the voice coil motor commonly used in semiconductor packaging equipment and analyzed its Position&Force servo control switching strategy. System identification is the basis for high-speed, high-precision motion control. We use the frequency domain identification method to identify the voice coil motor system. The frequency domain identification method uses sine sweep frequency and PRBS method to compare and verify. Feedback and feedforward servo control structures are used to ensure quick response and precise control of voice coil motors while ensuring system stability. In the feedforward control, we use an iterative learning control method. In semiconductor packaging equipment, the movement of the voice coil motor has always been a similar process cycle. Iterative learning control (ILC) has prominent control effects in situations where control objectives and external disturbances are periodic or repetitive. The iterative learning control algorithm is applied to the feedforward plus feedback servo control structure of the voice coil motor. From the actual effect point of view, the trajectory tracking ability of the voice coil motor during high-speed motion is significantly increased. The voice coil motor servo control algorithm described above is written on the DSP and verified on the motion control card. The experimental results show that the above servo control algorithm realizes the high speed and high precision motion control of the voice coil motor.

With the accelerating integration of renewable energy into new power system, load frequency control (LFC) plays a crucial role in maintaining system stability and reliability. Traditional LFC methods usually adopt fixed control strategies, which are vulnerable to the dynamic characteristics of the system. This vulnerability is especially evident during complex load fluctuations and the continuous decrease in system inertia, highlighting the need to enhance controller performance. Moreover, with the expanding role of communication networks in frequency modulation, this paper proposes a novel LFC strategy for power systems. This strategy utilizes an adaptive event-triggered mechanism based on iterative learning. The adaptive event-triggered mechanism is employed to reduce the transmission requirements of the communication network, and an iterative learning control algorithm is incorporated to improve frequency modulation performance. The effectiveness of this method is verified through simulations under various operating conditions. The results show that the adaptive event-triggered mechanism can dynamically adjust the execution frequency of the control strategy, thereby reducing communication burdens and energy losses. Additionally, the iterative learning control strategy not only improves the system response speed and stability but also enables the system to tolerate a larger event-triggered threshold, thus better coordinating with the adaptive event-triggered mechanism.

Iterative learning contouring control: Theory and application to biaxial systems

An iterative learning control (ILC) framework based on two-degree-of-freedom control is presented for a linear plant with perturbations. A sufficient condition in the frequency domain is obtained for robust convergence with the bounded asymptotic error in the existence of input disturbance, output disturbance and errors in initial states. Based on the sufficient condition, the problem of designing ILC can be converted to a robust performance problem and a robust ILC against disturbances and initialization errors is derived by structured singular value and mu synthesis

Gimbal-less dual axis point-to-point (quasistatic) MEMS mirrors have very wide bandwidths for laser beam steering, however users are often limited to only a third of the bandwidth due to the high Q-factor and use of low-pass filters in open-loop operation to avoid overshoot and oscillation. Closed-loop driving enables the use of the full bandwidth with additional complexity in optics and electronics, which can be undesirable in some low SWaP-C systems. But for many applications which require scanning repetitive patterns, such as LiDAR and biomedical imaging, bandwidth utilization and linearity can be greatly increased without any real-time feedback control by training the device and finding an optimal driving waveform using an iterative learning algorithm. The algorithm drives the device with a trial waveform, measures the scan on a Position Sensing Device (PSD), calculates the error between the desired waveform and the measured position, and adjusts the drive waveform for the next iteration based on an approximate linear device model. This is repeated until the error is reduced to below an acceptable specification. The waveform is then saved in the MEMS Controller and can be reliably used for extended periods of operation. Multiple such drive signals can be trained and stored on the controller to perform different types of scans. Several MEMS mirrors, including single- and dual-axis designs, were studied and three are reported here. Overall, in all cases a high accuracy of optical scans is achieved, typically to within ±0.025° of nominal. Repeatability after training, then running in open loop is better than ±0.01° - however, this measurement was limited by the lower resolution of the position detecting sensor. Scan rates achieved vary based on mirror design, but in each case are greatly improved from those achievable with basic driving approaches. Each mirror demonstrated higher quality vector graphics content at faster refresh rates and stable linear rasters at rates below resonance where lines are scanned with uniform velocity. Additionally, each mirror could achieve stable fast rasters with the line-scanning axis rates just below resonance, giving sinusoidal scans with line rates of ~1.6fres. Finally, each mirror was also demonstrated achieving rasters with rates above resonance, giving line rates of ~2.5fres. In all of those cases the other axis could scan linear and sharp sawtooth or triangle waveforms. Based on the symmetry of the MEMS design, we demonstrated the same performance at ddegerent angles, e.g. rastering at a 45° angle.

Angular vibration turntable is an important device in the performance test of inertial device. First, the mathematical model of the three-axis angular vibration turntable is established in this paper; second, the iterative learning control algorithm based on the mathematical model is designed; and considering the situations of parameter uncertainty and parameter time-varying, in order to improve the precision during the period of tracking periodic anticipant input, the method of parameter adaptive is introduced based on the traditional PID iterative learning control; third, the convergence analysis of the adaptive parameter iterative learning control algorithm is carried out; finally, the experiments are carried out on the three-axis angular vibration turntable in order to verify the control algorithm proposed in this paper. The results of the experiments show that the parameter adaptive iterative learning control, compared to traditional PID control, can improve the precision of the system during tracking the periodic input, and the design is of benefit to reducing the periodic disturbance too.