A TWO-STAGE ALGORITHM FOR COMBINED ITERATIVE LEARNING CONTROL WITH REAL-TIME FEEDBACK; A STATE SPACE FORMULATION

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.

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.

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.

A two-stage iterative learning control technique combined with real-time feedback for independent disturbance rejection

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, it is proposed a discrete-time multivariable iterative learning control (ILC) algorithm which guarantees the monotonic convergence of the tracking error norms along with the number of the trials. It is pointed out that an interactor matrix plays an important role in the iterative learning control. Since ILC is a design method not requiring plant parameters explicitly, the information on the interactor matrix should be treated same manner as in an adaptive control systems. In this paper, it is presented a design of ILC using G-interactor which is derived not requiring the plant parameters. It is also presented a design of ILC using a diagonal precompensator which will make the interactor of the compensated plant be diagonal. Numerical simulations are presented to confirm the validity of the proposed design.

On reference governor in iterative learning control for dynamic systems with input saturation

A robotic Ping-Pong server system could play an important role in skill training for players. Current available systems in the market provides different modes with rough control of the ball landing point. In this paper, we present the design and implementation of a robotic Ping-Pong server system to achieve precise ball landing point. A cost-effective multimodal landing point detection system is proposed to detect the landing point by fusing video and audio signals. A lookup table is used to relate the server launching parameters with the landing points. To keep the lookup table dynamic, an iterative learning control (ILC) algorithm is adopted to automatically update the lookup table in case of environmental changes. The ILC algorithm is tested and validated in simulation. Results showed the system was able to precisely serve the ball to the desired point automatically.

In this chapter, it is first observed that, when a typical iterative learning control(ILC) algorithm is applied to a class of linear dynamic systems with time-delay, erratic estimation of delay time may cause the control input to diverge. In order to resolve such a difficulty due to uncertainty of the delay time, a new ILC algorithm is proposed, in which the holding mechanism is adopted to hold the control input at a constant value for the duration of the delay time uncertainty. As a consequence, the output of the system tracks a given desired trajectory at discrete time-points which are spaced by the size of the uncertainty of delay time. In addition to the consideration of the delay time, the effect of the initial state error is also studied with a modification of the proposed ILC algorithm. Numerical examples are given to show the effectiveness of the proposed algorithms.

Focused ultrasound combined with bubble-based agents serves as a non-invasive way to open the blood-brain barrier (BBB). Passive acoustic detection was well studied recently to monitor the acoustic emissions induced by the bubbles under ultrasound energy, but the ability to perform reliable BBB opening with a real-time feedback control algorithm has not been fully evaluated. This study focuses on characterizing the acoustic emissions of different types of bubbles: Optison, Definity, and a custom-made nanobubble. Their performance on reliable BBB opening under real-time feedback control based on acoustic detection was evaluated both in-vitro and in-vivo. The experiments were conducted using a 0.5 MHz focused ultrasound transducer with in-vivo focal pressure ranges from 0.1–0.7 MPa. Successful feedback control was achieved with all three agents when combining with infusion injection. Localized opening was confirmed with Evans blue dye leakage. Microscopic images were acquired to review the opening effects. Under similar total gas volume, nanobubble showed a more reliable opening effect compared to Optison and Definity (p < 0.05). The conclusions obtained from this study confirm the possibilities of performing stable opening using a feedback control algorithm combined with infusion injection. It also opens another potential research area of BBB opening using sub-micron bubbles.

Iterative learning control (ILC) algorithms are typically used to improve the performance of repetitive processes. Numerous successful applications of ILC, such as computer numerical control (CNC) machining processes, robot manipulation, and lithography processes, have been reported. However, ILC often exhibits less than satisfactory performance since the control unit operates at a limited sampling rate in consideration of cost. In this study, the multiloop, multirate structure of servo motor control systems is taken advantage of and a fast-update multirate ILC (FILC) scheme is proposed for high-accuracy trajectory tracking, where compensation is updated at a rate faster than that of the original position loop without the need for redesigning the feedback controller. The key difficulty related to the design of the FILC lies in the extremely complex and time-varying dynamics inherent in multirate systems. To address this problem, a novel equivalent single-rate parametric model description of the multirate system is derived, which enables the use of the efficient norm optimal ILC algorithm. Consequently, a computationally efficient FILC is obtained to improve the performance. Subsequently, the proposed FILC is applied to the position control of the CNC motion stage. Simulation and experimental results are used to verify the effectiveness of the proposed method.

Lots of iterative learning control (ILC) algorithms have been developed to improve tracking performance of systems that track same trajectory repetitively. In this paper, we use an open-closed-loop PD-type ILC scheme for the control of a class of nonlinear systems. Sufficient condition for guaranteeing the convergence of the learning scheme is given under a few reasonable assumptions, and then proved with linear operator theory. It is shown that the proportional coefficients of the ILC scheme is not a factor for guaranteeing the convergence, the condition is weaker than known results for similar ILC schemes.

The tracking performance of two iterative learning control algorithms is compared to that, which can be achieved by an optimal feedback controller. P-type iterative Learning Control in parallel with a proportional feedback controller is compared with norm-optimal iterative learning control, then both ILC systems are compared with the performance achieved by an optimal feedback controller. Considering that the ILC plus proportional controller requires no prior modelling of the plant and minimal adjustment of gains, the tracking performance in terms of mean squared error (mse) per iteration can be reduced by two orders of magnitude further than can be achieved with the optimal feedback controller. However, the Norm-Optimal ILC improves upon this performance by reducing the mse by an extra order of magnitude. The experimental results are derived from tests performed on a gantry robot.

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.

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.