Input and Output Constraints in Iterative Learning Control Design for Robotic Manipulators

2018 ◽  
Vol 06 (03) ◽  
pp. 197-208 ◽  
Author(s):  
Gijo Sebastian ◽  
Ying Tan ◽  
Denny Oetomo ◽  
Iven Mareels
Keyword(s):  
Iterative Learning Control ◽  
Internal State ◽  
Tracking Error ◽  
Robotic Manipulators ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Feed Forward Control ◽  
Output Constraints ◽  
Rehabilitation Robotic

Motivated by the safety requirement of rehabilitation robotic systems for after stroke patients, this paper handles position or output constraints in robotic manipulators when the patients repeat the same task with the robot. In order to handle output constraints, if all state information is available, a state feedback controller can ensure that the output constraints are satisfied while iterative learning control (ILC) is used to learn the desired control input through iterations. By incorporating the feedback control using barrier Lyapunov function with feed-forward control (ILC) carefully, the convergence of the tracking error, the boundedness of the internal state, the boundedness of input signals can be guaranteed along with the satisfaction of the output constraints over iterations. The effectiveness of the proposed controller is demonstrated using simulations from the model of EMU, a rehabilitation robotic system.

scholarly journals A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shangtai Jin ◽  
Zhongsheng Hou ◽  
Ronghu Chi
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Prediction Algorithm ◽  
Control Law ◽  
Control Input

A data-driven predictive terminal iterative learning control (DDPTILC) approach is proposed for discrete-time nonlinear systems with terminal tracking tasks, where only the terminal output tracking error instead of entire output trajectory tracking error is available. The proposed DDPTILC scheme consists of an iterative learning control law, an iterative parameter estimation law, and an iterative parameter prediction law. If the partial derivative of the controlled system with respect to control input is bounded, then the proposed control approach guarantees the terminal tracking error convergence. Furthermore, the control performance is improved by using more information of predictive terminal outputs, which are predicted along the iteration axis and used to update the control law and estimation law. Rigorous analysis shows the monotonic convergence and bounded input and bounded output (BIBO) stability of the DDPTILC. In addition, extensive simulations are provided to show the applicability and effectiveness of the proposed approach.

scholarly journals Iterative Learning Control of a Nonlinear Aeroelastic System despite Gust Load

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xing-zhi Xu ◽  
Ya-kui Gao ◽  
Wei-guo Zhang
Keyword(s):  
Control Strategy ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Surface ◽  
Stream Velocity ◽  
Control Input ◽  
Parameter Uncertainties ◽  
Composite Energy

The development of a control strategy appropriate for the suppression of aeroelastic vibration of a two-dimensional nonlinear wing section based on iterative learning control (ILC) theory is described. Structural stiffness in pitch degree of freedom is represented by nonlinear polynomials. The uncontrolled aeroelastic model exhibits limit cycle oscillations beyond a critical value of the free-stream velocity. Using a single trailing-edge control surface as the control input, a ILC law under alignment condition is developed to ensure convergence of state tracking error. A novel Barrier Lyapunov Function (BLF) is incorporated in the proposed Barrier Composite Energy Function (BCEF) approach. Numerical simulation results clearly demonstrate the effectiveness of the control strategy toward suppressing aeroelastic vibration in the presence of parameter uncertainties and triangular, sinusoidal, and graded gust loads.

scholarly journals Chaotic Synchronizing Systems with Zero Time Delay and Free Couple via Iterative Learning Control

2017 ◽  
Author(s):  
Chun-Kai Cheng ◽  
Paul C.-P. Chao
Keyword(s):  
Time Delay ◽  
Iterative Learning Control ◽  
Mathematical Theory ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Law ◽  
Control Input ◽  
Feed Forward Control ◽  
Response Systems ◽  
Error Dynamics

This research not only dedicated a less restrictive method of iteration-varying function for a learning control law to design a controller but also synchronize two nonlinear systems with free time-delay. In addition, the mathematical theory of system synchronization has proved rigorously and the theory verified through an example to demonstrate the behavior of each parameter in the theory. The design of a controller using the iterative learning control law is significant for robotic tracking. The controller in this research generates a feed-forward control input using the error dynamics among the drive-response systems. The error dynamics satisfies the Lyapunov function and the combination of output errors, which respectively represented relative estimated differences of the drive-response systems. The iterative learning control rule serves the function of a filter adding previous control error after the end of each iteration. The numerical example of a synchronous system is given a Lorenz system for driving and another with the iterative learning control law for response under different initial condition. The results verify and demonstrate the proposed mathematical theory. The simulation exhibits consistency in the behavior of each parameter to match mathematical theory.

High-performance tracking of high-speed supercavitating vehicles with uncertain parameters using novel parameter-optimal iterative learning control

Robotica ◽  
2014 ◽  
Vol 33 (08) ◽  
pp. 1653-1670 ◽  
Author(s):  
Meisam Yahyazadeh ◽  
Abolfazl Ranjbar Noei
Keyword(s):  
Phase Behavior ◽  
Iterative Learning Control ◽  
High Speed ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Minimum Phase ◽  
Feed Forward ◽  
Feed Forward Control ◽  
Supercavitating Vehicle

SUMMARYThis paper proposes a new technique based on a Parameter-Optimal Iterative Learning Control (POILC) to track a command pitch rate of a high-speed supercavitating vehicle (HSSV). The pitch rate of a supercavitating vehicle has non-minimum phase behavior. Thus, tracking is fundamentally limited to poor performance. To solve this problem, a feed-forward control can be used while using the cavitator as a control input in the feed-forward path to modify the slow response caused by non-minimum phase behavior. The main idea of this paper is to apply the cavitator input with high precision as a feed-forward control to improve tracking performance. The exact value of the feed-forward control is achieved using POILC. However, in the presence of uncertainty, zero convergence of POILC algorithms is threatened. It will be shown that applying adaptive weight in the performance index, the convergence is guaranteed in the presence of uncertainty and also when the system is sign-indefinite. The proposed technique includes an optimal planning to make the error norm monotonically convergent to zero. The convergence and perfect tracking will be guaranteed through a Lyapunov candidate. Performance and significance of the proposed supercavitating vehicle control will be verified by simulation.

scholarly journals Iterative learning control with sampled-data feedback for robot manipulators

2014 ◽  
Vol 24 (3) ◽  
pp. 299-319 ◽  
Author(s):  
Kamen Delchev ◽  
George Boiadjiev ◽  
Haruhisa Kawasaki ◽  
Tetsuya Mouri
Keyword(s):  
Feedback Control ◽  
Iterative Learning Control ◽  
Feedforward Control ◽  
Robotic Manipulators ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Sampled Data ◽  
Model Based ◽  
Data Feedback

Abstract This paper deals with the improvement of the stability of sampled-data (SD) feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC) with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more), while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached

Iterative learning control algorithm for sensor fault nonlinear systems

2020 ◽  
pp. 1-8
Author(s):  
Zimian Lan
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Sensor Fault ◽  
Initial State ◽  
Control Gain

In this paper, we propose a new iterative learning control algorithm for sensor faults in nonlinear systems. The algorithm does not depend on the initial value of the system and is combined with the open-loop D-type iterative learning law. We design a period that shortens as the number of iterations increases. During this period, the controller corrects the state deviation, so that the system tracking error converges to the boundary unrelated to the initial state error, which is determined only by the system’s uncertainty and interference. Furthermore, based on the λ norm theory, the appropriate control gain is selected to suppress the tracking error caused by the sensor fault, and the uniform convergence of the control algorithm and the boundedness of the error are proved. The simulation results of the speed control of the injection molding machine system verify the effectiveness of the algorithm.

A data-driven approach for trajectory-based aircraft operation with controlled time of arrival and along-track wind effects

2020 ◽  
Vol 42 (12) ◽  
pp. 2166-2177
Author(s):  
Gaoyang Jiang ◽  
Zhongsheng Hou
Keyword(s):  
Control Problem ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Time Of Arrival ◽  
Multiple Time ◽  
Data Driven Approach ◽  
Along Track

Trajectory-based aircraft operation and control is one of the hot issues in air traffic management. However, the accurate mechanism modeling of aircraft is tough work, and the operation data have not been effectively utilized in many studies. So, in this work, we apply the model-free adaptive iterative learning control method to address the time-of-arrival control problem in trajectory-based aircraft operation. This problem is first formulated into a trajectory tracking problem with along-track wind disturbance. Through rigorous analysis, it is shown that this method, combined with point-to-point iterative learning control (ILC) strategy, can effectively deal with the arrival time control problem with multiple time constraints. Then, the terminal ILC strategy is applied, aiming to resolve the same problem with a time constraint at the end point. Compared with the PID (Proportional Integral Derivative) type ILC, the proposed method improves control performance by 11.15% in root mean square of tracking error and 9.32% in integral time absolute error. The sensitivity and flexibility of the data-driven approach is further verified through numerical simulations.

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