scholarly journals PD-Type Iterative Learning Control With Adaptive Learning Gains for High-Performance Load Torque Tracking of Electric Dynamic Load Simulator

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 811
Author(s):  
Mingguang Dai ◽  
Rong Qi ◽  
Yiyun Zhao ◽  
Yang Li
Keyword(s):  
Adaptive Learning ◽  
Iterative Learning Control ◽  
High Performance ◽  
Control Method ◽  
Iterative Learning ◽  
Learning Gains ◽  
Load Torque ◽  
Random Measurement ◽  
Load Simulator ◽  
Measurement Noises

To realize the high-performance load torque tracking of an electric dynamic load simulator system with random measurement noises and strong position disturbances, a PD-type iterative learning control (ILC) algorithm with adaptive learning gains is proposed in this paper. With the principle of system analyzing, a nonlinear discrete state-space model is established. The adaptive learning gains is used to suppress the effects of periodic disturbances and random measurement noises on the load torque tracking performance. A traditional PD feedback controller in parallel with the proposed ILC is designed to stabilize the system and render the ILC converge quickly. The convergence analysis of the proposed control method ensures the stability of the system. Compared with the fixed learning gains, the experiment results show that the proposed control method has better load torque tracking performance and can effectively suppress the adverse effects of periodic and aperiodic disturbances on tracking accuracy.

PD-PD Type Learning Control for Uncertain Nonlinear Systems

2009 ◽  
Author(s):  
P. R. Ouyang
Keyword(s):  
Feedback Control ◽  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Method ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Gains ◽  
Tracking Errors ◽  
Initial Errors

In this paper, a new learning control, called PD-PD type learning control, is proposed for trajectory tracking of nonlinear systems with uncertainty and disturbance. In the developed control scheme, a PD feedback control with the current tracking errors and a PD type iterative learning control using the previous tracking errors are combined in the updating law. Explicit expressions have been developed for choosing the feedback control gains and the iterative learning gains, and an initial updating scheme is proposed to reduce and eliminate initial errors from iteration to iteration. It is proven that the final tracking error is guaranteed to converge toward the desired trajectory in the presence of varying uncertainty, disturbance, and initial errors. Comparing with the traditional iterative learning control, the new algorithm has potential benefits that include: fast convergence rate, more flexible choices of the learning gains, and monotonic convergence of the tracking error. The effectiveness of the proposed learning control method is demonstrated by simulation experiments. Due to the straightforward implementation and very good trajectory performance of the proposed control algorithm, it should be highly applicable to industrial systems.

Iterative learning control of robot based on artificial bee colony algorithm

2019 ◽  
Vol 233 (9) ◽  
pp. 1221-1238
Author(s):  
Wanqiang Xi ◽  
Yaoyao Wang ◽  
Bai Chen ◽  
Hongtao Wu
Keyword(s):  
Iterative Learning Control ◽  
Degrees Of Freedom ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Control Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Industrial Robots ◽  
Control Effect ◽  
Bee Colony

For the repetitive motion control, inaccurate model, and other issues of industrial robots, this article presents a novel control method that the proportion differentiation-type iterative learning parameters are self-tuning based on artificial bee colony algorithm. Considering the influence of the numerical value of iterative learning parameters on the control system, especially in the early iteration, the control effect is not satisfactory. Thus, the artificial bee colony algorithm is introduced in this article. Using bee colony as search unit, the parameters in iterative learning are optimized through the exchange of information and the survival of fittest between them. And then the optimized results are returned to iterative learning control algorithm. Finally, the digital simulation of a two-degrees-of-freedom manipulator and the experimental verification of a cable-driven robot with its first two joints are carried out. The results show that the iterative learning control based on the artificial bee colony algorithm has faster convergence and better control effect than the iterative learning control with fixed parameters.

scholarly journals Novel Fuzzy PID-Type Iterative Learning Control for Quadrotor UAV

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 24 ◽  
Author(s):  
Jian Dong ◽  
Bin He
Keyword(s):  
Fuzzy Control ◽  
Iterative Learning Control ◽  
Control Method ◽  
External Disturbance ◽  
Learning Control ◽  
Iterative Learning ◽  
System Stability ◽  
Learning Gain ◽  
Control Precision ◽  
Quadrotor Aircraft

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.

Adaptive Iterative Learning Control for Nonlinear Systems With Unknown Control Gain1

2004 ◽  
Vol 126 (4) ◽  
pp. 916-920 ◽  
Author(s):  
Huadong Chen ◽  
Ping Jiang
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Learning Gain ◽  
Adaptive Iterative Learning Control ◽  
Control Gain ◽  
Unknown Control

An adaptive iterative learning control approach is proposed for a class of single-input single-output uncertain nonlinear systems with completely unknown control gain. Unlike the ordinary iterative learning controls that require some preconditions on the learning gain to stabilize the dynamic systems, the adaptive iterative learning control achieves the convergence through a learning gain in a Nussbaum-type function for the unknown control gain estimation. This paper shows that all tracking errors along a desired trajectory in a finite time interval can converge into any given precision through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.

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