Type Iterative Learning Control Algorithm Research Articles

Modern methods for solving partial differential equations of parabolic type and their mathematical theoretical foundations

Abstract In this paper, the existence problem of weak solutions in partial differential equations is first discussed. Secondly, the parabolic-type partial differential equation (PDE) model with non-chiral terms is used to portray the coupling principle of microwave heating dielectric medium, and the approximate models of TEM and TE10-wave microwave heating are constructed and verified by comparing with the COMSOL model in simulation. An open-loop type iterative learning control algorithm is proposed for the distributed parameter system, and the effectiveness of the control algorithm proposed in this paper is theoretically demonstrated. It is concluded that the TEM and TE10-wave microwave heating approximation models are more successful in describing the microwave heating process than the COMSOL model, and the consistency of the simulation results fully proves the correctness and effectiveness of the proposed model. The paradigm of the system output error decreases with the increase in the number of system runs, and the paradigm of the system output error tends to be close to zero.

Iterative learning control of complex Ginzburg–Landau system

Different from the existing research on iterative learning control (ILC) real-valued systems, complex-valued partial differential system is studied in this paper. All variables and parameters of the system are complex, and distributed control is imposed on the system. First, the original complex system is transformed into two coupled real systems, which represent the real and imaginary parts of the original complex system, respectively. Second, a complex [Formula: see text]-type ILC algorithm is designed that the real and imaginary parts of the learning law are coupled to each other. Then, the contraction mapping principle and analytical techniques are used to ensure that the tracking error converges to zero in the sense of [Formula: see text]-norm. Finally, a numerical simulation is presented to show the effectiveness of the proposed algorithm.

Iterative Learning Control for Motion Trajectory Tracking of a Circular Soft Crawling Robot.

Soft robots have recently received much attention with their infinite degrees of freedoms and continuously deformable structures, which allow them to adapt well to the unstructured environment. A new type of soft actuator, namely, dielectric elastomer actuator (DEA) which has several excellent properties such as large deformation and high energy density is investigated in this study. Furthermore, a DEA-based soft robot is designed and developed. Due to the difficulty of accurate modeling caused by nonlinear electromechanical coupling and viscoelasticity, the iterative learning control (ILC) method is employed for the motion trajectory tracking with an uncertain model of the DEA. A D2 type ILC algorithm is proposed for the task. Furthermore, a knowledge-based model framework with kinematic analysis is explored to prove the convergence of the proposed ILC. Finally, both simulations and experiments are conducted to demonstrate the effectiveness of the ILC, which results show that excellent tracking performance can be achieved by the soft crawling robot.

An Improved PD Type Iterative Learning Control Algorithm Based on Wavelet Filter

This paper proposes an improved PD type Iterative Learning Control (ILC) algorithm combined with Wavelet theory for linear time-invariant systems with random time delays. The transfer function of multi-level wavelet filter is researched, and the sufficient condition of the convergence is given. Simulation results illustrate the applicability and effectiveness of proposed approach.

Fuzzy iterative learning control based on genetic algorithm

In order to improve the control precision and to speed up the convergence rate of the controlled system,a kind of fuzzy PD type iterative learning control algorithm was put forward based on genetic algorithm.In the proposed approach,the iterative learning controller was designed by fuzzy Takagi-Sugeno-Kang(TSK) system,the parameters of fuzzy TSK system were calculated by genetic algorithm,and then appropriate updating law was created.Appropriate iterative learning control algorithm of controlled system was designed and compared with PD iterative learning control algorithm and fuzzy PID iterative learning control algorithm,and then the proposed algorithm was used in double joint manipulator simulation.The simulation results show the effectiveness of the proposed algorithm.

Robust monotone inverse type iterative learning control for discrete-time systems

In this report the robustness of inverse type iterative learning control algorithm is examined. It is shown that an optimally chosen adaptive learning gain results in robust geometric convergnce provided any multiplicative plant uncertainty meets a positivity requirement.