A Nonlinear Optimal Iterative Learning Control Algorithm Based on RBF Neural Network and Clonal Selection Algorithm

2013 ◽  
Vol 753-755 ◽  
pp. 1225-1229
Author(s):  
Heng Jie Li ◽  
Xiao Hong Hao ◽  
Xi Ping Pei
Keyword(s):  
Neural Network ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Rbf Neural Network ◽  
Learning Control ◽  
Iterative Learning ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm

Improved clonal selection algorithms and RBF neural network are used for solving nonlinear optimization problems and modeling respectively in iterative learning control, and a nonlinear optimal iterative learning control algorithm (NOILCA) is proposed. In this method, an improved clonal selection algorithm is used for solving the optimum input for the next iteration; another one is used to update the RBF neural network model of real plant. Compared with GA-ILC, NOILCA has faster convergence speed, and is able to deal with the problem of inaccurate plant model, can obtain satisfactory tracking through the few several iterations.

A Clonal Selection Algorithm Based Fuzzy Optimal Iterative Learning Control Algorithm

2012 ◽  
Vol 490-495 ◽  
pp. 329-333
Author(s):  
Heng Jie Li ◽  
Xiao Hong Hao ◽  
Xian Jun Du ◽  
Ya Rong Jin
Keyword(s):  
Model Uncertainty ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Clonal Selection ◽  
Learning Control ◽  
Iterative Learning ◽  
System Model ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Takagi Sugeno

In order to realize effective tracking of output of non-linear plants with model uncertainty in specified time domain, a clonal selection algorithm based fuzzy optimal iterative learning control algorithm is proposed. In the algorithm, a clonal selection algorithm is employed to search optimal input for next iteration, and another clonal selection algorithm is used to update the parameters of Takagi-Sugeno-Kang fuzzy system model of the plant. Simulations show that the proposed method converges faster than GA-ILC in iterative domain，and is able to deal with model uncertainty well

A Clonal Selection Algorithm Based Optimal Iterative Learning Control with Random Disturbance

2011 ◽  
Vol 105-107 ◽  
pp. 2299-2302
Author(s):  
Xiao Hong Hao ◽  
Qun Gu
Keyword(s):  
Iterative Learning Control ◽  
Clonal Selection ◽  
Learning Control ◽  
Iterative Learning ◽  
Convergence Speed ◽  
Control Law ◽  
Clonal Selection Algorithm ◽  
Random Disturbance ◽  
Selection Algorithm ◽  
Plant Model

Clonal selection algorithm is improved and proposed as a method to solve optimization problems in iterative learning control. And a clonal selection algorithm based optimal iterative learning control algorithm with random disturbance is proposed. In the algorithm, at the same time, the size of the search space is decreased and the convergence speed of the algorithm is increased. In addition a model modifying device is used in the algorithm to cope with the uncertainty in the plant model. In addition a model is used in the algorithm cope with the uncertainty in the plant model. Simulations show that the convergence speed is satisfactory regardless of whether or not the plant model is precise nonlinear plants. The simulation test verify the controlled system with random disturbance can reached to stability by using improved iterative learning control law but not the traditional control law.

Iterative Learning Control Based on Radial Basis Function Network for Exoskeleton Arm

2011 ◽  
Vol 415-417 ◽  
pp. 116-122 ◽  
Author(s):  
Jie Liu ◽  
Yu Wang ◽  
He Ting Tong ◽  
Ray P.S. Han
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Iterative Learning Control ◽  
Rbf Neural Network ◽  
Radial Basis Function Network ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Radial Basis

In this paper, we propose iterative learning control (ILC) scheme for exoskeleton arm driven by pneumatic artificial muscles (PAM), with special and unknown parameters, performing repetitive tasks. This desired control input of ILC was estimated by radial basis function (RBF) neural network incorporated experience database. An ILC controller, which uses the position of the joint where an angular sensor is used as the input of the ILC controller, is developed and tested on exoskeleton arm under well controlled conditions. RBF neural network was proposed to obtain the initial value of ILC. The experiment result on the experimental platform show that the algorithm is successful also in the application of exoskeleton arm.

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