Iterative Learning Instantaneous Optimal Control of Discrete Systems and Optimization

TONG Shao-wei; TANG Huai-ping

doi:10.3879/j.issn.1000-0887.2016.02.005

Volume 37 Issue 2

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TONG Shao-wei, TANG Huai-ping. Iterative Learning Instantaneous Optimal Control of Discrete Systems and Optimization[J]. Applied Mathematics and Mechanics, 2016, 37(2): 160-172. doi: 10.3879/j.issn.1000-0887.2016.02.005

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Iterative Learning Instantaneous Optimal Control of Discrete Systems and Optimization

doi: 10.3879/j.issn.1000-0887.2016.02.005

School of Mechanics and Engineering, Southwest Jiaotong University, Chengdu 610031, P.R.China

Funds: The National Natural Science Foundation of China(51378437)

Received Date: 2015-07-08
Rev Recd Date: 2015-12-08
Publish Date: 2016-02-15

Abstract

Abstract

Through combination of the instantaneous optimal control (IOC) and the iterative learning control (ILC), one new hybrid control strategy called the iterative learning instantaneous optimal control was proposed. The discrete linear system was chosen as the target model for the new control strategy, and the quadratic performance functional of the discrete system was taken as the objective function to be minimized. During the controlling process of the system, the core idea of the ILC was introduced in order to modify the control signals which were initialized by the IOC. With the method of matrix norms, the sufficient condition for convergence of the new control strategy was established. Compared with the IOC, the iterative learning instantaneous optimal control gives simulation results of improved effectiveness. Furthermore, based on the improved genetic algorithm (GA), the optimization of the actuator positions in a multistory building to be controlled was investigated. Results of the numerical simulation indicate that, while the actuators are partially positioned at some optimally seleted floors, the control effects may reach or even be better than those in the case of full installation of actuators at all floors.
- instantaneous optimal control,
- iterative learning control,
- stability,
- performance functional,
- genetic algorithm

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References(16)

References

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