Adaptive Control Using Multiple Models and Model Weighting
A general methodology for the identification and control of dynamical systems with several operating environments and possessing a high degree of uncertainty is presented. Neural networks are used to create multiple models to capture the dynamics of the various environments of the system. Control is effected by combining these models by using an evolutionary strategy. The methodology is applied to the problem of controlling a two-link robotic manipulator in the presence of disturbances and varying load conditions. Simulated results presented show that the proposed methodology yields better results compared to the ones obtained by using a single model or by using multiple models but switching to and tuning the model with the smallest tracking error.
A. Rajagopal and K. Krishnamurthy, "Adaptive Control Using Multiple Models and Model Weighting," American Society of Mechanical Engineers, Dynamic Systems and Control Division (Publication) DSC, vol. 58, pp. 805-812, American Society of Mechanical Engineers (ASME), Nov 1996.
ASME International Mechanical Engineering Congress and Exposition (1996: Nov. 17-22, Atlanta, GA)
Mechanical and Aerospace Engineering
Keywords and Phrases
Backpropagation; Computer simulation; Genetic algorithms; Identification (control systems); Learning systems; Manipulators; Mathematical models; Neural networks; Nonlinear control systems; Optimization; Robotics; Standard backpropagation method; Two link robotic manipulator; Adaptive control systems
Article - Conference proceedings
© 1996 American Society of Mechanical Engineers (ASME), All rights reserved.
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