Optimal control and stochastic estimation: theory and applications
Optimal control and stochastic estimation: theory and applications
Continuous-time self-tuning control
Continuous-time self-tuning control
Discrete Linear Control: The Polynomial Equation Approach
Discrete Linear Control: The Polynomial Equation Approach
Self-Tuning Systems: Control and Signal Processing
Self-Tuning Systems: Control and Signal Processing
Adaptation and Learning in Automatic Systems
Adaptation and Learning in Automatic Systems
Dynamic Programming
Journal of Control Science and Engineering
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This paper presents the implementation of an explicit model reference adaptive control (MRAC) for position tracking of a dynamically unknown robot. An auto regressive exogenous (ARX) model is chosen to define the plant model and the control input is optimised in a H2 norm to reduce computational time and to simplify the algorithm. The theory of MRAC falls into a description of the various forms of controllers and parameter estimation techniques, therefore, applications may require very complicated solution methods depending on the selected laws. However, in this study, the proposed MRAC shows that applications may be as easy as classical control methods, such as PID, by guaranteeing the stability and achieving the convergency of the plant parameters. Despite the selected simple control model, simple optimisation method and drawbacks of the robot the experimental results show that MRAC provides an excellent position tracking compared with conventional control (PID). Many experimental implementations have been done on the robot and one of them is included in the paper.