Abstract
In this work, a closed-loop identification method based on a reinforcement learning algorithm is proposed for multiple-input multiple-output (MIMO) systems. This method could be an attractive alternative solution to the problem that the current frequency-domain identification algorithms are usually dependent on the attenuation factor. With this method, after continuously interacting with the environment, the optimal attenuation factor can be identified by the continuous action reinforcement learning automata (CARLA), and then the corresponding parameters could be estimated in the end. Moreover, the proposed method could be applied to time-varying systems online due to its online learning ability. The simulation results suggest that the presented approach can meet the requirement of identification accuracy in both square and non-square systems.
Highlights
With the rapid development of modern industry, it becomes increasingly difficult for the traditional model control methods to properly control complex process due to the uncertainty, time-delay, multivariable coupling, and constraints between input and output
It is a challenge for the traditional identification methods to obtain optimal results, in multivariable systems, due to their complex structure, various parameters, and time-varying in industrial applications
Schwefel’s learning problem (CARLA) algorithms were respectively tested for the standard functions above
Summary
With the rapid development of modern industry, it becomes increasingly difficult for the traditional model control methods to properly control complex process due to the uncertainty, time-delay, multivariable coupling, and constraints between input and output. It is a challenge for the traditional identification methods to obtain optimal results, in multivariable systems, due to their complex structure, various parameters, and time-varying in industrial applications. Methods for the identification of multivariable systems based on state space have large computational demand, take a long time, and it is difficult for them to achieve the global optimal solution. The proposed method can be used online and applied to time-varying systems due to its online learning ability
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