Abstract
The main problem concerning model free learning controllers in particular BELBIC (Brain Emotional Learning Based Intelligent controller), is attributed to initial steps of learning process since the system performance is dramatically low, because they produce inappropriate control commands. In this paper a new approach is proposed in order to control unstable systems or systems with unstable equilibrium. This method is combination of one imitation phase to imitate a basic solution through a basic controller and two optimization phases based on PSO (Particle Swarm Optimization) which are employed to find a new solution for stress generation and to improve control signal gradually in reducing error. An inverted pendulum system is opted as the test bed for evaluation. Evaluation measures in simulation results show the improvement of error reduction and more robustness than a basic tuned double-PID controller for this task.
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Valipour, M.H., Maleki, K.N., Ghidary, S.S. (2015). Optimization of Emotional Learning Approach to Control Systems with Unstable Equilibrium. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Studies in Computational Intelligence, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-319-10389-1_4
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DOI: https://doi.org/10.1007/978-3-319-10389-1_4
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