Adaptive Control with Approximated Policy Search Approach - Naba, Agus (ITB Journal Publisher, LPPM ITB, 2010)
Most of existing adaptive controlÂ schemes are designed to minimize errorÂ betweenÂ plantÂ stateÂ andÂ goalÂ stateÂ despiteÂ theÂ factÂ thatÂ executingÂ actions that are predicted to result in smaller errors only can misleadÂ to non-goal states. We develop an adaptive control scheme that involves manipulating a controller ofÂ aÂ generalÂ typeÂ toÂ improveÂ itsÂ performanceÂ asÂ measuredÂ byÂ anÂ evaluation function. The developed method is closely relatedÂ to a theory of Reinforcement Learning (RL) but imposes a practical assumption made for faster learning. We assumeÂ thatÂ aÂ valueÂ functionÂ ofÂ RLÂ canÂ beÂ approximatedÂ byÂ aÂ functionÂ of Euclidean distance from a goal state and an action executed at the state. And, we proposeÂ toÂ useÂ itÂ forÂ theÂ gradientÂ searchÂ asÂ anÂ evaluationÂ function.Â Simulation results provided through application of the proposed scheme to a pole -balancing problem using a linearÂ state feedback controller and fuzzy controller verify the schemeâs efficacy.