Adaptive Control with Approximated Policy Search Approach - Naba, Agus (Journal of Engineering and Technological Sciences, 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.