Functional Adaptive Control: An Intelligent Systems Approach
Functional Adaptive Control: An Intelligent Systems Approach
Brief paper: Adaptive critic methods for stochastic systems with input-dependent noise
Automatica (Journal of IFAC)
A probabilistic particle-control approximation of chance-constrained stochastic predictive control
IEEE Transactions on Robotics
Radial basis function neural network-based adaptive critic control of induction motors
Applied Soft Computing
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Following the recently developed algorithms for fully probabilistic control design for general dynamic stochastic systems (Herzallah & Kaarnay, 2011; Karny, 1996), this paper presents the solution to the probabilistic dual heuristic programming (DHP) adaptive critic method (Herzallah & Kaarnay, 2011) and randomized control algorithm for stochastic nonlinear dynamical systems. The purpose of the randomized control input design is to make the joint probability density function of the closed loop system as close as possible to a predetermined ideal joint probability density function. This paper completes the previous work (Herzallah & Kaarnay, 2011; Karny, 1996) by formulating and solving the fully probabilistic control design problem on the more general case of nonlinear stochastic discrete time systems. A simulated example is used to demonstrate the use of the algorithm and encouraging results have been obtained.