Hamidreza Modares

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This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of(More)
This paper presents a method of Q-learning to solve the discounted linear quadratic regulator (LQR) problem for continuous-time (CT) continuous-state systems. Most available methods in the existing literature for CT systems to solve the LQR problem generally need partial or complete knowledge of the system dynamics. Q-learning is effective for unknown(More)
A hybrid algorithm by integrating an improved particle swarm optimization (IPSO) with successive quadratic programming (SQP), namely IPSO–SQP, is proposed for solving nonlinear optimal control problems. The particle swarm optimization (PSO) is showed to converge rapidly to a near optimum solution, but the search process will become very slow around global(More)
Bilinear models can approximate a large class of nonlinear systems adequately and usually with considerable parsimony in the number of coefficients required. This paper presents the application of Particle Swarm Optimization (PSO) algorithm to solve both offline and online parameter estimation problem for bilinear systems. First, an Adaptive Particle Swarm(More)