Mohammad-Bagher Naghibi-Sistani

TL;DR: An integral reinforcement learning algorithm on an actor-critic structure is developed to learn online the solution to the Hamilton-Jacobi-Bellman equation for partially-unknown constrained-input systems and it is shown that using this technique, an easy-to-check condition on the richness of the recorded data is sufficient to guarantee convergence to a near-optimal control law.

TL;DR: This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems where two neural networks are tuned online and simultaneously to generate the optimal bounded control policy.

TL;DR: An output-feedback solution to the infinite-horizon linear quadratic tracking (LQT) problem for unknown discrete-time systems is proposed and a novel Bellman equation is developed that evaluates the value function related to a fixed policy by using only the input, output, and reference trajectory data from the augmented system.

TL;DR: A novel fully distributed controller is developed based on backstepping technique and neuro-adaptive update mechanism to ensure bipartite consensus of multiple fractional-order nonlinear systems with output constraints and it is shown that all the closed-loop error signals are uniformly ultimately bounded.

TL;DR: Finite-time bipartite synchronization of multi-agent systems is assessed here and a virtual affine variable is introduced, and neural network along with minimal learning parameter principle are employed to approximate composite uncertainties including unknown functions in the system dynamics, unknown control coefficients and control inputs.