Safety-critical Policy Iteration Algorithm for Control under Model Uncertainty

Safety is an important aim in designing safe-critical systems. To design such systems, many policy iterative algorithms are introduced to find safe optimal controllers. Due to the fact that in most practical systems, finding accurate information from the system is rather impossible, a new online training method is presented in this paper to perform an iterative reinforcement learning based algorithm using real data instead of identifying system dynamics. Also, in this paper the impact of model uncertainty is examined on control Lyapunov functions (CLF) and control barrier functions (CBF) dynamic limitations. The Sum of Square program is used to iteratively find an optimal safe control solution. The simulation results which are applied on a quarter car model show the efficiency of the proposed method in the fields of optimality and robustness

A Novel PSOGSA_SQP Algorithm for Solving Optimal Power Flow (OPF) Problem Based on Specific Switching Condition

Abstract In this paper, PSOGSA_SQP novel hybrid algorithm is proposed to solve the optimal power flow (OPF) problem. Employing the proposed method aims at optimally adjusting control variables and increasing speed. The performance of the proposed method is studied on a standard system introduced in IEEE standards with 30 buses and specific objective functions which seek to minimize generator fuel costs, voltage stability and voltage profile improvement in different modes. Simulation results certify that this algorithm is faster than other methods such as PSO, GSA, GA, in view of using the SQP classical gradient-based method and its combination with PSOGSA intelligent method. We also propose a new switching method to switch from intelligent optimization method to SQP. This algorithm minimizes cost function value by accurately adjusting control variables