This paper proposes the bat-inspired algorithm (BIA) and gravitational search algorithm (GSA) as a new two artificial intelligence (AI) technique to design the model predictive controllers (MPC) with superconducting magnetic energy storage (SMES) and capacitive energy storage (CES) for load frequency control (LFC). In recent years, the designer used the trial, error technique and their expertise to set the parameters of the MPC with SMES and CES units. Herein, the BIA and GSA are used to tune the parameters of MPC with SMES and CES units simultaneously to overcome this problem. The proposed methods are applied to nonlinear interconnected three area power system to minimize the deviations of frequency and tie line powers against load perturbations. The superiority of the proposed methods is emphasized by comparing them with the BIA-based MPC without SMES and CES units and the conventional proportional-integral (PI) controller based on Ziegler-Nichols (ZN) technique.

The goal is to control the trajectory of the flight path of six degree of freedom flying body model using fractional PID. The design of fractional PID controller for the six degree of freedom flying body is described. The parameters of fractional PID controller are optimized by particle swarm optimization (PSO) method. In the optimization process, various objective functions were considered and investigated to reflect both improved dynamics of the missile system and reduced chattering in the control signal of the controller

Integration between energy storage systems and renewable energy sources (RESs) can effectively smooth natural fluctuations of the latter and ensure better frequency regulation. Optimal performance of the plug‐in hybrid electric vehicle (PEHV) battery, having longer plug‐in than driving time, makes it a good candidate for integration with RESs. Decentralised model predictive control (MPC) is proposed here for frequency regulation in a smart three‐area interconnected power system comprising PHEVs. Two MPCs in each area are considered to manipulate the input signals of the governor and PHEV in order to tolerate frequency perturbations subject to load disturbances and RES fluctuations. Setting the parameters of the six MPC controllers is carried out simultaneously based on imperialist competitive algorithm (ICA) and bat‐inspired algorithm (BIA). Time‐domain based objective function is suggested to account for system non‐linearities emanating from governor dead bands and turbine generation rate constraints. The proposed tuning procedures utilising ICA and BIA are completely accomplished off‐line. Comparative simulation results are presented to confirm the effectiveness of the proposed design.