A first approach to fuzzy differential game problem: guarding a territory
Fuzzy Sets and Systems
Advances in linear matrix inequality methods in control: advances in design and control
Advances in linear matrix inequality methods in control: advances in design and control
Nonlinear Control Systems
Linear Optimal Control Systems
Linear Optimal Control Systems
A strategy for a payoff-switching differential game based on fuzzy reasoning
Fuzzy Sets and Systems - Fuzzy models
Design of optimal disturbance rejection PID controllers usinggenetic algorithms
IEEE Transactions on Evolutionary Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An improved robust fuzzy-PID controller with optimal fuzzy reasoning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Finite-Dimensional Constrained Fuzzy Control for a Class of Nonlinear Distributed Process Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy differential games for nonlinear stochastic systems: suboptimal approach
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
New Chaotic PSO-Based Neural Network Predictive Control for Nonlinear Process
IEEE Transactions on Neural Networks
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The main focus of this paper is to develop an optimization method for the automatic fighter tracking (AFT) problem. The AFT problem is similar to a general evader-pursuer maneuvering automation problem between the dynamic systems of two highly interactive objects. This paper proposes a particle swarm optimizer-based variable feedback gain controller (PSO-based VFGC) for dealing with AFT problems. The PSO-based VFGC is designed to obtain the control value of a pursuer through an error-feedback gain controller. Once conditions of system closed-loop stability have been satisfied, the optimal feedback gains can be obtained through PSO, and the actual control values can be derived from the obtained values. Simulation results confirm the capabilities of the proposed method by comparing the results against two other methods in the field: the weight matrix value defined Ricatti equation, and the linear matrix inequality (LMI) based linear quadratic regulator (LQR). The performance of the proposed method is superior to that of its alternatives.