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This can result in entirely new gene values being added to the gene pool. Mutation:In this process one or more indivisual values in a chromosome are altered from its initial state. Reproduction may proceed in three stages as follows: (1) two newly reproduced strings are randomly selected from a Mating Pool (2) a number of crossover positions along each string are uniformly selected at random and (3) two new strings are created and copied to the next generation by swapping string characters between the crossover positions defined before. In order to avoid the best individuals of current population missing in the next generation due to destruction influence of crossover and mutation or selection error, De Jong put forward to the cream choice strategy Ĭrossover:This operation is the process of mimicking gene recombination of natural sexual reproduction, through combining the genetic information of two gens to create a new offspring contining more complicated gene structur. At present, there mainly are: sequencing choice, adaptive value proportional choice, tournament choice and so on. Selection:This step includes choosing individual genomes with high adaptive value from the current population to create mating pool. The procedure of GA approach includes three basic steps: selection, crossover and mutation, that constitute the main core of GA with powerful searching ability. This optimization method is utilized to obtain an optimum global solution for more control and manipulating problems. GA is a global search optimization technique bases on the strategy of natural selection. In this study, GA tuning approach has been invoked to tune the gain matrix of LQR controller used to approximate the gain parameters of PID controller for 3DOF helicopter system. Simulations were implemented utilizing Matlab programming environment to verify the efficiency and effectiveness of the proposed hybrid control method. In the proposed system the classical PID and optimal LQR controller have been combined to formulate a hybrid controller system. To this end an improvement in the system performance has been achieved in both the transient and steady-state responses. The intent of this study is to design a new hybrid PID controller based on an optimal LQR state feedback controller for stabilization of 3DOF helicopter system. In a new PID and LQR control system was proposed to improve a nonlinear quarter car suspension system. The gain parameters of the hybrid controller is tuned using the Grey-Wolf Optimizer (GWO). introduced a hybrid LQR-PID controller to regulate and monitor the locomotion of a quadruped robot. They adopted a conventional multi-variable PID and LQR algorithm for reducing cross-regulation in DC-to-DC converters. A new hybrid control algorithm was presented by Lindiya et al. The presented hybrid controller is optimized by Particle Swarm Optimization (PSO) to compute the gain parameters of the PID controller. proposed a new LQR-PID controller to obtain an optimal load sharing of an electrical grid. Many studies have proposed to develop a new hybrid PID controller with ability to provide better and more robust system performance in terms of transient and steady-state responses over the standard PID controllers. The control of complex dynamic systems using classic PID controllers is considered as a big challenge, where the stabilization of these systems requires applying a more robust controller technique. PID controllers are used successfully for single-input single-output (SISO) and linear systems due to their good performance and can be easily implemented. PID is regarded as the standard control structure of classical control theory. An improvement in the performance of the hybrid LQR-PID controller is achieved by using Genetic Algorithm (GA) which, is adopted to obtain best values of gain parameters for LQR-PID controller. This chapter focuses on design and simulation a hybrid LQR-PID controller used to stabilize elevation, pitch and travel axes of helicopter system. The strategy of the hybrid controller is based on the idea that the parameters of the PID controller are calculated using gain elements of LQR optimal controller. In the last decade, hybrid control strategies are developed by researchers using conventional PID controllers with other controller techniques such as Linear Quadratic Regulator (LQR) controllers. Extending the using of PID controller for complex dynamical systems has attracted the attention of control engineers. This technique can be successfully applied to control the behavior of single-input single-output (SISO) systems. Proportional Integral Derivative (PID) is the most popular controller that is commonly used in wide industrial applications due to its simplicity to realize and performance characteristics.