Neural PI Controller Research Articles

Modelling and optimisation of active front wheel steering system control for armoured vehicle for firing disturbance rejection

While firing on the move, the handling performance of an armoured vehicle is affected, thus causing it to lose its directional stability. This is due to an impulse force generated at the centre of the gun turret, which can produce an unwanted yaw moment at the centre of gravity of the armoured vehicle. In order to reject the unwanted yaw moment, a new hybrid control strategy known as Neural-PI controller had been introduced by combining neural network system and conventional PI controller. This paper developed 14 DOF of armoured vehicle and 2 DOF of Pitman arm steering system. Other than that, determination of the most suitable activation function to be implemented in the Neural-PI controller has been carried out and optimised by using the Genetic Algorithm (GA) method. The performance of the controller was evaluated by comparing the conventional PI controller with the Neural-PI controller implemented with different activation functions.

Modelling and optimisation of active front wheel steering system control for armoured vehicle for firing disturbance rejection

While firing on the move, the handling performance of an armoured vehicle is affected, thus causing it to lose its directional stability. This is due to an impulse force generated at the centre of the gun turret, which can produce an unwanted yaw moment at the centre of gravity of the armoured vehicle. In order to reject the unwanted yaw moment, a new hybrid control strategy known as Neural-PI controller had been introduced by combining neural network system and conventional PI controller. This paper developed 14 DOF of armoured vehicle and 2 DOF of Pitman arm steering system. Other than that, determination of the most suitable activation function to be implemented in the Neural-PI controller has been carried out and optimised by using the Genetic Algorithm (GA) method. The performance of the controller was evaluated by comparing the conventional PI controller with the Neural-PI controller implemented with different activation functions.

A new control method based on type-2 fuzzy neural PI controller to improve dynamic performance of a half-bridge DC–DC converter

Due to nonlinear behavior and the uncertainty with converter structure, controller design is difficult and accompanied with complexities. In this paper, a new control method based on Type-2 fuzzy neural PI (T2FN PI) controller is applied to improve the dynamic response of half-bridge DC–DC converters for different operation conditions. The T2FNN PI controller amends the specifications of the converter system by controlling its duty cycle of switching. In addition, proposed controller can property handle the uncertainty which is associated to converter structure, measuring devices and measured control signals.The T2FN PI controller is a combination of type-2 fuzzy linguistic process and neural network learning capability. The back-propagation algorithm is applied to train the parameters of T2FNN PI controller. Furthermore, PSO algorithm is applied to obtain the T2FN PI training pattern.In order to evaluate the performance of the proposed T2FN PI controller, it has been compared with a Fuzzy controller and the conventional PI controller for five different operating conditions. The simulation results show efficiency of the T2FNN PI controller in compare to Fuzzy controller and PI controller in terms of better rejection of disturbances, faster transient response and less peak overshoot.

대부하 표적지향 시스템의 정밀 제어 기법 연구

In this study, the performance of a heavy load pointing system has been investigated. The PI controller are being widely used in industrial application because of simple, cheap, and excellent performance. However, the requirement for control precision becomes higher and higher, as well as the plants becomes more and more complex. In order to achieve the satisfied control performance, we have to consider the affection of nonlinear factor contained in plant. In this paper, the neural-PI control law have been evaluated. The proposed controller is compared with the existing controllers through simulations, and the results show that the pointing accuracy of the proposed control system is improved against the disturbance induced by vehicle running on the bump course.