Earticle

It goes without saying that, there is quite a diverse mixture of the linear controller. Nevertheless, I assume the most famous would probably be Proportional Integral (PI) controller. The things with PI controller are that most of PI controllers are reduction the error. As well as PI controllers, another kind of linear controller worth mentioning could be Proportional Derivative (PD) controller and the unique characteristic of PD controller is that PD controllers are high speed and independent of system’s dynamic modeling. In addition, there are the usual things like PID controllers and PI2D controllers. The main objective of this paper designs a minimum delay proportional-derivative (PD) controller to the control of first order delay (FOD) system. First order delay system has delay time about 4 seconds in certain and about 19 seconds in limited uncertain condition. To improve the flexibility, design high-speed and low-cost controller, the micro-electronic device (FPGA-Based) controller is used in this research. The proposed design is 30-bits FPGA-based controller for inputs and 35-bits for output. In this research, the maximum frequency is 63.629 MHz and the minimum period is 15.716 ns, the minimum input arrival time before the clock is 4.362 ns and the maximum output required time after clock is19.727 ns. In this algorithm, the delay time for the derivative algorithm is 15.526 ns which 87.8% is a logic delay and 12.2% is route delay.

In this paper, a PD control algorithm based on fuzzy logic dead compensation has been proposed. The fuzzy logic dead zone compensator was used to compensate the dead zone which existed in the mechanical system, PD control scheme was used to control trajectory of the joints of robot, so that the stability and robustness can be improved. In order to verify the validity of the control algorithm, we realized the proposed adaptive control strategy in MATLAB. The simulation results has revealed that:comparing with the traditional PD control scheme, the proposed PD control algorithm based on fuzzy logic dead compensation can effectively compensate the dead zone which existed in the mechanical system, also the position tracking performance and precision of the manipulator has been enhanced.

The appropriate choice of crossover and mutation rates is critical to the success of genetic algorithms. Earlier researches focused on finding optimal non-dynamic crossover or mutation rates, which vary for different problems, and different stages of the genetic process in a problem. This paper proposed an ameliorate adaptive genetic algorithm where the mutation and crossover rates are adapted dynamically based on the evaluation results of the respective offspring in the next generation. Simultaneously, we combined this new algorithm with PD controllers to improve the stability of control systems. The experimental results are compared with other researchers’ approaches in this field. The PD-GA method can also significantly accelerate the system response and induce lower overshoot as well.

In this paper are proposed 2nd-order PD-type iterative learning control algorithms for linear continuous-time system and linear discrete-time system. In contrast to conventional methods, the proposed learning algorithms are constructed based on both time-domain performance and iteration-domain performance. The convergence of the proposed learning algorithms is proved. Also, it is shown that the proposed method has robustness in the presence of external disturbances and the convergence accuracy can be improved. A numerical example is provided to show the effectiveness of the proposed algorithms.

This paper suggests a design method of the robust model following PD control system using genetic algorithm. This PD control system is designed by applying genetic algorithm with reference model to the optimal determination of proportional and derivative gains that are given by PD servo controller. These proportional and derivative gains are optimized simultaneously in the search domain guaranteeing the robust stability of closed-loop system satisfying different stability margins. The effectiveness of this PD control system is verified by computer simulation.