Magnetic suspension systems are highly nonlinear and essentially unstable systems. In this thesis, we utilize a phase-lead controller operating in the inner loop to stabilize the magnetic suspension system at first. Furthermore, we design a fuzzy PID controller operating in the outer loop to overcome the nonlinearity and to improve the system¡¦s performances. Because of setting the parameters in traditional fuzzy PID is a long-winded trial and error, so we adopt non-binary modified adaptive genetic algorithms to help us finding the parameters of fuzzy PID controller. As to the experimental implementation, we set two situations in our experiment test: (1) we utilize fuzzy PID controller with initial voltage to test the positions control, and eliminate the extra disturbance. And, (2) we utilize fuzzy PID controller without initial voltage to control the position of suspension object. For the experimental results, we obtain that the designed fuzzy PID controller not only increases the system¡¦s operating range, but also positions accurately and rapidly, and it meanwhile can eliminate the extra disturbance.

In this study, the non-binary coding, elitist strategy, increasing mutation rate, extinction, and immigration strategy are used to improve the simple genetic algorithms. The improved search technique can reduce the possibility of falling into the local optimum due to the premature convergence in a large searching space, and increase the chance of finding out the near-optimal parameters. The hydraulic forming machine used in this thesis, includes a power source of a hydraulic motor and a actuator of two hydraulic cylinders. Both the internal pressure and axial force are controlled to hydroform the tubes into the shapes we want. The PID fuzzy logic controller is implemented to control the proportional direction valve and pressure reducing valve of this dual-cylinder electro-hydraulic system so that the loading path can follow the optimal forming curve of axial-feeding and pressure prescribed. From the experimental results, it is clear that the near-optimal PIDFLC controller designed via modified genetic algorithms can make the loading path follow the prescribed curve, and effective for reducing system uncertainty caused by the varying loads and system unstability resulting from the nonlinear characteristics of the hydraulic system.

Abstract This thesis studies on the design of hybrid fuzzy PID controller via genetic algorithms and the position control of linear DC motors. Due to the high precision and high speed positioning ability, linear DC motors have been widely used in many fields. However, with the higher requirements of positioning accuracy, the effect of nonlinear friction becomes very significant. Because of the large difference between dynamic friction in macrodynamic region and static friction in microdynamic region, we design the two-stage controller for positioning in macrodynamic and microdynamic stage individually. In the macrodynamic stage, we use the hybrid fuzzy PID controller and finding the optimal membership functions and scaling factors of the controller via adaptive genetic algorithms to enhance performance of the system. A novel formula for calculating adaptive crossover and mutation rate is also presented. Since it is not easy to establish a precise static friction model in the microdynamic region, the relay feedback method is adopted to design PID controllers. Finally, through computer simulations and experiments, it is obviously that the performance of the proposed controllers is satisfactor