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In this research we develop a Real-time face tracking system by single pan-tilt camera. The system includes face detection, deformable template tracking and motion control. We refer a method to search the facial features by using the genetic algorithm searching technique, the learning algorithm for face detector is based on AdaBoost. In the face tracking, we refer a tracking way to combine with detection and tracking. In the pan-tilt camera control part, two fuzzy logic controllers are designed to control the tracking and handling of moving face. We achieve a more robust tracking way than the single-template by renewing face-template continuously. Finally in our tests, the system can track the face of people in 30-frame per second under complex environment by using the personal computer.

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The speed of fuzzy controllers implemented on dedicated hardware is adequate for control of any physical process, but too slow for todays high-complexity data networks. Defuzzification has been the bottleneck for fast implementations due to the large number of computationally expensive multiplication and division operations. In this thesis, we propose a high-speed fuzzy inferential system based on log-domain arithmetic, which only requires addition and subtraction operations. The system is implemented on a Xilinx Virtex-II FPGA with a processing speed of 67.6 MFLIPS having a maximum combinational path delay of 4.2 ns. It is a clear speedup compared to the reported fastest 50 MFLIPS implementation. A pipelined version of the controller is also implemented, which achieves a speed of 248.7 MFLIPS. Although a small approximation error is introduced, software simulation and hardware implementation on FPGA confirm high similarity of the outputs for control surfaces and a number of second-order plants. / Software Engineering and Intelligent Systems

This thesis presents a fuzzy logic controller aimed at maintaining constant tension between two adjacent stands in tandem rolling mills. The fuzzy tension controller monitors tension variation by resorting to electric current comparison of different operation modes and sets the reference for speed controller of the upstream stand. Based on modeling the rolling stand as a single input single output linear discrete system, which works in the normal mode and is subject to internal and external noise, the element settings and parameter selections in the design of the fuzzy controller are discussed. To improve the performance of the fuzzy controller, a dynamic fuzzy controller is proposed. By switching the fuzzy controller elements in relation to the step response, both transient and stationary performances are enhanced. To endow the fuzzy controller with intelligence of generalization, flexibility and adaptivity, self-learning techniques are introduced to obtain fuzzy controller parameters. With the inclusion of supervision and concern for conventional control criteria, the parameters of the fuzzy inference system are tuned by a backward propagation algorithm or their optimal values are located by means of a genetic algorithm. In simulations, the neuro-fuzzy tension controller exhibits the real-time applicability, while the genetic fuzzy tension controller reveals an outstanding global optimization ability.

This report presents a new way of control engineering. Dc motor speed controlled by three controllers PID, pole placement and Fuzzy controller and discusses the advantages and disadvantages of each controller for different conditions under loaded and unloaded scenarios using software Matlab. The brushless series wound Dc motor is very popular in industrial application and control systems because of the high torque density, high efficiency and small size. First suitable equations are developed for DC motor. PID controller is developed and tuned in order to get faster step response. The simulation results of PID controller provide very good results and the controller is further tuned in order to decrease its overshoot error which is common in PID controllers. Further it is purposed that in industrial environment these controllers are better than others controllers as PID controllers are easy to tuned and cheap. Pole placement controller is the best example of control engineering. An addition of integrator reduced the noise disturbances in pole placement controller and this makes it a good choice for industrial applications. The fuzzy controller is introduce with a DC chopper to make the DC motor speed control smooth and almost no steady state error is observed. Another advantage is achieved in fuzzy controller that the simulations of three different controllers are compared and concluded from the results that Fuzzy controller outperforms to PID controller in terms of steady state error and smooth step response. While Pole placement controller have no comparison in terms of controls because designer can change the step response according to nature of control systems, so this controller provide wide range of control over a system. Poles location change the step response in a sense that if poles are near to origin then step response of motor is fast. Finally a GUI of these three controllers are developed which allow the user to select any controller and change its parameters according to the situation.

This thesis presents a fuzzy logic controller aimed at maintaining constant tension between two adjacent stands in tandem rolling mills. The fuzzy tension controller monitors tension variation by resorting to electric current comparison of different operation modes and sets the reference for speed controller of the upstream stand. Based on modeling the rolling stand as a single input single output linear discrete system, which works in the normal mode and is subject to internal and external noise, the element settings and parameter selections in the design of the fuzzy controller are discussed. To improve the performance of the fuzzy controller, a dynamic fuzzy controller is proposed. By switching the fuzzy controller elements in relation to the step response, both transient and stationary performances are enhanced. To endow the fuzzy controller with intelligence of generalization, flexibility and adaptivity, self-learning techniques are introduced to obtain fuzzy controller parameters. With the inclusion of supervision and concern for conventional control criteria, the parameters of the fuzzy inference system are tuned by a backward propagation algorithm or their optimal values are located by means of a genetic algorithm. In simulations, the neuro-fuzzy tension controller exhibits the real-time applicability, while the genetic fuzzy tension controller reveals an outstanding global optimization ability.

Abstract We use the non-binary coding ,elitist strategy, increasing mutation rate, extinction, and immigration strategy to improve the simple genetic algorithms in this study. We expect that the search technique can avoid falling into the local optimum due to the premature convergence, and purse the chance that finding the near-optimal parameters in the larger searching space could be obviously increased. The accelerometer is then taken as the feedback sensor for output measurement, and the designed actuator and the PID fuzzy logic controller (PIDFLC) is implemented to actively suppress the vibration of the supporting mechanism that is due to the excitation effect of the high-speed and precision positioning action of the linear motor. From the computer simulations and the experimental results, it is obvious that the near-optimal PIDFLC controller designed by modified genetic algorithms can improve the effect of the vibration suppression; the settling time is also decrease. For the vibration suppressions of high-speed precision positioning problems, the vibrating supporting mechanism can quickly be stabilized.

Abstract Magnetic suspension systems are highly nonlinear and essentially unstable systems. In this thesis, we facilitate the position control problem for the DC electromagnetic suspension system. We utilize a phase-lead controller operating in the inner loop to stabilize the system first, and try to design a PID fuzzy logic controller (PIDFLC) operating in the outer loop to overcome the nonlinearity of the system and to improve the system¡¦s performance. Since the work of setting fuzzy control parameters is a long-winded trial and error, we adopt non-binary modified GAs to help us setting and optimizing parameters. As experimental results show that the designed PIDFLC not only increases the system¡¦s operating range, but also positions accurately and rapidly; meanwhile, it has the ability to eliminate extra disturbance. In addition, comparing with other control theories, the control method which we utilize is easier to be implemented.

This thesis deal with improvement behavior of temperature control in bioreactor. It was created model of system. Basic PI controller could solve all problems. After analysis of system were decided to used fuzzy controller. Fuzzy controller in comparison with classic PI controller has better behavior in this application.