Using the chip MC9S12XS128, two-wheeled self-balancing robot control system is designed. Its posture information is detected by accelerometer MMA7260 and gyro NEC-03, multi inertial sensor data fusion is realized by Kalman filter, posture data optimal estimation is gotten, and the accuracy of posture sensing system is improved. Using integral separation PID control algorithm, controlling the left and right motors are accelerated or decelerated, self-balancing control of two-wheeled robot is achieved. The experimental results show that, using the hardware platform MC9S12XS128, Kalman filter algorithm has high efficiency and posture data fusion is accurate and reliable, requirements which are posture optimal estimation and inclination data real-time feedback are met, and the system is stable and can accurately and quickly realize self-balancing control of two-wheeled robot.

Because the sinter mixture moisture control system has time-varying, nonlinear and time-delay characteristics, it is difficult to establish its precise mathematical model. According to the defects that the conventional PID control in the fixed parameter is difficult to guarantee the system performance, this paper proposed the parameter self-tuning fuzzy PID controller which is used to the system. Based on the sintering furnace structural features and control performance requirements, the PID parameters are set by fuzzy logic reasoning. The actual operation results proved that the parameter self-tuning fuzzy PID controller improved more obviously the system performance than the original PID controller, and stabilized the sinter mixture moisture, and improved the sintering production condition and promoted the production capacity.

Aiming at the problem that it is difficult to predict the highway traveling passenger volume (HTPV), a new prediction model of HTPV based on wavelet neural network (WNN) is proposed. A case study is given to verify the proposed model. The simulation results show that the WNN model has higher convergence speed and prediction precision than the traditional BP neural network model (TBPNNM), and has more practical values.

Annular heating furnace temperature control is a typical and complex industrial process control system,with the characteristics of multivariable,time-varying parameters,nonlinear,coupling, large inertia and pure delay,so it is difficult to obtain satisfactory control effect when using conventional PID control. In view of the above problems, adopting fuzzy PID algorithm and cascade double-cross limiting control, combustion control effect is greatly improved, self-tuning of PID parameters is realized, and control quality is enhanced. The experimental results show that, fuzzy PID control shows good dynamic and steady performance, its control effect is significantly improved when compared with conventional PID control, and it has better overall performance.

The control principle of stepper motor which is widely used at present has been analyzed. It adopts 8051 single-chip microcomputer to achieve stepper motor the pulse distribution control, acceleration and deceleration control in the four phase eight beat mode. This study has avoided complex circuit design, solved the stepper motor "step", "overshoot" phenomenon, promoted step motor to be more stable, faster response, more accurate positioning. This control which has the versatility can be achieved by modifying the corresponding circuit and related procedures for different stepper motor. The control which is flexible and convenient, low cost, has the very high use value in the practical application.