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Aiming at the problem of inflexible steering and motion of robot, this paper applies fuzzy adaptive PID control algorithm to the differential steering control system of mowing robot to improve its rapidity and accuracy. In this paper, according to the domestic green environment and the overall parameters of the mowing robot, the overall design of the mowing robot is made, the fuzzy adaptive PID control algorithm of the mowing robot differential steering control system is designed, the mathematical model of the mowing robot differential steering is constructed, and the MATLAB/Simulink is used to simulate the fuzzy adaptive algorithm. The simulation results show that the response speed and overshoot of fuzzy adaptive PID algorithm are 2.42 s and 16.4, respectively, The response speed and overshoot of PID are 7.18 s and 36.3% respectively, so the dynamic performance of the differential steering control system based on Fuzzy Adaptive PID algorithm is better than that based on ordinary PID control.

Aiming at the difficulties in realizing the accurate control due to the nonlinearity of the automatic drilling system of oil drilling rig, a design scheme is proposed by giving a constant drilled-pressure to the rig for fuzzy control. Sampling error with changes in the signal was sent into the fuzzy controller, which turned the signal into a fuzzy volume. Subsequently, a precise volume was obtained accordingly and then added to an actuator for the motor control. According to the MATLAB simulation results, the response could be faster and more stable compared with the traditional control.

Aiming at the characteristics of large lag, strong interference, nonlinearity and difficult pressure control in pressure controlled drilling, this paper designs an automatic control system for pressure controlled drilling without model adaptive control. The automatic control system consists of signal input device, pressure controlled drilling electronic control unit, actuator and other devices. In order to improve the speed and stability of pressure control in pressure drilling, a model-free adaptive control scheme is proposed. The control scheme only needs on-line I/O data of the controlled system to complete the design of the controller, which has strong resistance to nonlinearity and is suitable for more complicated control projects. The system is modeled and simulated in MATLAB. The experimental results show that compared with cascade PID control, the pressure regulating time of the automatic control system of pressure controlled drilling using model-free adaptive control scheme is shorter and the pressure change curve is smoother, which can better meet the control requirements of the automatic control system of pressure controlled drilling.

At present, the detection of important components of nutrient solution in soilless culture is high cost, difficult and low precision. A soft measurement method of nutrient solution component based on deep belief network and extreme learning machine (DBN-ELM) is proposed. The component concentration in nutrient solution was selected as the dominant variable, and the variables that were easy to be measured and correlated with the ion concentration were the auxiliary variables, including PH value, conductivity, nutrient solution circulation speed and temperature. The deep belief network is used to extract the features of the auxiliary variables, then the extracted features are input into the ultimate learning machine for training, and the soft measurement model is obtained. Finally, the data of soil - free tomato culture nutrient solution was used to verify the experiment. The results show that this method has higher comprehensive measurement accuracy than the method using the extreme learning machine or the least square method, and is of great significance for improving the yield and quality of the soilless cultivated crops.

A four-wheeled autonomic trolley of obstacle avoidance is controlled by way of front-wheel steering and back- wheel driving. Three pairs of ultrasonic sensors are, respectively, installed on the left front, front and right front of the trolley in order to measure the distances between obstacles and the trolley. Its control strategy is drived from the concept of path planning for mobile robot based on fuzzy logic. The reference variables are the current location coordinates, X Y, and tp(the steering angle to target point), thus to find out the output steering angle (hp) of the front wheel.

This paper introduced the self-tuning fuzzy PID controller based on particle swarm optimization which aims to gain more precise control over the position of pneumatic proportional valve barrel, where particle swarm works to optimize the membership function, fuzzy rule and PID parameter in fuzzy control. The study fruits also include online optimization of the self-tuning fuzzy PID controller parameters. Comparing to the conventional control methodology, The self-tuning fuzzy PID controller with PSO optimization is proven to show better precision, dynamic performance index and more rapid tracking performance and robustness. To this end, step response was used to compare and analyze the results from the PSO algorithm optimization, providing a pragmatic method of better comprehensive performance for PID parameter optimization.

For the variable pitch grid-connected wind turbine maximum power tracking and improved power quality problems, using new double-fed machine constitutes a wind turbine, based on vector control and the whole fuzzy controller is used to achieve brushless doubly-fed variable speed constant frequency motor control. Self correct factor of fuzzy was introduced in the fuzzy controller design process. The fuzzy controller with parameters self-adjusting is changed different control requirements. Made below rated wind speed, according to wind speed changes, control of motor speed is used to obtain the best tip-speed ratio to achieve maximum power tracking and variable-speed constant frequency. Simulation results show that compared with conventional PID controller can more effectively reduce the vibration. Rapidly to achieve steady state, wind energy conversion system efficiency and quality is increased.

Constant pressure water supply system is a complex time-delay system which makes it difficult to ensure the good control performance and fast response in the working condition for traditional PID control. This paper presents a speed control system of constant pressure pump unit based on fuzzy self-tuning PID control strategy which can realize parameter tuning of PID real-timely. According to a real pumping station, a simulation model is established based on Matlab/Simulink. The simulation result shows that the fuzzy self-tuning PID controller possesses better rapidity of response, shorter adjusting time, and higher steady precision compared with traditional PID controller. Furthermore, fuzzy self-tuning PID control system has no overshoot and strong robustness. Thus, it is to be a good solution to complex pump-delay system problems.

Based on Variable Universe fuzzy control technology with good adaptive and robust capability, a new excitation control strategy for DFIG(doubly fed induction generator) without accurate generator mathematical model is proposed in this paper. Full fuzzy excitation control model is established and analyzed in detail. the whole wind generation system model Was established, simulation results indicate that the proposed methods is efectiveness for maintaining optimum power operating, even during generation system characters varying.

PID controller can not perform well in liquid level control system because liquid system has characteristics of time-delay and inertia. Based on the characteristics of two-tank liquid control system, an ADRC is designed by using control technique of active disturbance rejection. In order to study the control performance of ADRC, the comparative analysis with PID controller is to be carried out in the research. Simulation research shows that water level control system based on ADRC possesses better rapidity, less overshoot and strong disturbance inhibition compared with PID controller. Thus, the research result is of important guidance for engineering of water level control.

In order to most effectively utilize the wind energy and improve the efficiency of wind generation system, an optimum control strategy of doublyfed induction generators (DFIG) was proposed, which made the system operation for both the maximum wind enemy captured below the rated wind speed. Based on the wind turbine characteristics and basic electromagnetic relationship of DFIG the mathematical models of the stator active power and reactive power of DFIG were derived to fulfill maximal wind energy capture and conversion. A dual-passage excitation fuzzy control strategy based on dynamic synchronous reference frame was applied to control the proposed optimal stator active and reactive power. The operational performances of the wind turbine system with DFIG with wind speed variation were analyzed and compared by using Matlab/Simulink . The results show the correctness and feasibility of the proposed control strategy.

PID controller can not perform well in two-order large Inertia and time-delay system. ADRC is a new control technique which possesses special advantages for large Inertia and time-delay system. Moreover, ADRC overcomes lots of defects of PID control and improves control performance greatly. Two-tank liquid system — the research object in this paper is a typical two-order large Inertia and time-delay system. In the research an ADRC is designed by using control technique of active disturbance rejection and by comparing with PID controller. Simulation research shows that liquid level control system possesses good rapidity and little overshooting with ADRC and can inhibit strong disturbance. Thus, the research result is of important guidance for engineering of liquid level control.