During the variable spray process, the micro-flow control is often held back by such problems as low initial sensitivity, large inertia, large hysteresis, nonlinearity as well as the inevitable difficulties in controlling the size of the variable spray droplets. In this paper, a novel intelligent double closed-loop control with chaotic optimization and adaptive fuzzy logic is developed for a multi-sensor based variable spray system, where a Bang-Bang relay controller is used to speed up the system operation, and adaptive fuzzy nonlinear PID is employed to improve the accuracy and stability of the system. With the chaotic optimization of controller parameters, the system is globally optimized in the whole solution space. By applying the proposed double closed-loop control, the variable pressure control system includes the pressure system as the inner closed-loop and the spray volume system as the outer closed-loop. Thus, the maximum amount of spray droplets deposited on the plant surface may be achieved with the minimum medicine usage for plants. Multiple sensors (for example: three pressure sensors and two flow rate sensors) are employed to measure the system states. Simulation results show that the chaotic optimized controller has a rise time of 0.9 s, along with an adjustment time of 1.5 s and a maximum overshoot of 2.67% (in comparison using PID, the rise time is 2.2 s, the adjustment time is 5 s, and the maximum overshoot is 6.0%). The optimized controller parameters are programmed into the hardware to control the established variable spray system. The experimental results show that the optimal spray pressure of the spray system is approximately 0.3 MPa, and the flow rate is approximately 0.08 m3/h. The effective droplet rate is 89.4%, in comparison to 81.3% using the conventional PID control. The proposed chaotically optimized composite controller significantly improved the dynamic performance of the control system, and satisfactory control results are achieved.

The mowing robots work with a multivariable strong coupling underactuated system that is mostly troubled by difficulty controlling and unsatisfactory accuracy. Especially, the frequent external disturbances and parameter changes are likely to get missed and heavy cutting. In this paper, a new trajectory tracking control method based on extended state observer (ESO) is introduced with a particular focus on dual closed-loop sliding mode. Firstly, from the perspective of kinematics, a speed assistant controller was designed to generate the speed control quantity, and secondly, a sliding mode control algorithm based on the improved Fractional Power Rate Reaching Law (IFPRRL) was programmed to control the drive motor that tracked the speed control quantity. By means of comparison, our improved algorithm presented faster arrival time and better robustness along with similar jittering. At the same time, the robustness of the system was further enhanced with the help of an optimized ESO to tackle unmodeled disturbances and uncertain disturbances during the operation. Finally, the experimental analysis of the motor drive circuit and the trajectory tracking control system of the lawn mowing robot were both carried out respectively. The analysis shows that the performance of the proposed reaching law sliding mode control algorithm had some new pleasing changes, such as adjustment time and robustness. The circular trajectory and the detour mowing trajectory were respectively tracked in the double closed-loop sliding mode designed in this paper. The experimental goal was to ensure that the error vector Pe = (x Axis position error xe, y Axis position error ye, Angle error θe) all remaining at (0.01m, 0.01m, 0.01rad) were 5.34s and 5.36s, respectively, and both could be finally converged to 0. The results show that the newly developed controller based on ESO presented smaller arrival time and stronger robustness. The dual-closed-loop control of sliding-mode trajectory tracking method was capable to meet the real-time and precision requirements of the lawnmower robot for quick trajectory tracking.