Development of a Depth Control System Based on Variable-Gain Single-Neuron PID for Rotary Burying of Stubbles

Rotary burying by tractor-hitched rotary tillers is a common practice in southern China for treating rice stubbles. Currently, it is difficult to maintain stable tillage depths due to surface unevenness and the residual stubbles in the field, which leads to unstable tillage quality and nonuniform crop growth in later stages. In this study, an RTK-GNSS was used to measure the real-time height and roll angle of the tractor, and a variable-gain single-neuron PID control algorithm was designed to adjust the coefficients (KP, KI, and KD) and gain K in real-time according to the control effects. An on-board computer sent the angles of the upper swing arm u(t) to an STM32 microcontroller through a CAN bus. Compared with the current angle of the upper swing arm, the microcontroller controlled an electronic-control proportional hydraulic system, so that the height of the rotary tiller could be adjusted to follow the field undulations in real-time. Field experiments showed that when the operation speed of the tractor-rotary tiller system was about 0.61 m/s, the variable-gain single-neuron PID algorithm could effectively improve the stability of the working depth and the stubbles’ burying rate. Compared with a conventional PID controller, the stability coefficient and the stubbles’ burying rate were improved by 5.85% and 4.38%, respectively, and compared with a single-neuron PID controller, the stability coefficient and the stubbles’ burying rate were improved by 4.37% and 3.49%, respectively. This work controlled the working depth of the rotary tiller following the changes in the field surface in real-time and improved the stubbles’ burying rate, which is suitable for the unmanned operation of the rotary burying of stubbles in the future.

Sliding Mode Self-Tuned Single Neuron PID Controller for Power System Stabilizer

In this paper, a modified technique based on the combination of the Single Neuron PID (SNPID), as the main controller and Sliding Mode Control (SMC), as an adaptation technique, to design an optimized self-tuned for SNPID controller that may overcome difficulties faced when a change in system operating points occurs. The proposed approach has been implemented as a power system stabilizer (PSS) for a synchronous generator connected to an infinite bus. The Flower Pollination (FP) optimization is based on an appropriate objective function. To demonstrate the effectiveness of the combination obtained controllers, PSS, is tested under different operating conditions. The combination controllers are shown through uncertainties system parameters changes under different disturbances. The results show the ability of the suggested controllers to enhance well the system performances

Feedforward Compensation Analysis of Piezoelectric Actuators Using Artificial Neural Networks with Conventional PID Controller and Single-Neuron PID Based on Hebb Learning Rules

This paper presents a deep analysis of different feed-forward (FF) techniques combined with two different proportional-integral-derivative (PID) control to guide a real piezoelectric actuator (PEA). These devices are well known for a non-linear effect called “hysteresis” which generates an undesirable performance during the device operation. First, the PEA was analysed under real experiments to determine the response with different frequencies and voltages. Secondly, a voltage and frequency inputs were chosen and a study of different control approaches was performed using a conventional PID in close-loop, adding a linear compensation and a FF with the same PID and an artificial neural network (ANN). Finally, the best result was contrasted with an adaptive PID which used a single neuron (SNPID) combined with Hebbs rule to update its parameters. Results were analysed in terms of guidance, error and control signal whereas the performance was evaluated with the integral of the absolute error (IAE). Experiments showed that the FF-ANN compensation combined with an SNPID was the most efficient.