Adaptive Sliding Mode Path Tracking Control of Unmanned Rice Transplanter

Abstract

To decrease the impact of uncertainty disturbance such as sideslip from the field environment on the path tracking control accuracy of an unmanned rice transplanter, a path tracking method for an autonomous rice transplanter based on an adaptive sliding mode variable structure control was proposed. A radial basis function (RBF) neural network, which can precisely approximate arbitrary nonlinear function, was used for parameter auto-tuning on-line. The sliding surface was built by a combination of parameter auto-tuning and the power approach law, and thereafter an adaptive sliding controller was designed. Based on theoretical and simulation analysis, the performance of the proposed method was evaluated by field tests. After the appropriate hardware modification, the high-speed transplanter FLW 2ZG-6DM was adapted as a test platform in this study. The contribution of this study is providing an adaptive sliding mode path tracking control strategy in the face of the uncertainty influenced by the changeable slippery paddy soil environment in the actual operation process of the unmanned transplanter. The experimental results demonstrated that: compared to traditional sliding control methods, the maximum lateral deviation was degraded from 17.5 cm to 9.3 cm and the average of absolute lateral deviation was degraded from 9.1 cm to 3.2 cm. The maximum heading deviation was dropped from 46.7° to 3.1°, and the average absolute heading deviation from 10.7° to 1.3°. The proposed control method not only alleviated the system chattering caused by uncertain terms and environmental interference but also improved the path tracking performance of the autonomous rice transplanter. The results show that the designed control system provided good stability and reliability under the actual rice field conditions.

Improved Collision Avoidance Algorithm of Autonomous Rice Transplanter Based on Virtual Goal Point

“…The detailed structure of vehicle speed control unit can be seen in Ref. [27] and is not repeated here. Two six-axis attitude sensors and the electric steering wheel were connected to the VCR by the RS-232 serial ports, while the navigation module communicated with the navigation controller by the RS-485 serial ports.…”

Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

“…Figure 7c illustrates that when v = 5 m/s, the tracking effect is ranked as N pre (2) < N pre (3) > N pre (4) > N pre (5), where N pre = 3 indicates the tracking effect's optimality. Figure 7d illustrates that when v = 10 m/s, the tracking effect is ranked as N pre (4) < N pre (5) < N pre (6) > N pre (7), where N pre = 6 indicates the tracking effect's optimality. In summary, the selection of the value of Npre is correlated positively with the c ture of the reference path and velocity.…”

“…Path tracking control is at the heart of autonomous driving technology, which is widely used in farming, planting, management, harvesting, transportation, and other aspects of agricultural production to improve work quality and efficiency and to reduce the waste of agricultural production materials [2]. Typical path tracking control methods include geometric model-based PP [3] and Stanley [4], etc., kinematic and dynamic model-based PID [5], LQR [6], SMC [7], and MPC [8], etc., as well as model-free based fuzzy control [9], reinforcement learning [10], and neural networks [11], etc.…”

Overview of Sliding Mode Control Technology for Permanent Magnet Synchronous Motor System

“…Many literatures have adopted adaptive algorithms to adjust the parameters of the controller, and have achieved certain results, but there are still some improvements to be made [201,202]. The variable gain controller can be designed to adjust the controller parameters according to the state inside the sliding mode algorithm [144].…”