Adaptive Sliding Mode Path Tracking Control of Unmanned Rice Transplanter

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