Adaptive Control of a Farm Tractor with Varying Yaw Properties Accounting for Actuator Dynamics and Nonlinearities

Abstract

Two adaptive control algorithms for the automatic steering control of a farm tractor with varying hitch forces are developed. Tractors can be configured many different implements, and implements interact with the soil in various ways. These variations cause the yaw dynamics to change with respect to different implements and soil conditions; therefore, this thesis uses a model reference adaptive (MRAC) control law to compensate for different implement configurations. Models are described and analyzed for the steering actuator, yaw rate plant, and lateral position plant. It is shown that the DC gain of the steering angle to yaw rate transfer function is the model parameter that changes the most with hitch loading. In order to develop the adaptive control algorithm, a cascaded controller is first implemented with three feedback loops containing the steering angle, yaw rate, and lateral position measurements. Controllers are designed for each subsystem, and root locus analysis is used to describe the stability and performance characteristics.

Two MRAC algorithms are derived to compensate the loop gain and feed-forward gain of the yaw rate controller to account for changes in the yaw rate plant. The two algorithms are named the model reference adaptive control loop gain adaptation (MRAC-LGA) algorithm and the model reference adaptive control feed-forward gain adaptation (MRAC-FGA) algorithm. Simulations are presented that show that the algorithms perform poorly due to neglected steering actuator properties. Both algorithms are modified to account for the steering actuator properties, and more simulations are presented that demonstrate satisfactory performance. Experimental results are presented for the LGA algorithm, and issues with experimental implementation are discussed. Next, experimental results are presented for the FGA algorithm that show improved performance over the LGA algorithm. Finally, experimental tests further validate that the FGA algorithm improves lateral error performance versus a fixed-gain controller.