Spatial Path Tracking Controllers for Autonomous Ground Vehicles:Conventional and Nonconventional Schemes

Unlike time-based path tracking controllers,theε-controller is a spatial path tracking controller.It is a purely geometric path tracking controller and essentially a P-controller to maintain the reasonable spatial distance,ε,from the vehicle to the desired path.In this paper,we present some enhancement schemes using the non-conventional PI control laws via optimization.We propose to use a nonlinear termε^(1/3)for the proportional controller.A fractionalorder integral used to achieve a PI^(α)control.Among the schemes,an optimization search procedure applied to-nd optimal controller gains by meshing the regions around the values from approximate linear designs.The performance index for parametric optimization is the integration of the absolute purely spatial deviation from the desired path.Three different types of road shape were chosen and the Gazebo-ROS simulation results were presented to show the effectiveness of the proposed enhancement schemes.The results show that in some cases a smaller J_(area)and O_(ι)can be achieved by using P^(1/3)controller,but its disadvantage is there may be some oscillation.For PI^(α)controller,there is an additional adjustable parameterα,better performance can be achieved without signi-cant disadvantages which is worth in-depth research.

Recurrent neural networks-based multivariable system PID predictive control

A nonlinear proportion integration differentiation(PID)controller is proposed on the basis of recurrent neural networks,due to the difficulty of tuning the parameters of conventional PID controller.In the control process of nonlinear multivariable system,a decoupling controller was constructed,which took advantage of multi-nonlinear PID controllers in parallel.With the idea of predictive control,two multivariable predictive control strategies were established.One strategy involved the use of the general minimum variance control function on the basis of recursive multi-step predictive method.The other involved the adoption of multi-step predictive cost energy to train the weights of the decou-pling controller.Simulation studies have shown the efficiency of these strategies.