Nonlinear optimal control for autonomous navigation of a truck and trailer system

Rigatos, Gerasimos, Siano, P., Wira, P., Busawon, Krishna and Binns, Richard (2017) Nonlinear optimal control for autonomous navigation of a truck and trailer system. In: ICAR 2017 - 18th International Conference on Advanced Robotics, 10th - 12th July 2017, Hong Kong, China.

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A solution to the problem of autonomous navigation of a truck and trailer system is developed using a nonlinear optimal control method. The kinematic model of the autonomous vehicle undergoes linearization through Taylor series expansion. The linearization is performed at a temporary operating point (equilibrium) that is re-computed at each time instant using the present value of the state vector and the last value of the control inputs vector. The linearization is based on the Jacobian matrices of the model. For the linearized equivalent model of the truck and trailer system an H-infinity feedback controller is designed. The feedback control gain is obtained from the solution of an algebraic Riccati equation at each iteration of the control algorithm. The stability of the control loop is proven with Lyapunov analysis. It is demonstrated that the control loop exhibits the H-infinity tracking performance which signifies elevated robustness against modelling errors and external disturbances. Moreover, under moderate conditions the global asymptotic stability of the control loop is assured. Finally, to implement state estimation-based control for the autonomous vehicle, through the processing of a small number of sensor measurements, the H-infinity Kalman Filter is proposed.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Jacobian matrices, Kinematics, Cost function, Mathematical model, Feedback control, Asymptotic stability, Optimal control
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Paul Burns
Date Deposited: 14 Nov 2018 16:57
Last Modified: 11 Oct 2019 18:30

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