Robust H Network Observer-Based Path Tracking Control of an Autonomous Ground Vehicle

PROJECT TITLE :

Robust H∞ Network Observer-Based Attack-Tolerant Path Tracking Control of Autonomous Ground Vehicle

ABSTRACT:

Under the influence of external disturbance, measurement noise, and actuator/sensor attack signals, a robust $H infty $ network observer-based attack-tolerant path tracking control design is proposed for the autonomous ground vehicle (AGV) in this study. In the beginning, a more realistic AGV system is utilized in order to describe the interaction between the longitudinal speed, the lateral speed, and the yaw rate. The information from the local AGV is sent to the remote control center via wireless channel and is based on Controller Area Network (CAN). The remote control center can then calculate the control command based on the information it has received. In order to prevent the actuator/sensor attack signal from becoming corrupted as a result of an insecure CAN, two novel smoothed signal models have been developed to describe these attack signals. These models have been embedded within the AGV dynamics system as an augmented system. After that, the conventional Luenberger-type observer of the augmented system is able to simultaneously estimate these attack signals along with the AGV system state. A robust $H infty $ network observer-based attack-tolerant path tracking controller is constructed by making use of estimated state and attack signals. The goal of this construction is to attenuate the effect of unknown disturbance on the energy of path tracking error and eliminate the influence of attack signals. The design conditions of a robust $H infty $ network observer-based attack-tolerant path tracking control design for an automated guided vehicle (AGV) are derived in terms of a set of nonlinear difference inequalities with the assistance of a convex Lyapunov function. The Takagi-Sugeno fuzzy interpolation method is applied to approximate the nonlinear AGV system, and the design can be simplified to a set of LMIs, each of which can be easily solved by using the LMI TOOLBOX in MATLAB. This helps to reduce the amount of difficulty involved in solving these nonlinear difference inequalities. A simulation example of an AGV performing a double lane change task within CAN is given in order to illustrate the design procedure and validate the effectiveness of the proposed design method in comparison to the conventional steering control method.

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