Robust adaptive control using a universal approximator with application to vehicle motion control for ivhs (1997)

This dissertation is concerned with the robust adaptive control of a class of nonlinear systems in the presence of parametric uncertainties and dominant uncertain nonlinearities. A new approach, robust adaptive control using a universal approximator (RACUA), is proposed.
The proposed RACUA utilizes the robust adaptive control (RAC) by backstepping designs to guarantee uniform boundedness and convergence of tracking errors. In addition, a universal approximator is used to reduce the model uncertainties coming from uncertain nonlinearities and to improve tracking performance. An adaptive fuzzy logic system is used as a universal approximator. Therefore, this approach effectively combines the design techniques of RAC by backstepping and adaptive fuzzy logic control (AFLC), and provides several attractive features. Specifically, the approach does not require the matching condition imposed on control systems, and provides boundedness of tracking errors under poor parameter adaptation. These are standard features of RAC by backstepping. Furthermore, the universal approximator makes it possible to reduce the size of the residual set of tracking errors without using a high gain in the feedback loop. RACUA can be constructed for a class of single-input single-output (SISO) and multi-input multi-output (MIMO) nonlinear systems, transformable to a parametric-strict-feedback or a parametric-pure-feedback form.
This method is applied to vehicle motion control for intelligent vehicle highway systems (IVHS). In general, the success of vehicle motion control depends on the accuracy of the velocity estimates. For the estimation of vehicle velocities, a reliability indexed sensor fusion (RISF) technique is proposed in an adaptive Kalman filter framework to increase the estimation accuracy and to handle sensor failure situations. The noise covariance is used as a reliability index for each sensor because filter gain is determined by the noise covariance. Two different methods to determine unknown noise statistics are presented.
The RACUA algorithm is applied to the longitudinal motion control of vehicles via longitudinal traction force control for IVHS. A new coordination algorithm for engine and brake torque is proposed. In this coordination, a synchronous engine torque is applied during braking to partly solve the lack of control input. To deal with unknown longitudinal traction forces, which are dominant terms in vehicle longitudinal dynamics, the nominal traction force concept is proposed. According to this concept, a well-defined nominal traction force model is utilized in place of unknown actual traction force in the design of controllers. The difference between the nominal and the actual traction forces is approximated by adaptive fuzzy logic systems in the RACUA scheme.
For combined control of longitudinal and lateral motion of vehicles, a unified and global design procedure is proposed with proper coordination of the steering and the accelerating/braking actions of the vehicles. Unlike the previous research, the linearized traction force assumption is removed to extend the applicable region of the controller. To simplify the problem, the traction forces are controlled instead of the wheel slips. The nominal traction force concept is used to handle the uncertain traction forces. Controllers are designed for three different classes of the vehicle actuating systems: the properly-actuated system, the over-actuated system, and the under-actuated system. This classification is dependent on the various vehicle chassis control systems designed for active safety. Simulations show that the proposed control algorithm achieves stable and robust vehicle maneuvers in a wider range of vehicle operations than the previous control algorithms.