Data-Driven Modeling and Regulation of Aircraft Brakes Degradation via Antiskid Controllers

In ground vehicles, braking actuator degradation and tire consumption do not represent a significant maintenance cost as the lifespan of both components, at least in common situations, is rather long. In the aeronautical context, and for aircraft in particular, instead, braking actuator degradation and tire consumption significantly contribute to an aircraft maintenance cost due to the frequency of their replacement. This is mainly due to the fact that aircraft braking maneuvers last significantly longer than those in the automotive context. So that the antilock braking system is always active during the braking maneuver, making its impact on the consumption of the two components significant. This work proposes an innovative data-driven model of brake and tire degradation, showing how they are related to the antiskid controller parameters. The analysis is carried out in a MATLAB/Simulink environment on a single wheel rigid body model, validated experimentally, which includes all the nonlinear effects peculiar of the aeronautic context. The results show that by using an appropriate antiskid control approach, it is possible to directly regulate the consumption of these components while at the same time guaranteeing the required braking performance

Heterogeneous and hybrid control with application in automotive systems

Control systems for automotive systems have acquired a new level of complexity. To fulfill the requirements of the controller specifications new technologies are needed. In many cases high performance and robust control cannot be provided by a simple conventional controller anymore. In this case hybrid combinations of local controllers, gain scheduled controllers and global stabilisation concepts are necessary. A considerable number of state-of-the-art automotive controllers (anti-lock brake system (ABS), electronic stabilising program (ESP)) already incorporate heterogeneous and hybrid control concepts as ad-hoc solutions. In this work a heterogeneous/hybrid control system is developed for a test vehicle in order to solve a clearly specified and relevant automotive control problem. The control system will be evaluated against a state-of-the-art conventional controller to clearly show the benefits and advantages arising from the novel approach. A multiple model-based observer/estimator for the estimation of parameters is developed to reset the parameter estimate in a conventional Lyapunov based nonlinear adaptive controller. The advantage of combining both approaches is that the performance of the controller with respect to disturbances can be improved considerably because a reduced controller gain will increase the robustness of the approach with respect to noise and unmodelled dynamics. Several alternative resetting criteria are developed based on a control Lyapunov function, such that resetting guarantees a decrease in the Lyapunov function. Since ABS systems have to operate on different possibly fast changing road surfaces the application of hybrid methodologies is apparent. Four different model based wheel slip controllers will be presented: two nonlinear approaches combined with parameter resetting, a simple linear controller that has been designed using the technique of simultaneously stabilising a set of linear plants as well as a sub-optimal linear quadratic (LQ)-controller. All wheel slip controllers operate as low level controllers in a modular structure that has been developed for the ABS problem. The controllers will be applied to a real Mercedes E-class passenger car. The vehicle is equipped with a brake-by-wire system and electromechanical brake actuators. Extensive real life tests show the benefits of the hybrid approaches in a fast changing environment

Model-free intelligent Control for anti-lock braking systems on rough terrain

Advances made in Advanced Driver Assistance Systems such as Antilock Braking Systems (ABS), have significantly improved the safety of road vehicles. ABS enhances the braking performance and steerability of a vehicle under severe braking conditions. However, ABS performance degrades on rough terrain. This is largely due to noisy measurements, the type of ABS control algorithm used, and the excitation of complex dynamics such as higher order tyre mode shapes that are neglected in the control strategy. This study proposes a model-free intelligent control technique with no modelling constraints that can overcome these un-modelled dynamics and parametric uncertainties. The Double Deep Q-learning Network algorithm with the Temporal Convolutional Network is presented as the intelligent control algorithm. The model is initially trained with a simplified single wheel model. The initial training data is transferred to and then enhanced by using a validated full-vehicle model including a physics-based tyre model, a 3D rough road profile with added stochasticity. The performance of the newly developed ABS controller is compared to a Bosch algorithm tuned for off-road use. Simulation results show a generalizable and robust control algorithm that can prevent wheel lockup over rough terrain without significantly deteriorating the vehicle’s stopping distance on smooth roadsDissertation (MEng (Mechanical Engineering))--University of Pretoria, 2022.Mechanical and Aeronautical EngineeringMEng (Mechanical Engineering)Unrestricte