Among the trends that are going to shape the automotive industry in the coming years, autonomous vehicles stand out as having the potential to completely change the automotive industry as we know it. One of the critical tasks in this framework includes robust execution of the steering control action to maintain a pre-defined path. The main shortcomings of the state of the art steering controllers are controller robustness, cross-comparison and performance validation. To address this aspect, firstly, this study focuses on implementing a sensor model that geometrically measures the lateral and the heading error of the vehicle with respect to the path. Secondly, the focus was to implement existing lateral controllers like Stanley, Path Control with Preview (PCwP), Linear Quadratic Regulator (LQR), Immersion and Invariance (II), Passivity Based Control (PBC) and evaluate path-tracking performance. The Sliding Mode Control (SMC) methodology has proven effective in dealing with complex dynamical systems affected by disturbances, uncertainties and unmodeled dynamics. However, the application of SMC and its algorithms to lateral control in vehicles is not effectively analysed. Finally, this master thesis includes a novel design of three variants of Sliding Mode Control; namely Sliding Mode Control with Super Twisting Algorithm, Modified Super Twisting Algorithm and Non-singular Terminal Modified Super Twisting algorithm. These algorithms were then tested against external disturbances such as localisation error, cross-wind, and parametric variations. This thesis successfully illustrates in detail, the aspects involved in path-tracking control. Results effectively suggest a betterment of the novel steering controllers designed, over the previously existing Sliding Mode Control Super Twisting algorithm and other bench-marking controllers at higher speeds. The work ends with vital conclusions and recommendations for future researchers in this domain to further increase robustness and achieve better performance.

To promote automation in vehicles, autonomous driving should feel comfortable. To achieve low discomfort, a comfort oriented nonlinear model predictive controller is created. We know humans are sensitive for discomfort in certain frequencies in acceleration. By penalizing the frequencies for discomfort a higher comfort performance can be achieved. Two band pass filters are created to penalise the frequencies. A band pass filter (0.03-0.2 Hz) for the frequency of motion sickness and a band pass filter (1-2 Hz) for general discomfort. Due to the MPC framework the filters can be implemented on the predicted accelerations. The filtered accelerations are penalised within the MPC. The MPC is made for path following control. To test the MPC a reference generator is built. The reference generator creates reference signals about the path ahead for the controller. To test the performance of the filters, tests are done with different controllers. In the different controllers the filters are penalised individually and together and compared against other controllers. The controllers are tested on multiple scenarios (e.g. double lane change and a sinusoidal trajectory). On the scenarios multiple disturbances are tested (e.g. wind disturbance and sensor noise). We conclude that the MPC design that relies on the motion sickness filter has a significant decrease of motion sickness in scenarios where a lot of motion sickness is present, with improvements up to 30.7% compared to the basic controller. The MPC design that relies on the general discomfort filter helps bring the general discomfort down. On the double lane change maneuver with a changing velocity the general discomfort filter has improvements up to 10.7% compared to the basic controller.

Motion sickness is a common phenomenon, with close to two-thirds of the population experiencing it in their lifetime. With the advent of automated vehicles in the market, it is anticipated to become an even greater problem as the passengers face a lack of predictability of motion and loss of control over the vehicle. This could nullify the host of possible benefits that automated vehicles propose to offer, and therefore affect their acceptance among potential users. It is well known that the nauseogenicity of imposed motion is dependent on the frequency content of endured accelerations, with low-frequency accelerations being the primary contributor. This thesis presents a motion planning algorithm targeted towards minimization of motion sickness among passengers of automated vehicles, through targeted reduction of low-frequency accelerations. A Deep Reinforcement Learning (DRL) framework was utilised along with the design of a custom environment and a reward function which incorporates a measure of nauseogenicity of the planned trajectories. The frequency shaping effect of the reward function was evaluated by comparing against a DRL agent trained to optimize general motion comfort described by total acceleration energy. It was found that the nauseogenicity was reduced by 9.6% with the proposed DRL agent. Further, on-road trials were performed with human drivers to establish a benchmark of driving comfort. The performance of the DRL agent was compared to human drivers as well as against an optimization-based motion planner that computationally maximizes the reward function. The DRL planner displayed comparable performance to the human drivers, and was within 10 to 15% of the discomfort levels of the optimization-based planner for a range of travel times. Meanwhile, the DRL planner offered notable improvements in computational efficiency, taking 1-2 ms to generate a sub-optimal trajectory, as opposed to approximately 5 s as taken by the optimization-based planner.

ZF Friedrichshafen AG is developing its own active suspension system designed as a single unit per corner to optimize the dynamic behavior of a vehicle. The innovative active damper design integrates all the essential components into one damper unit, with the aim to facilitate implementation. This master thesis in cooperation with ZF Friedrichshafen AG presents the development of a disturbance compensation controller for the active suspension unit. The goal is to suppress forces induced by road irregularities on the passenger cabin. The emphasis throughout this work lies on the implementation and applicability of the control algorithm.

Fully automated vehicles have the potential to increase road safety and improve traffic flow by taking the human element out of the driving loop. They can also provide mobility to people who are unable to operate a conventional vehicle. Safe automated vehicles must be able to respond in emergency situations or drive on slippery roads in bad weather conditions. Therefore it is crucial to have a safe and robust control strategy that can use the full handling capabilities of the vehicle. This thesis presents how safe reinforcement learning can be used to design a steering policy that can drive an automated vehicle at the limit of friction. The steering policies are trained using the Lyapunov Safe Actor-Critic (LSAC) algorithm. LSAC is a combination of the Soft Actor-Critic (SAC) algorithm and a Lyapunov stability analysis to solve constrained control problems. The performance of LSAC is tested in a vehicle simulator against SAC and Model Predictive Control (MPC) in a series of tests that include changing lanes at different speeds, recovering from a destabilizing collision, and driving on a race track at the limit of friction. The experiments show that LSAC outperforms MPC and SAC control strategies in terms of safety and vehicle stability. LSAC can recover from larger disturbances than MPC and SAC. A control strategy is presented that will keep the vehicle stable when driving at the limit of friction but can use the maneuverability of an unstable vehicle when is it necessary to avoid dangerous situations. Additionally, a policy is presented that can find the fastest way around a race track while staying within the track limits.

Driving simulators are widely used for understanding Human-Machine Interaction, driver behavior and driver training. The effectiveness of such simulators in this process depends largely on their ability to generate realistic motion cues. Though the conventional filter-based motion cueing strategies have provided reasonable results, these methods result in poor workspace management. To address this issue, linear MPC-based strategies have been applied in the past. However, since the kinematics of the motion platform itself is non-linear and the required motion varies with the driving conditions, this approach tends to produce sub-optimal results. In this thesis, a nonlinear MPC-based algorithm is presented which incorporates the non-linear kinematics of the Stewart platform within the MPC algorithm to increase the effectiveness and utilize maximum workspace. Further, adaptive weights-based tuning is used to smoothen the movement of the platform near its physical limits. Full-track simulations were carried out and performance indicators were defined to objectively compare the response of the proposed algorithm with classical washout filter and linear MPC-based algorithms. The results indicate a better reference tracking with lower root mean square error and higher shape correlation for the proposed algorithm. Lastly, the effect of the adaptive weights based tuning was also observed in the form of smoother actuator movements and better workspace utilization.

The introduction of control systems in the automotive industry has significantly increased safety. Improved control over the vehicle dynamics has been shown to contribute to substantial reductions in the number of deaths and serious injuries resulting from road traffic crashes. The introduction of Electronic Stability Control yielded impressive improvement in vehicle stability. A meta-analysis revealed that a 49% reduction of single-vehicle accidents is realized. Recent research continues the development of a fully autonomously operating vehicle. The vehicle requires the ability to operate in all situations safely, in order to reach the highest level of automation. Vehicle performance should be guaranteed within, at and beyond the limits of friction. Previous research revealed that the unstable drift motion could enlarge the operating envelope of a vehicle. An extensive amount of research is dedicated to controlling a drift. The results show that control systems are increasingly capable of stabilizing a steady-state cornering scenario. The main limitation of these studies is that only a portion of the vehicle motion that is observed in reality can be considered to be steady-state motion. This thesis presents a multi-objective trajectory optimization which extends the steady-state analysis to a dynamic driving scenario. Based on experimental data obtained with a 1:10 scaled vehicle, accurate vehicle and tire models are derived. It is validated that the models closely mimic the dynamics of the scaled vehicle. In order to justify the use of drifting, the differences between stable and unstable driving equilibria are studied. The stability and controllability are assessed through the construction of the phase portraits and the computation of the Controllability Grammian. The findings, obtained under the assumption of steady-state conditions, are then validated in the dynamic driving scenario. A two-step optimization approach is presented. Spline optimization based on a simplified model is used to obtain initial conditions for a high fidelity model-based optimization. The scope is limited to a single corner, which is optimized under varying velocities and friction conditions. Under the assumption of steady-state conditions, it is found that the drift motion imposes various benefits over normal driving. Higher cornering velocities and therewith yaw rates can be achieved in a drift. Besides, the principles of tire saturation and force coupling allow for controlling the lateral and yaw dynamics of the vehicle through the rear longitudinal tire force. This increases the maneuverability of the vehicle. The results of the dynamic optimization extend the findings of the steady-state analysis. In the dynamic maneuvers, drifting is found to improve vehicle maneuverability at high velocities and in scenarios of low friction. The approach presented in this work forms a basis for studying the effects that drifting could have on vehicle motion in reality. The relevant aspects of vehicle motion are translated into a multi-objective optimization. The methods that are developed in this work release the simplifying assumption of steady-state driving conditions. As a result, the drift motion can be studied in a more realistic driving scenario. It is expected that through further improving the optimization algorithm, the full operation envelope of the vehicle can be explored.