A feedback-feed-forward steering control strategy for improving lateral dynamics stability of an A-double vehicle at high speeds

A control strategy based on H∞-type static output feedback combined with dynamic feed-forward is proposed to improve the high-speed lateral performance of an A-double combination vehicle (tractor–semitrailer–dolly–semitrailer) using active steering of the front axle of the dolly. Both feedback and feed-forward syntheses are performed via Linear Matrix Inequality (LMI) optimisation. From a practical point of view, the proposed controller is simple and easy to implement, despite its theoretical complexity. In fact, the measurement of the driver steering angle and only one articulation angle are required for the feed-forward and the feedback controllers, respectively. The results are verified using a high-fidelity vehicle model and confirm a significant reduction in yaw rate and lateral acceleration rearward amplification and also high-speed transient off-tracking, and subsequently improving the lateral stability and performance of the A-double combination vehicle during sudden lane change manoeuvres

Human factors : a new approach for designing the truck-driver system

The logistics sector is an often forgotten force behind modern life in the UK, and it is increasingly under pressure to become more efficient, more safety-conscious, and more environmentally sustainable. This triple bottom line necessitates deep changes to the traditional way of working. As evidenced by an expert-led technology forecast, many technological and organisational interventions are on the horizon for the next 15-30 years. This rapid pace of advancement, together with the frequent assumption that workers are ‘hyper-rational’, echoes a worrying pattern from other sectors that have since benefited from human factors & ergonomics (HF/E) expertise. This thesis aims to apply HF/E principles and methods to both current and projected future truck-driver scenarios, in order to leverage the most agile and intelligent agent in the logistics system: the human. Despite a lack of past work at this intersection, logistics and HF/E can be drawn together by their mutual use of systems complexity concepts. This thesis proposes that logistics is a large, complex adaptive socio-technical system (CASTS), and reviews HF/E methods to determine their fit to different system scales and dynamics. From this it is determined that initial work requires a bottom-up focus on the truck-driver system. A range of methods are employed to understand the existing truck driving task and what it requires of the modern driver; identify and prioritise potentially critical system ‘parts’; design new supportive technologies from scratch in a way that allows for emergent behaviour; and analytically prototype how truck-driver systems are likely to change in projected future scenarios. This work provides new practical insights for current truck-driver systems, and a map of how this may change – shedding light on potential future problems and how we might adapt to them before they occur. Not only does this thesis provide a solid empirical foundation and a ‘direction of travel’, it also contributes the methodological guidance necessary to strategise next steps beyond this thesis, into deeper logistics complexity. Taken together this demonstrates the power of human factors methods for logistics, and their potential for other unexplored ‘complex adaptive sociotechnical systems’ (CASTS)

State and Parameter Estimation of Vehicle-Trailer Systems

Vehicle-trailer systems have different unstable modes that should be considered in their stability control, including trailer snaking, jack-knifing, and roll-over. In general, vehicle control systems require vehicle parameters and states, including geometric parameters, mass, tire forces, and side slip angles which some are not constant or can be measured economically. In a vehicle-trailer system, the trailer states and parameters such as articulation angle, trailer geometric parameters, trailer mass, trailer tire forces, and yaw rate need to be measured or identified/estimated, in addition to the unknown vehicle states/parameters. The trailer states and parameters can be measured by sensors such as Inertial Measurement Unit (IMU), wheel torque sensors, and force measurement units. However, most of these sensors are not commercially viable to be used in a vehicle or trailer due to significant extra costs. Estimation algorithms are the other tools to identify the parameters and states of the system without imposing extra costs. Accurate state and parameter estimators are needed for the development and implementation of a stability control system for a vehicle-trailer system. The main purpose of this research is to design real-time state and parameter estimation algorithms for vehicle-trailer systems. Correspondingly, a comprehensive overview of different model-based and non-model-based techniques/algorithms used for estimating vehicle-trailer states and parameters are provided. The vehicle-trailer system equations of motion are then presented and based on the presented vehicle-trailer model, the possibility of the trailer states and parameters estimation are investigated for different possible vehicle-trailer on-board sensor settings. Two different methods are proposed to estimate trailer mass for arbitrary vehicle-trailer configurations: model-based and Machine Learning (ML). The stability of the model-based estimation algorithm is analyzed, establishing the convergence of the estimation error to zero. In the proposed ML-based approach, a deep neural network is designed to estimate trailer mass. The inputs of the ML-based method are selected based on the vehicle-trailer model and are normalized by the vehicle mass, tire sizes, and geometry so that retraining of the network is not needed for different towing vehicles. The simulation and experimental results demonstrate that the trailer mass can be estimated with with acceptable computational costs. In this thesis, ultrasonic sensors along with kinematics and dynamics equations of a towing vehicle are used to develop three approaches for hitch angle estimation. The first approach is based on direct calculation of hitch angle using certain a priori geometric information and distance measurements of four Ultra sonic sensors. As the second and third approaches, kinematic and dynamic models of the vehicle-trailer system are used to develop least-square and Kalman filter based recursive hitch angle estimations. A more reliable hitch angle estimation scheme is then proposed as the integration of the algorithms developed following each of the three approaches via a switching data fusion logic. It is shown that the proposed integrated hitch angle estimation scheme can be used for any ball type trailer with a flat or symmetric V-nose frontal face without any priori information on the trailer parameters. Additionally, a new approach in estimating the lateral tire forces and hitch-forces of a vehicle-trailer system is introduced. It is shown that the proposed hitch-force estimation is independent of trailer mass and geometry. The designed lateral tire forces and hitch-force estimation algorithms can be used for any ball type trailer without any priori information on the trailer parameters. A vehicle-trailer model is proposed to design an observer for the estimation of the hitch-forces and lateral tire forces. Simulations studies in CarSim along with experimental tests are used to validate the presented method to confirm the accuracy of the developed observer. Moreover, using the vehicle-trailer lateral dynamics along with the LuGre tire model, an estimation system for the lateral velocity of a vehicle-trailer is proposed. It is shown that the proposed estimation is robust to the road conditions. An affine quadratic stability approach is used to analyze the stability of the proposed estimation. The test results confirm the accuracy of the developed estimation and convergence of the vehicle-trailer lateral velocity estimation to the actual value. Model-based and ML-based estimators are developed for estimating road angles for arbitrary vehicle-trailer configurations. The estimators are shown to be independent from road friction conditions. The model-based method employs unknown input observers on the vehicle-trailer roll and pitch dynamic models. In the proposed ML-based estimator, a recurrent neural network with Long-short-term-memory gates is designed to estimate the road angles. The inputs to the ML-based method are normalized by the vehicle wheel-base, mass, and CG height to make it applicable to any towing vehicle with the need of retraining. The simulation and experimental results justify the convergence of the road angle estimation error

Robust tracking controller design for active dolly steering

In this paper different actuation level steering control methods for an A-double vehicle combination (tractor-semitrailer-dolly-semitrailer) are proposed. The aim of the paper is to show viability of advanced actuation control strategies on a practical vehicular application. Three different types of robust controllers are proposed: a robust Proportional Integral Derivative (PID) controller, an output feedback linear Hinf controller and an induced L2-norm minimizing Linear Parameter Varying (LPV) controller. All controllers are augmented with anti-windup compensators to respect steering angle and steering rate limits. Each model based controller robustly rejects external disturbances and tracks a reference steering angle, generated by motion control system. Frequency- and time domain analysis proves that Hinf and LPV controllers outperform PID controller in terms of reference tracking and disturbance rejection. Comparative simulation scenarios are provided on the basis of Volvo Group Trucks Technology’s high fidelity vehicle simulator

Inverse Model Control Including Actuator Dynamics for Active Dolly Steering in High Capacity Transport Vehicle

This paper describes an advance controller designed using the nonlinear inversion technique of a Modelica based simulation tool, such as Dymola, for active dolly steering of a high capacity transport vehicle. Actuator dynamics is included in the inverse model controller. Therefore, it can automatically generate required steering angle request for the dolly axles of the vehicle combination. The resultant controller is transfered as a functional mock-up unit (FMU) to Simulink environment where the actual simulations are conducted. The controller is simulated against a high-fidelity vehicle model of an A-double combination from Virtual Truck Models (VTM) library -- developed by Volvo Group Trucks Technology. Effects of variations of the actual actuator dynamics, with respect to the modeled dynamics in the inverse model controller, on overall vehicle performance are investigated