Fractional Order State Feedback Control for Improved Lateral Stability of Semi-Autonomous Commercial Heavy Vehicles

With the growing development of autonomous and semi-autonomous large commercial heavy vehicles, the lateral stability control of articulated vehicles have caught the attention of researchers recently. Active vehicle front steering (AFS) can enhance the handling performance and stability of articulated vehicles for an emergency highway maneuver scenario. However, with large vehicles such tractor-trailers, the system becomes more complex to control and there is an increased occurrence of instabilities. This research investigates a new control scheme based on fractional calculus as a technique that ensures lateral stability of articulated large heavy vehicles during evasive highway maneuvering scenarios. The control method is first implemented to a passenger vehicle model with 2-axles based on the well-known “bicycle model”. The model is then extended and applied onto larger three-axle commercial heavy vehicles in platooning operations. To validate the proposed new control algorithm, the system is linearized and a fractional order PI state feedback control is developed based on the linearized model. Then using Matlab/Simulink, the developed fractional-order linear controller is implemented onto the non-linear tractor-trailer dynamic model. The tractor-trailer system is modeled based on the conventional integer-order techniques and then a non-integer linear controller is developed to control the system. Overall, results confirm that the proposed controller improves the lateral stability of a tractor-trailer response time by 20% as compared to a professional truck driver during an evasive highway maneuvering scenario. In addition, the effects of variable truck cargo loading and longitudinal speed are evaluated to confirm the robustness of the new control method under a variety of potential operating conditions

Feedback Synthesis for Controllable Underactuated Systems using Sequential Second Order Actions

This paper derives nonlinear feedback control synthesis for general control affine systems using second-order actions---the needle variations of optimal control---as the basis for choosing each control response to the current state. A second result of the paper is that the method provably exploits the nonlinear controllability of a system by virtue of an explicit dependence of the second-order needle variation on the Lie bracket between vector fields. As a result, each control decision necessarily decreases the objective when the system is nonlinearly controllable using first-order Lie brackets. Simulation results using a differential drive cart, an underactuated kinematic vehicle in three dimensions, and an underactuated dynamic model of an underwater vehicle demonstrate that the method finds control solutions when the first-order analysis is singular. Moreover, the simulated examples demonstrate superior convergence when compared to synthesis based on first-order needle variations. Lastly, the underactuated dynamic underwater vehicle model demonstrates the convergence even in the presence of a velocity field.Comment: 9 page

Advanced Sensing and Control for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs

Real-time sensor data development for smart truck drivetrains

Heavy articulated transport vehicles have a poor reputation associated with dramatic road accidents with frequent fatalities for those in automobiles. The result of this work is a formal data flow structure to enhance real-time decision-making in complex mechanical systems to increase performance capability and responsiveness to human commands. This structure recognizes the multiple layers of highly non-linear mechanical components (actuators, wheel tire & ground surfaces, controllers, power supplies, human/machine interfaces, etc.) that must operate in unison (i.e., reduce conflicts) in real-time (in milli-seconds) to enhance operator (driver) control to maximize human choice. This work contains a discussion on dependable sensor data is vital in complex systems that rely on a suite of sensors for both control as well as condition monitoring purposes as well as discussion on real-time energy distribution analysis in high momentum mechanical systems. The focus will be on tractor trucks of class 7 & 8 that are outfitted with an array of low-cost redundant sensors leveraging advances in intelligent robotic systems. This work details many topics including: Most relevant sensor types and their technologies, Designing, implementing, and maintaining a multi-sensor system using feasible industry standards, Sensor signal integrity and data flow processing for decision making, Asynchronous data flow methods for operating decision making schemes in real-time, Multiple applications to enhance tractor trucks systems with multi-sensor systems for real-time decision making.Mechanical Engineerin

Integrated Stability and Tracking Control System for Autonomous Vehicle-Trailer Systems

The addition of a trailer to a vehicle significantly changes the dynamics of a single-vehicle and generates new instability modes including jackknifing and snaking. Understanding of the dynamics of these modes leads to the development of more effective control strategies for improved stability and handling. In addition, vehicle-trailer systems suffer from the off-tracking problem meaning that the path of the trailer rear end differs from that of the vehicle front end. Off-tracking makes these vehicles less maneuverable and increases swept path of the vehicle, especially in urban areas and tight spaces. Vehicle active safety systems have been extensively developed over the past decades to improve vehicle handling and assist the driver to keep the vehicle under control in unfavorable driving situations. Such active safety systems, however, are not well developed for vehicle-trailer systems specialty to control the above-mentioned instability modes. In addition to the above active safety systems, off-tracking of vehicle-trailers is another aspect that is essential in path planning and path tracking of autonomous vehicle-trailers. In this thesis, first, phase portraits are used to study the non-linear dynamics of the system through state trajectories and equilibrium point locations of a vehicle-trailer system, with respect to the inputs and also the loading conditions of the trailer. By the study of the phase portraits, the foundation of the vehicle-trailer yaw instability is recognized and a two-dimensional stable region as the stability envelope is defined. This stability envelope is utilized to prevent unnecessary control interventions while the vehicle is operating in the safe stability region whereas the controller is allowed to effectively interfere when the vehicle crosses the stability envelope. To handle multiple control actions in the vehicle and trailer units, a control structure with two hierarchical layers is proposed. In the upper layer, a model predictive controller is formulated as a quadratic optimization problem with a virtual control action for each degree of freedom of the vehicle dynamic model. In this formulation, the developed stability envelope is constructed as state constraints along with control action constraints. In the lower layer, a control allocation approach optimally transforms the virtual control actions provided by the upper layer into steering and/or braking commands for each wheel. In this approach, actuator failure in addition to actuator and tire capacity constraints is taken into account in real-time. A hybrid A*-based motion planning is developed to generate a trajectory while considering the trailer effect in terms of stability in high speed and off-tracking in high curvature paths. The proposed motion planning utilizes a hybrid A* algorithm combined with potential fields to find a feasible and collision-free trajectory for the vehicle-trailer system. To assess the performance of the proposed control structure in instability prevention both in snaking and jackknifing, different simulations are conducted using different control actions. Additionally, the fault tolerance and robustness of the proposed controller are investigated. To validate the real-time performance of the proposed control strategy, experimental tests are performed on a vehicle-trailer system with differential braking capability. The results show that the proposed control strategy is able to effectively prevent both instability modes as well as unnecessary engagement of the control action to reduce control intervention. The performance of the developed motion planning module is also evaluated for normal driving, obstacle avoidance, off-tracking compensation, and crash mitigation using high-fidility Carsim model. It is observed that proposed motion planning is able to effectively satisfy all the expected requirements

Event-Triggered Multi-Lane Fusion Control for 2-D Vehicle Platoon Systems with Distance Constraints

This paper investigates the event-triggered fixedtime multi-lane fusion control for vehicle platoon systems with distance keeping constraints where the vehicles are spread in multiple lanes. To realize the fusion of vehicles in different lanes, the vehicle platoon systems are firstly constructed with respect to a two-dimensional (2-D) plane. In case of the collision and loss of effective communication, the distance constraints for each vehicle are guaranteed by a barrier function-based control strategy. In contrast to the existing results regarding the command filter techniques, the proposed distance keeping controller can constrain the distance tracking error directly and the error generated by the command filter is coped with by adaptive fuzzy control technique. Moreover, to offset the impacts of the unknown system dynamics and the external disturbances, an unknown input reconstruction method with asymptotic convergence is developed by utilizing the interval observer technique. Finally, two relative threshold triggering mechanisms are utilized in the proposed fixed-time multi-lane fusion controller design so as to reduce the communication burden. The corresponding simulation results also verify the effectiveness of the proposed strategy

Volume 1 – Symposium: Tuesday, March 8

Group A: Digital Hydraulics Group B: Intelligent Control Group C: Valves Group D | G | K: Fundamentals Group E | H | L: Mobile Hydraulics Group F | I: Pumps Group M: Hydraulic Components:Group A: Digital Hydraulics Group B: Intelligent Control Group C: Valves Group D | G | K: Fundamentals Group E | H | L: Mobile Hydraulics Group F | I: Pumps Group M: Hydraulic Component

Detection of Anomalous Vehicle Loading

Determining the mass of a vehicle from ground based passive sensor data is important for many security and traffic safety reasons. A vehicle consists of multiple dependent and independent systems that each respond differently to changes in vehicle mass. In some cases, the responses of these vehicle systems can be measured remotely. If these remotely sensed system responses are correlated to the vehicle\u27s mass, and the required vehicle parameters were known, it would be possible to calculate the mass of the vehicle as a function of these responses. The research described here investigates multiple vehicle phenomenologies and their correlation to vehicle load. Brake temperature, engine acoustics, exhaust output, tire temperature, tire deformation, vehicle induced ground vibration, suspension response, and engine torque induced frame twist were all evaluated and assessed as potential methods of remotely measuring a vehicle\u27s mass. Extensive field experiments were designed and carried out using multiple sensors of various types; including microphones, accelerometers, high-speed video cameras, high-resolution video cameras, LiDAR, and thermal imagers. These experiments were executed at multiple locations and employed passenger vehicles, and commercial trucks with loads ranging from zero to beyond the recommended load capacity of the vehicle. The results of these experiments were used to determine if the signature for each phenomenology could be accurately observed remotely, and if so, how well they correlated to vehicle mass. The suspension response and engine torque induced frame twist phenomenologies were found to have the best correlation to vehicle mass of the phenomenologies considered, with correlation values of 90.5% and 97.7%, respectively. Physics-based models were built for both the suspension response, and the engine torque induced frame twist phenomenologies. These models detailed the relationship between each phenomenology and the mass of the vehicle. Full-scale field testing was done using improved remote detection methods, and the results were used to validate the physics-based models. The results of the full-scale field testing showed that both phenomenology could accurately calculate the mass of the vehicle remotely, given that certain vehicle parameters were accurately known. The engine torque induced frame twist phenomenology was able to find the mass of the test vehicle to within 10% of the true mass. Using the suspension response phenomenology the mass was accurately predicted as a function of its location on the vehicle. For either phenomenology to be effective, certain vehicle parameters must be known accurately; specifically the spring constant and damping coefficients of the vehicle\u27s suspension, the unloaded mass, the unloaded center of gravity, and the unloaded moment of inertia of the vehicle. The models were also used to propagate measurement and parameter uncertainty through the vehicle mass calculation to arrive at the uncertainty in the mass estimation. Finally, the results of both the phenomenologies were combined into a single vehicle mass estimate with a smaller uncertainty than the individual vehicle system estimations taken alone

Applicable Solutions in Non-Linear Dynamical Systems

From Preface: The 15th International Conference „Dynamical Systems - Theory and Applications” (DSTA 2019, 2-5 December, 2019, Lodz, Poland) gathered a numerous group of outstanding scientists and engineers who deal with widely understood problems of theoretical and applied dynamics. Organization of the conference would not have been possible without great effort of the staff of the Department of Automation, Biomechanics and Mechatronics of the Lodz University of Technology. The patronage over the conference has been taken by the Committee of Mechanics of the Polish Academy of Sciences and Ministry of Science and Higher Education of Poland. It is a great pleasure that our event was attended by over 180 researchers from 35 countries all over the world, who decided to share the results of their research and experience in different fields related to dynamical systems. This year, the DSTA Conference Proceedings were split into two volumes entitled „Theoretical Approaches in Non-Linear Dynamical Systems” and „Applicable Solutions in Non-Linear Dynamical Systems”. In addition, DSTA 2019 resulted in three volumes of Springer Proceedings in Mathematics and Statistics entitled „Control and Stability of Dynamical Systems”, „Mathematical and Numerical Approaches in Dynamical Systems” and „Dynamical Systems in Mechatronics and Life Sciences”. Also, many outstanding papers will be recommended to special issues of renowned scientific journals.Cover design: Kaźmierczak, MarekTechnical editor: Kaźmierczak, Mare

MPC and PSO based control methodology for path tracking of 4WS4WD vehicles

© 2018 by the authors. Four wheel steering and four wheel drive (4WS4WD) vehicles are over-actuated systems with superior performance. Considering the control problem caused by the system nonlinearity and over-actuated characteristics of the 4WS4WD vehicle, this paper presents two methods to enable a 4WS4WD vehicle to accurately follow a predefined path as well as its reference trajectories including velocity and acceleration profiles. The methodologies are based on model predictive control (MPC) and particle swarm optimization (PSO), respectively. The MPC method generates the virtual inputs in the upper controller and then allocates the actual inputs in the lower controller using sequential quadratic programming (SQP), whereas the PSO method is proposed as a fully optimization based method for comparison. Both methods achieve optimization of the steering angles and wheel forces for each of four independent wheels simultaneously in real time. Simulation results achieved by two different controllers in following the reference path with varying disturbances are presented. Discussion about two methodologies is provided based on their theoretical analysis and simulation results