38,271 research outputs found

    Gain Scheduling PID Control With Pitch Moment Rejection For Pneumatically Actuated Active Suspension

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    This study deals with the use of pneumatically actuated active suspension in canceling out the effects of weight transfer to the vehicle dynamics performance in longitudinal direction. The main content of this study is the development of a full vehicle model, which consists of ride, handling and tire subsystems as to study the vehicle dynamics behavior in longitudinal direction. The full vehicle model is then validated experimentally using an instrumented experimental vehicle based on the driver input from brake and throttle pedals. Two types of vehicle dynamics tests are performed for the purpose of model validation namely sudden braking test and sudden acceleration test. The results of model validation show that the behaviors of the model closely follow the behaviors of a real vehicle with acceptable error. An active suspension control system is then developed on the validated full vehicle model to reduce unwanted vehicle motions during braking and throttling maneuvers such as body pitch angle, body pitch rate, vertical displacement and vertical acceleration of the vehicle body. A proportional-integral-derivative (PID) scheme integrate with pitch moment rejection loop is proposed to control the system. In presented scheme the result verifies improved performance of the proposed control structure during braking and throttling maneuvers compared to the passive vehicle system. It is also noted that the additional pitch moment rejection loop is able to further improve the performance of the PID controller for the system. It is well-known that conventional PID scheme is not robust for controlling the system with unknown disturbances. To improve the performance of PID scheme, a gain scheduling proportional-integral-derivative (GSPID) control with pitch moment rejection loop is then proposed of the active suspension system. The results of the study show that the proposed control structure is able to significantly improve the dynamic performance of the vehicle during sudden braking and sudden acceleration maneuvers compared to conventional PID and the passive vehicle system under various conditions. The effectiveness of the proposed control algorithm on a road test using instrumented experimental vehicle is also observed. Finally, potential benefits in the use of this control are investigated. The result of the study demonstrates the potential benefits of the gain scheduling PID controller in controlling the active suspension

    Robust Control Techniques Enabling Duty Cycle Experiments Utilizing a 6-DOF Crewstation Motion Base, a Full Scale Combat Hybrid Electric Power System, and Long Distance Internet Communications

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    The RemoteLink effort supports the U.S. Army\u27s objective for developing and fielding next generation hybrid-electric combat vehicles. It is a distributed soldierin- the-Ioop and hardware-in-the-Ioop environment with a 6-DOF motion base for operator realism, a full-scale combat hybrid electric power system, and an operational context provided by OneSAF. The driver/gunner crewstations rest on one of two 6-DOF motion bases at the U.S. Army TARDEC Simulation Laboratory (TSL). The hybrid power system is located 2,450 miles away at the TARDEC Power and Energy System Integration Laboratory (P&E SIL). The primary technical challenge in the RemoteLink is to operate both laboratories together in real time, coupled over the Internet, to generate a realistic power system duty cycle. A topology has been chosen such that the laboratories have real hardware interacting with simulated components at both locations to guarantee local closed loop stability. This layout is robust to Internet communication failures and ensures the long distance network delay does not enter the local feedback loops. The TSL states and P&E SIL states will diverge due to (1) significant communications delays and (2) unavoidable differences between the TSL\u27s powersystem simulation and the P&E SIL\u27s real hardware-inthe- loop power system. Tightly coupled, bi-directional interactions exist among the various distributed simulations and software- and hardware-in-the-Ioop components representing the driver, gunner, vehicle, and power system. These interactions necessitate additional adjustment to ensure that the respective states at the TSL and P&E SIL sites converge. This is called state convergence and ensures the dominant energetic states of both laboratories remain closely matched in real time. State convergence must be performed at both locations to achieve bi-directional, real-time interaction like that found on a real vehicle. The result is a distributed control system architecture with Internet communications in the state convergence feedback loop. The Internet communication channel is a primary source of uncertainty that impacts the overall state convergence performance and stability. Multiple control schemes were developed and tested in simulation. This paper presents robust control techniques that compensate for asynchronous Internet communication delays during closed loop operation of the TSL and P&E SIL sites. The subsequent soldier- and hardware-in-the-Ioop experiments were performed using a combination of nonlinear Sliding-mode and linear PID control laws to achieve state convergence at both locations. The control system development, performance, and duty cycle results are presented in this paper

    Vehicle Steering Systems - Hardware-in-the-Loop Simulator, Driving Preferences, and Vehicle Intervention

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    The steering system is a critical component of all ground vehicles regardless of their propulsion source. Chassis directional control is provided by the steering system, which in turn relays valuable feedback about the road and vehicle behavior. As the primary feedback channel to the driver, the steering system also delivers the initial perception of a vehicle\u27s handling and responsiveness to the consumer. Consequently, the steering system is an important aspect of the vehicle\u27s evaluation and purchasing process, even if drivers are unaware of its direct influence in their decision making. With automobile purchases potentially hinging on the steering system, a need exists for a better understanding of steering preference through a focused research project. In this investigation, driver steering preferences have been studied using an advanced hardware-in-the-loop automobile steering simulator. Additionally, vehicle run-off-road situations have been studied, which occur when some of the vehicle wheels drift off the road surface and the driver recovers through steering commands. The Clemson University steering simulator underwent three significant generations of refinements to realize a state-of-the-art automotive engineering tool suitable for human subject testing. The first and third generation refinements focused on creating an immersive environment, while the second generation introduced the accurate reproduction of steering feel found in hydraulic systems and real-time adjustable steering feel. This laboratory simulator was the first known validated driving simulator developed for the sole purpose of supporting driver steering preference studies. The steering simulator successfully passed all validation tests (two pilot studies) leading to an extensive demographics-based driver preference study with 43 subjects. This study reflected the following preliminary trends: Drivers who used their vehicles for utility purposes preferred quicker steering ratios and heavier efforts in residential, country, and highway environments. In contrast, car enthusiasts preferred quick steering ratios in residential and country environments and light steering effort on the highway. Finally, rural drivers preferred quicker steering ratios on country roads. These relationships may be used to set steering targets for future vehicle developments to accurately match vehicles to their intended market segments. The second research aspect was the development of an objective steering metric to evaluate a driver\u27s steering preference. In past simulator studies, driver feedback has been gathered extensively using written questionnaires. However, this delays the testing procedure and introduces an outside influence that may skew results. Through the data collected in this project, a robust objective steering preference metric has been proposed to gather steering preferences without directly communicating with the driver. The weighted steering preference metric demonstrated an excellent correlation with survey responses of -0.39 regardless of steering setting. This global steering preference metric used a combination of yaw rate, longitudinal acceleration, and lateral acceleration. The objective data was further dissected and it was discovered that changes made to the steering ratio resulted in a correlation of -0.55 between the objective data and subjective response from the test subjects. This substantial correlation relied on the longitudinal acceleration, left front tire angle, and throttle position. Beyond steering preferences, vehicle safety remains a major concern for automotive manufacturers. One important type of crash results from the vehicle leaving the road surface and then returning abruptly due to large steering wheel inputs: road runoff and return. A subset of run-off-road crashes that involves a steep hard shoulder has been labeled \u27shoulder induced accidents\u27. An active steering controller was developed to mitigate these \u27shoulder induced accidents\u27. A cornering stiffness estimation technique, using a Kalman filter, was coupled with a full state feedback controller and \u27driver intention\u27 module to create a safe solution without excessive intervention. The concept was designed to not only work for shoulder induced accidents, but also for similar road surface fluctuations like patched ice. The vehicle crossed the centerline after 1.0s in the baseline case; the controller was able to improve this to 1.3s for a 30% improvement regardless of driver expertise level. For the case of an attentive driver, the final heading angle of the vehicle was reduced by 47% from 0.48 rad to 0.255 rad. These laboratory investigations have clearly demonstrated that advancements in driver preference and vehicle safety may be realized using simulator technology. The opportunity to apply these tools should result in better vehicles and greater safety of driver and occupants. With the development of the objective steering preference metric, future research opportunities exist. For prior steering preference research, the feedback loop has typically required interaction with the subject to rate a setting before continuing. However, the objective steering preference metric allows this step to be automated, opening the door for the development of an automatic tuning steering system

    A Flexible Modeling Approach for Robust Multi-Lane Road Estimation

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    A robust estimation of road course and traffic lanes is an essential part of environment perception for next generations of Advanced Driver Assistance Systems and development of self-driving vehicles. In this paper, a flexible method for modeling multiple lanes in a vehicle in real time is presented. Information about traffic lanes, derived by cameras and other environmental sensors, that is represented as features, serves as input for an iterative expectation-maximization method to estimate a lane model. The generic and modular concept of the approach allows to freely choose the mathematical functions for the geometrical description of lanes. In addition to the current measurement data, the previously estimated result as well as additional constraints to reflect parallelism and continuity of traffic lanes, are considered in the optimization process. As evaluation of the lane estimation method, its performance is showcased using cubic splines for the geometric representation of lanes in simulated scenarios and measurements recorded using a development vehicle. In a comparison to ground truth data, robustness and precision of the lanes estimated up to a distance of 120 m are demonstrated. As a part of the environmental modeling, the presented method can be utilized for longitudinal and lateral control of autonomous vehicles

    Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles

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    In this paper we propose a constrained optimal control architecture for combined velocity, yaw and sideslip regulation for stabilisation of the vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear vehicle and tyre model are used to find reference steady-state cornering conditions and design two model predictive control (MPC) strategies of different levels of fidelity: one that uses a linearised version of the full vehicle model with the rear wheels' torques as the input, and another one that neglects the wheel dynamics and uses the rear wheels' slips as the input instead. After analysing the relative trade-offs between performance and computational effort, we compare the two MPC strategies against each other and against an unconstrained optimal control strategy in Simulink and Carsim environment

    Multi-Lane Perception Using Feature Fusion Based on GraphSLAM

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    An extensive, precise and robust recognition and modeling of the environment is a key factor for next generations of Advanced Driver Assistance Systems and development of autonomous vehicles. In this paper, a real-time approach for the perception of multiple lanes on highways is proposed. Lane markings detected by camera systems and observations of other traffic participants provide the input data for the algorithm. The information is accumulated and fused using GraphSLAM and the result constitutes the basis for a multilane clothoid model. To allow incorporation of additional information sources, input data is processed in a generic format. Evaluation of the method is performed by comparing real data, collected with an experimental vehicle on highways, to a ground truth map. The results show that ego and adjacent lanes are robustly detected with high quality up to a distance of 120 m. In comparison to serial lane detection, an increase in the detection range of the ego lane and a continuous perception of neighboring lanes is achieved. The method can potentially be utilized for the longitudinal and lateral control of self-driving vehicles

    Integrated controls/structures study of advanced space systems

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    A cost tradeoff is postulated for a stiff structure utilizing minimal controls (and control expense) to point and stabilize the vehicle. Extra costs for a stiff structure are caused by weight, packaging size, etc. Likewise, a more flexible vehicle should result in reduced structural costs but increased costs associated with additional control hardware and data processing required for vibration control of the structure. This tradeoff occurs as the ratio of the control bandwidth required for the mission to the lowest (significant) bending mode of the vehicle. The cost of controlling a spacecraft for a specific mission and the same basic configuration but varying the flexibility is established
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