Active damping of transient vibration in dual clutch transmission equipped powertrains: A comparison of conventional and hybrid electric vehicles

The purpose of this paper is to investigate the active damping of automotive powertrains for the suppression of gear shift related transient vibrations. Conventionally, powertrain vibration is usually suppressed passively through the application of torsional dampers in dual clutch transmissions (DCT) and torque converters in planetary automatic transmissions (AT). This paper presents an approach for active suppression of transient responses utilising only the current sensors available in the powertrain. An active control strategy for manipulating engine or electric machine output torque post gear change via a proportional-integral-derivative (PID) controller is developed and implemented. Whilst conventional internal combustion engine (ICE) powertrains require manipulation of the engine throttle, for HEV powertrains the electric machine (EM) output torque is controlled to rapidly suppress powertrain transients. Simulations for both conventional internal combustion engine and parallel hybrid vehicles are performed to evaluate the proposed strategy. Results show that while both the conventional and hybrid powertrains are both capable of successfully suppressing undesirable transients, the EM is more successful in achieving vibration suppression. © 2014 Elsevier Ltd

Off-line optimization based active control of torsional oscillation for electric vehicle drivetrain

© 2017 by the authors. As there is no clutch or hydraulic torque converter in electric vehicles to buffer and absorb torsional vibrations. Oscillation will occur in electric vehicle drivetrains when drivers tip in/out or are shifting. In order to improve vehicle response to transients, reduce vehicle jerk and reduce wear of drivetrain parts, torque step changes should be avoided. This article mainly focuses on drivetrain oscillations caused by torque interruption for shifting in a Motor-Transmission Integrated System. It takes advantage of the motor responsiveness, an optimal active control method is presented to reduce oscillations by adjusting motor torque output dynamically. A rear-wheel-drive electric vehicle with a two gear automated manual transmission is considered to set up dynamic differential equations based on Newton's law of motion. By linearization of the affine system, a joint genetic algorithm and linear quadratic regulator method is applied to calculate the real optimal motor torque. In order to improve immediacy of the control system, time consuming optimization process of parameters is completed off-line. The active control system is tested in AMEsim® and limitation of motor external characteristics are considered. The results demonstrate that, compared with the open-loop system, the proposed algorithm can reduce motion oscillation to a satisfied extent when unloading torque for shifting

Handling Qualities Evaluation of a Supersonic Tailless Air Vehicle

This thesis presents the results of a handling qualities evaluation of a supersonic tailless air vehicle. The 2006 Quadrennial Defense Review mandated the need for the next generation of long-range strike aircraft by 2018. Due to speed and stealth requirements, this resulted in a tailless aircraft with an instantaneous center of rotation located well forward of that of a conventional aircraft. This thesis examines how this center of rotation affected pilot handling qualities ratings. This effect should have been the most pronounced during approach and landing, and was where the testing focused. The goal of this research was to develop a systematic procedure for evaluating the handling qualities of this aircraft, and to determine how different pilot flying techniques or pilot-inceptor interactions influenced them. This procedure was demonstrated in simulator testing and in flight testing on the Calspan-operated Total In-Flight Simulator aircraft

Integration of Active Systems for a Global Chassis Control Design

Vehicle chassis control active systems (braking, suspension, steering and driveline), from the first ABS/ESC control unit to the current advanced driver assistance systems (ADAS), are progressively revolutionizing the way of thinking and designing the vehicle, improving its interaction with the surrounding world (V2V and V2X) and have led to excellent results in terms of safety and performances (dynamic behavior and drivability). They are usually referred as intelligent vehicles due to a software/hardware architecture able to assist the driver for achieving specific safety margin and/or optimal vehicle dynamic behavior. Moreover, industrial and academic communities agree that these technologies will progress till the diffusion of the so called autonomous cars which are able to drive robustly in a wide range of traffic scenarios. Different autonomous vehicles are already available in Europe, Japan and United States and several solutions have been proposed for smart cities and/or small public area like university campus. In this context, the present research activity aims at improving safety, comfort and performances through the integration of global active chassis control: the purposes are to study, design and implement control strategies to support the driver for achieving one or more final target among safety, comfort and performance. Specifically, the vehicle subsystems that are involved in the present research for active systems development are the steering system, the propulsion system, the transmission and the braking system. The thesis is divided into three sections related to different applications of active systems that, starting from a robust theoretical design procedure, are strongly supported by objective experimental results obtained fromHardware In the Loop (HIL) test rigs and/or proving ground testing sessions. The first chapter is dedicated to one of the most discussed topic about autonomous driving due to its impact from the social point of view and in terms of human error mitigation when the driver is not prompt enough. In particular, it is here analyzed the automated steering control which is already implemented for automatic parking and that could represent also a key element for conventional passenger car in emergency situation where a braking intervention is not enough for avoiding an imminent collision. The activity is focused on different steering controllers design and their implementation for an autonomous vehicle; an obstacle collision avoidance adaptation is introduced for future implementations. Three different controllers, Proportional Derivative (PD), PD+Feedforward (FF) e PD+Integral Sliding Mode (ISM), are designed for tracking a reference trajectory that can be modified in real-time for obstacle avoidance purposes. Furthermore, PD+FF and PD+ISM logic are able to improve the tracking performances of automated steering during cornering maneuvers, relevant fromthe collision avoidance point of view. Path tracking control and its obstacle avoidance enhancement is also shown during experimental tests executed in a proving ground through its implementation for an autonomous vehicle demonstrator. Even if the activity is presented for an autonomous vehicle, the active control can be developed also for a conventional vehicle equipped with an Electronic Power Steering (EPS) or Steer-by-wire architectures. The second chapter describes a Torque Vectoring (TV) control strategy, applied to a Fully Electric Vehicle (FEV) with four independent electric motor (one for each wheel), that aims to optimize the lateral vehicle behavior by a proper electric motor torque regulation. A yaw rate controller is presented and designed in order to achieve a desired steady-state lateral behaviour of the car (handling task). Furthermore, a sideslip angle controller is also integrated to preserve vehicle stability during emergency situations (safety task). LQR, LQR+FF and ISM strategies are formulated and explained for yaw rate and concurrent yaw rate/sideslip angle control techniques also comparing their advantages and weakness points. The TV strategy is implemented and calibrated on a FEV demonstrator by executing experimental maneuvers (step steer, skid pad, lane change and sequence of step steers) thus proving the efficacy of the proposed controller and the safety contribution guaranteed by the sideslip control. The TV could be also applied for internal combustion engine driven vehicles by installing specific torque vectoring differentials, able to distribute the torque generated by the engine to each wheel independently. The TV strategy evaluated in the second chapter can be influenced by the presence of a transmission between themotor (or the engine) and wheels (where the torque control is supposed to be designed): in addition to the mechanical delay introduced by transmission components, the presence of gears backlashes can provoke undesired noises and vibrations in presence of torque sign inversion. The last chapter is thus related to a new method for noises and vibration attenuation for a Dual Clutch Transmission (DCT). This is achieved in a new way by integrating the powertrain control with the braking system control, which are historically and conventionally analyzed and designed separately. It is showed that a torsional preload effect can be obtained on transmission components by increasing the wheel torque and concurrently applying a braking wheel torque. For this reason, a pressure following controller is presented and validated through a Hardware In the Loop (HIL) test rig in order to track a reference value of braking torque thus ensuring the desired preload effect and noises reduction. Experimental results demonstrates the efficacy of the controller, also opening new scenario for global chassis control design. Finally, some general conclusions are drawn and possible future activities and recommendations are proposed for further investigations or improvements with respect to the results shown in the present work

Design, Implementation and Testing of Advanced Control Laws for Fixed-wing UAVs

The present PhD thesis addresses the problem of the control of small fixed-wing Unmanned Aerial Vehicles (UAVs). In the scientific community much research is dedicated to the study of suitable control laws for this category of aircraft. This interest is motivated by the several applications that these platforms can perform and by their peculiarities as dynamical systems. In fact, small UAVs are characterized by highly nonlinear behavior, strong coupling between longitudinal and latero-directional planes, and high sensitivity to external disturbances and to parametric uncertainties. Furthermore, the challenge is increased by the limited space and weight available for the onboard electronics. The aim of this PhD thesis is to provide a valid confrontation among three different control techniques and to introduce an innovative autopilot configuration suitable for the unmanned aircraft field. Three advanced controllers for fixed-wing unmanned aircraft vehicles are designed and implemented: PID with H1 robust approach, L1 adaptive controller and nonlinear backstepping controller. All of them are analyzed from the theoretical point of view and validated through numerical simulations with a mathematical UAV model. One is implemented on a microcontroller board, validated through hardware simulations and tested in flight. The PID with H1 robust approach is used for the definition of the gains of a commercial autopilot. The proposed technique combines traditional PID control with an H1 loop shaping method to assess the robustness characteristics achievable with simple PID gains. It is demonstrated that this hybrid approach provides a promising solution to the problem of tuning commercial autopilots for UAVs. Nevertheless, it is clear that a tradeoff between robustness and performance is necessary when dealing with this standard control technique. The robustness problem is effectively solved by the adoption of an L1 adaptive controller for complete aircraft control. In particular, the L1 logic here adopted is based on piecewise constant adaptive laws with an adaptation rate compatible with the sampling rate of an autopilot board CPU. The control scheme includes an L1 adaptive controller for the inner loop, while PID gains take care of the outer loop. The global controller is tuned on a linear decoupled aircraft model. It is demonstrated that the achieved configuration guarantees satisfying performance also when applied to a complete nonlinear model affected by uncertainties and parametric perturbations. The third controller implemented is based on an existing nonlinear backstepping technique. A scheme for longitudinal and latero-directional control based on the combination of PID for the outer loop and backstepping for the inner loop is proposed. Satisfying results are achieved also when the nonlinear aircraft model is perturbed by parametric uncertainties. A confrontation among the three controllers shows that L1 and backstepping are comparable in terms of nominal and robust performance, with an advantage for L1, while the PID is always inferior. The backstepping controller is chosen for being implemented and tested on a real fixed-wing RC aircraft. Hardware-in-the-loop simulations validate its real-time control capability on the complete nonlinear model of the aircraft adopted for the tests, inclusive of sensors noise. An innovative microcontroller technology is employed as core of the autopilot system, it interfaces with sensors and servos in order to handle input/output operations and it performs the control law computation. Preliminary ground tests validate the suitability of the autopilot configuration. A limited number of flight tests is performed. Promising results are obtained for the control of longitudinal states, while latero-directional control still needs major improvements

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

DECENTRALIZED ROBUST NONLINEAR MODEL PREDICTIVE CONTROLLER FOR UNMANNED AERIAL SYSTEMS

The nonlinear and unsteady nature of aircraft aerodynamics together with limited practical range of controls and state variables make the use of the linear control theory inadequate especially in the presence of external disturbances, such as wind. In the classical approach, aircraft are controlled by multiple inner and outer loops, designed separately and sequentially. For unmanned aerial systems in particular, control technology must evolve to a point where autonomy is extended to the entire mission flight envelope. This requires advanced controllers that have sufficient robustness, track complex trajectories, and use all the vehicles control capabilities at higher levels of accuracy. In this work, a robust nonlinear model predictive controller is designed to command and control an unmanned aerial system to track complex tight trajectories in the presence of internal and external perturbance. The Flight System developed in this work achieves the above performance by using: 1 A nonlinear guidance algorithm that enables the vehicle to follow an arbitrary trajectory shaped by moving points; 2 A formulation that embeds the guidance logic and trajectory information in the aircraft model, avoiding cross coupling and control degradation; 3 An artificial neural network, designed to adaptively estimate and provide aerodynamic and propulsive forces in real-time; and 4 A mixed sensitivity approach that enhances the robustness for a nonlinear model predictive controller overcoming the effect of un-modeled dynamics, external disturbances such as wind, and measurement additive perturbations, such as noise and biases. These elements have been integrated and tested in simulation and with previously stored flight test data and shown to be feasible