During a multi-speed transmission development process, the final calibration of the gearshift controller parameters is usually performed on a physical test bench. Engineers typically treat the mapping from the controller parameters to the gearshift quality as a black-box, and use methods rooted in experimental design -- a purely statistical approach -- to infer the parameter combination that will maximize a chosen gearshift performance indicator. This approach unfortunately requires thousands of gearshift trials, ultimately discouraging the exploration of different control strategies. In this work, we calibrate the feedforward and feedback parameters of a gearshift controller using a model-based reinforcement learning algorithm adapted from Pilco. Experimental results show that the method optimizes the controller parameters with few gearshift trials. This approach can accelerate the exploration of gearshift control strategies, which is especially important for the emerging technology of multi-speed transmissions for electric vehicles.
Multi-speed transmissions can enhance the performance and reduce the overall cost of an electric vehicle, but they also introduce a challenge: avoiding gearshift jerk, which may sometimes prove to be impossible in the presence of motor and clutch saturation. In this article, we introduce three theorems that explicitly define the fundamental limitations to no-jerk gearshifts resulting from motor or actuator saturation. We compare gearshifts that consist of transferring transmission torque from one friction clutch to another, to the case in which one of the clutches is a one-way clutch. We show …
In this paper, we propose an approach to coordinated receding-horizon control of vehicle speed and transmission gearshift for automated battery electric vehicles (BEVs) to achieve improved energy efficiency. The introduction of multi-speed transmissions in BEVs creates an opportunity to manipulate the operating point of electric motors under given vehicle speed and acceleration command, thus providing the potential to further improve the energy efficiency. However, co-optimization of vehicle speed and transmission gearshift leads to a mixed integer nonlinear program (MINLP), solving which can …
Saturations in control signal can challenge the stability proof of a feedback-linearization-based controller, even leading the system unstable [1]. Thus, several approaches are established to avoid reaching the saturation bound [2,3]. Meanwhile, to help design the controller for a quad-tilt-rotor, [1] modeled a tilt vehicle with implementing the feedback-linearization-based controllers. In this article, we provide a gait plan for this tilt vehicle and control it utilizing the feedback linearization. Since saturations exist in the control signals, we study the stability based on Lyapunov theory…
Vehicle stability control systems are important components of active safety systems for road transport. The problem of vehicle lateral stability control is addressed in this paper using active front wheel steering and individual braking. Vehicle lateral stability control means keeping the vehicle yaw rate and the vehicle side slip angle in desired values. For this reason, a model-based controller is designed. The desired yaw rate is obtained from the single track vehicle model and the desired side slip angle is chosen as zero. Controller design consists of two parts, lower and upper controller…
This paper deals with the design of resilient Cooperative Adaptive Cruise Control (CACC) for homogeneous vehicle platoons in which communication is vulnerable to Denial-of-Service (DOS) attacks. We consider DOS attacks as consecutive packet dropouts. We present a controller tuning procedure based on linear matrix inequalities (LMI) that maximizes the resiliency to DOS attacks, while guaranteeing performance and string stability. The design procedure returns controller gains and gives a lower bound on the maximum allowable number of successive packet dropouts. A numerical example is employed to…
This paper investigates the longitudinal control problem in a dynamic traffic environment where driving scenarios change between free-driving scenarios and car-following scenarios. A comprehensive longitudinal controller is proposed to ensure reasonable transient response and steady-state response in scenarios changes, which is independent of planning algorithms. This design takes into account passenger comfort, safety concerns and disturbance rejections, and attempts to meet the requirement of lower cost, faster response, increased comfort, enhanced safety and elevated extendability from the …
In this paper, we present a data-driven Model Predictive Controller that leverages a Gaussian Process to generate optimal motion policies for connected autonomous vehicles in regions with uncertainty in the wireless channel. The communication channel between the vehicles of a platoon can be easily influenced by numerous factors, e.g. the surrounding environment, and the relative states of the connected vehicles, etc. In addition, the trajectories of the vehicles depend significantly on the motion policies of the preceding vehicle shared via the wireless channel and any delay can impact the saf…
This work presents an integrated framework of: vehicle dynamics models, with a particular attention to instabilities and traffic waves; vehicle energy models, with particular attention to accurate energy values for strongly unsteady driving profiles; and sparse Lagrangian controls via automated vehicles, with a focus on controls that can be executed via existing technology such as adaptive cruise control systems. This framework serves as a key building block in developing control strategies for human-in-the-loop traffic flow smoothing on real highways. In this contribution, we outline the fund…
This paper presents a new Lyapunov-based nonlinear model predictive controller (LNMPC) for the attitude control problem of unmanned aerial vehicles (UAVs), which is essential for their functioning operation. The controller is designed based on a quadratic cost function integrating UAV dynamics and system constraints. An additional contraction constraint is then introduced to ensure closed-loop system stability. That constraint is fulfilled via a Lyapunov function derived from a sliding mode controller (SMC). The feasibility and stability of the LNMPC are finally proved. Simulation and comparis…
The design of an automated vehicle controller can be generally formulated into an optimal control problem. This paper proposes a continuous-time finite-horizon approximate dynamicprogramming (ADP) method, which can synthesis off-line near-optimal control policy with analytical vehicle dynamics. Lying on the general Policy Iteration framework, it employs value andpolicy neural networks to approximate the mappings from thesystem states to value function and control inputs, respectively. The proposed method can converge to the near-optimal solutionof the finite-horizon Hamilton-Jacobi-Bellman (HJ…
The plants of nano air vehicles (NAVs) are generally unstable, adversely coupled, and uncertain. Besides, the autopilot hardware of a NAV has limited sensing and computational capabilities. Hence, these vehicles need a single controller referred to as Robust Simultaneously Stabilizing Decoupling (RSSD) output feedback controller that achieves simultaneous stabilization, desired decoupling, robustness, and performance for a finite set of unstable multi-input-multi-output adversely coupled uncertain plants. To synthesize a RSSD output feedback controller, a new method that is based on a central …
This paper presents a vehicle lateral controller based on spiking neural networks capable of replicating the behavior of a model-based controller but with the additional ability to perform online adaptation. By making use of neural plasticity and thanks to reward modulation learning, neural connections are modified to adjust the response according to the committed error. Therefore, the error performs a similar role to dopamine in a biological system, modulating the learning process based on spiking time dependency. The connections are initially set to replicate behavior of the model-based cont…
In this paper, we present an effective online tube-based model predictive control (T-MPC) solution for autonomous driving that aims at improving the computational load while ensuring robust stability and performance in fast and disturbed scenarios. We focus on reformulating the non-linear original problem into a pseudo-linear problem by transforming the non-linear vehicle equations to be expressed in a Linear Parameter Varying (LPV) form. An scheme composed by a nominal controller and a corrective local controller is propossed. First, the local controller is designed as a polytopic LPV-H$_{\in…
To stabilize a conventional quadrotor, simplified equivalent vehicles, e.g., autonomous car, are developed to test the designed controller. Based on that, various controllers based on feedback linearization have been developed. With the recently developed concept of tilt-rotor, there lacks the simplified or equivalent model, however. Indeed, the tilt structure is relatively unusual in vehicles. In this research, we put forward a unique fictional vehicle with tilt structure, which is to help evaluate the property of the tilt-structure-aimed controllers. One phenomenon, state drift, in controlli…
In this paper, we propose a framework for the longitudinal control of connected and automated vehicles traveling in mixed traffic consisting of connected and non-connected human-driven vehicles. Reactive and predictive controllers are proposed. Reactive controllers are given by explicit feedback control laws. In predictive controllers, the control input is optimized in a receding-horizon fashion, which depends on the predictions of motions of preceding vehicles. Beyond-line-of-sight information is obtained via vehicle-to-vehicle (V2V) communication, and is utilized in the proposed reactive and…
The isoline tracking of this work is concerned with the control design for a sensing vehicle to track a desired isoline of an unknown scalar field. To this end, we propose a simple PI-like controller for a Dubins vehicle in the GPS-denied environments. Our key idea lies in the design of a novel sliding surface based error in the standard PI controller. For the circular field, we show that the P-like controller can globally regulate the vehicle to the desired isoline with the steady-state error that can be arbitrarily reduced by increasing the P gain, and is eliminated by the PI-like controller…
This paper presents the design of an integral controller for vehicle platoons with actuator dynamics. The proposed controller ensures string stability with disturbances and simultaneously compensates for constant disturbances through integral action. Sufficient conditions for string stability are satisfied by the use of a suitable state transformation. The proposed controller guarantees disturbance string stability for a prescribed time constant of the actuator dynamics, and we show through simulation that platoons with faster dynamics are also made disturbance string stable.
Urban traffic congestion is a chronic problem faced by many cities. It is essentially inefficient infrastructure use which results in increased vehicle fuel consumption and emissions. This in turn adds extra costs to commuters and businesses. Addressing this issue is therefore of paramount interest due to the perceived dual benefit. Many technologies were and are being developed. These include adaptive traffic signals, dedicated lanes, etc. This paper presents a simple platooning algorithm that maintains relatively small distances (pre-specified time gap) between consecutive vehicles to enhanc…
This paper investigates the lateral control problem in vehicular path-following when the feedback sensor(s) are mounted at an arbitrary location in the longitudinal symmetric axis. We point out that some existing literature has abused the kinematic bicycle model describing the motion of rear axle center for other locations, which may lead to poor performance in practical implementations. A new nonlinear controller with low-complexity and high-maneuverability is then designed that takes into account senor mounting location, driving comfort and transient response with large initial errors. Desig…
This paper focuses on energy-efficient longitudinal controller design for a connected automated truck that travels in mixed traffic consisting of connected and non-connected vehicles. The truck has access to information about connected vehicles beyond line of sight using vehicle-to-vehicle (V2V) communication. A novel connected cruise control design is proposed which incorporates additional delays into the control law when responding to distant connected vehicles to account for the finite propagation of traffic waves. The speeds of non-connected vehicles are modeled as stochastic processes. A …
This paper proposes a novel extension of the Simplex architecture with model switching and model learning to achieve safe velocity regulation of self-driving vehicles in dynamic and unforeseen environments. To guarantee the reliability of autonomous vehicles, an $\mathcal{L}_{1}$ adaptive controller that compensates for uncertainties and disturbances is employed by the Simplex architecture as a verified safe controller to tolerate concurrent software and physical failures. Meanwhile, safe switching controller is incorporated into the Simplex for safe velocity regulation through the integration…
This paper presents an online-capable controller for the energy management system of a parallel hybrid electric vehicle based on model predictive control. Its task is to minimize the vehicle's fuel consumption along a predicted driving mission by calculating the distribution of the driver's power request between the electrical and the combustive part of the powertrain, and by choosing the driving mode, which depends on the vehicle's clutch state. The inclusion of the clutch state in a model predictive control structure is not trivial because the underlying optimization problem becomes a mixed-…
Generic Evolving Self-Organizing Neuro-Fuzzy Control of Bio-inspired Unmanned Aerial Vehicles
University of New South Wales at the Australian Defence Force Academy（新南威尔士大学澳大利亚国防军学院）；Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）
Connected and Automated Vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driving conditions. In particular, the Eco-Driving problem seeks to design optimal speed and power usage profiles based on available information from connectivity and advanced mapping features to minimize the fuel consumption over an itinerary. This paper presents a hierarchical multi-layer Model Predictive Control (MPC) approach for improving the fuel economy of a 48V mild-hybrid powertrain in a connected vehicle enviro…
A fundamental problem in traffic networks is driving under safety and limited physical space constraints. In this paper, we design longitudinal vehicle controllers and study the dynamics of a system of homogeneous vehicles on a single-lane ring road in order to understand the interplay of limited space, speed, and safety. Each vehicle in the system either operates in the cruise control mode or follows a vehicle ahead by keeping a safe time headway. We show that if the number of vehicles is less than a certain critical threshold, vehicles can occupy the limited space in many different configura…
This article concerns the development of the Vector Field Orientation - Active Disturbance Rejection (VFO-ADR) cascaded path-following controller for underactuated vehicles moving in a 3-dimensional space. The proposed concept of a cascaded control structure decouples system kinematics from system dynamics, resembling the approach utilized for nonholonomic systems. Thanks to the use of an ADR control approach in the dynamic-level controller, the proposed control structure is robust to even significant model uncertainties and external disturbances. Application of an error-based form of the Exte…
An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has myriads of exploitable vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. These standalone or networked EVCS open up large attack surfaces for the local or state-funded adversaries. The state-of-the-art approaches are not agile and intelligent enough to defend against and mitigate advanced persistent threats (APT). We propose the data-driven model-free distributed intelligence based o…
