J. Karl Hedrick's research while affiliated with University of California, Berkeley and other places
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Publications (383)
Collaboration requires coordination, and we coordinate by anticipating our teammates’ future actions and adapting to their plan. In some cases, our teammates’ actions early on can give us a clear idea of what the remainder of their plan is, i.e. what action sequence we should expect. In others, they might leave us less confident, or even lead us to...
This paper proposes a new adaptation methodology to find the control inputs for a class of nonlinear systems with time-varying bounded uncertainties. The proposed method does not require any prior knowledge of the uncertainties including their bounds. The main idea is developed under the structure of adaptive sliding mode control; an update law dec...
Collaboration requires coordination, and we coordinate by anticipating our teammates' future actions and adapting to their plan. In some cases, our teammates' actions early on can give us a clear idea of what the remainder of their plan is, i.e. what action sequence we should expect. In others, they might leave us less confident, or even lead us to...
The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications. Robots will need to predict and adapt to the actions of human collaborators in order to achieve good performance and improve safety and end-user adoption. This paper evaluates a human-robot collaboration scheme that combines the task al...
A classical approach for guaranteeing persistent feasibility of model predictive controllers during setpoint changes adds an artificial reference variable, whereby allowing for reference offset at a cost specified by an additional term in the cost function. Typically, the classical approach employs a linear quadratic regulator parameterized by the...
Adaptive cruise control is one of the most widely used vehicle driver assistance systems. However, uncertainty about drivers’ lane change maneuvers in surrounding vehicles, such as unexpected cut-in, remains a challenge. We propose a novel adaptive cruise control framework combining convolution neural network (CNN)-based lane-change-intention infer...
This paper presents a novel distributed Bayesian filtering (DBF) method using measurement dissemination (MD) for multiple unmanned ground vehicles (UGVs) with dynamically changing interaction topologies. Different from statistics dissemination (SD)-based algorithms that transmit posterior distributions or likelihood functions, this method relies on...
Active control of electric powertrains is challenging, due to the fact that backlash and structural flexibility in transmission components can cause severe performance degradation or even instability of the control system. Furthermore, high impact forces in transmissions reduce driving comfort and possibly lead to damage of the mechanical elements...
Sliding mode control (SMC) is a robust and computationally efficient solution for tracking control problems of highly nonlinear systems with a great deal of uncertainty. High frequency oscillations due to chattering phenomena and sensitivity to data sampling imprecisions limit the digital implementation of conventional first order continuous-time S...
This paper presents a measurement dissemination-based distributed Bayesian filter (DBF) for a network of unmanned ground vehicles (UGVs). The DBF utilizes the Latest-In-and-Full-Out (LIFO) exchange protocol to disseminate the sensor measurements within local neighbors. Different from existing statistics dissemination strategies that transmit poster...
In this paper, a gain scheduled linear quadratic tracking system (LQTS) tuned optimally by an evolutionary strategy (ES) is devised to reduce the total tailpipe hydrocarbon (HC) emissions of an automotive engine over the coldstart period. As the engine’s behavior during coldstart operations is nonlinear, the system dynamics is clearly analyzed and...
Deep learning-based approaches have been widely used for training controllers for autonomous vehicles due to their powerful ability to approximate nonlinear functions or policies. However, the training process usually requires large labeled data sets and takes a lot of time. In this paper, we analyze the influences of features on the performance of...
Personalized driver models play a key role in the development of advanced driver assistance systems and automated driving systems. Traditionally, physical-based driver models with fixed structures usually lack the flexibility to describe the uncertainties and high non-linearity of driver behaviors. In this paper, two kinds of learning-based car-fol...
Bridging the gap between designed and implemented model-based controllers is a major challenge in the design cycle of industrial controllers. This gap is created due to (i) digital implementation of controller software that introduces sampling and quantization uncertainties, and (ii) uncertainties in the modeled plant's dynamics. In this paper, a n...
Considering the controllability and observability of the braking torques of the hub motor, Integrated Starter Generator (ISG), and hydraulic brake for four-wheel drive (4WD) hybrid electric cars, a distributed and self-adaptive vehicle speed estimation algorithm for different braking situations has been proposed by fully utilising the Electronic St...
![Fig. 2. Typical types of information flow topology [8]: (a)...](profile/Shengbo-Li-2/publication/311649038/figure/fig1/AS:439429773959169@1481779630917/Typical-types-of-information-flow-topology-8-a-bidirectional-type-b_Q320.jpg)
The topological variety significantly affects the platooning of multi-vehicle systems. This paper presents a distributed sliding mode control (SMC) method for vehicular platoons with positive definite topologies. The platoon model is assumed to be homogeneous with strict-feedback nonlinear node dynamics. The design of distributed SMC is divided int...
We propose in this paper an autonomous motion planning framework for companion robots to accompany humans in a socially desirable manner, which takes safety and comfort requirements into account. The overall framework consists of two parts: first, a novel parallel interacting multiple model-unscented Kalman filter (PIMM-UKF) approach is developed t...
Analog-to-digital conversion (ADC) and uncertainties in modeling the plant dynamics are the main sources of imprecisions in the design cycle of model-based controllers. These implementation and model uncertainties should be addressed in the early stages of the controller design, otherwise they could lead to failure in the controller performance and...
The key to the launching control of a dual clutch transmission (DCT) lies in the coordination control of the engine and dual clutches, along with the accurate closed-loop control of the torque transmitted by each clutch and the output torque of the engine. The implementation feasibility and precision of the clutch torque closed-loop control are com...
This paper presents a performance assessment of the recently developed receding horizon sliding control (RHSC) algorithm. The RHSC strategy is extended to multi-input multi-output systems and will be evaluated using a model predictive control (MPC) approach as a benchmark. The focus of this investigation is on comparing performance in computational...
Uncertainties caused by sampling and quantization (Analog to Digital Conversion, ADC) generate discrepancies between actual and recorded data. This results in degrading controller performance in general. However, if these uncertainties are considered in designing a controller, performance can be improved. In this paper, a Receding Horizon Sliding C...
This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a priori unknown desired set point. The vehicles (or nodes) in a platoon are dynamically decoupled but constrained by spatial geometry. Each node is assigned a local open-loop optimal control problem only...
![Fig. 1. Background of previous studies [5], [10]-[24] on controller...](profile/M-Reza-Amini/publication/292146531/figure/fig3/AS:667788469362708@1536224586777/Background-of-previous-studies-5-10-24-on-controller-design-against-sampling-and_Q320.jpg)
Analog-to-digital conversion (ADC) is one of the main sources of controller implementation imprecisions due to sampling and quantization. In this paper, a new control approach is developed to mitigate the ADC imprecisions by (i) identifying the ADC imprecisions in the early stages of a controller design cycle, (ii) developing a mechanism for real-t...
The aim of the current study is to probe the potential of receding horizon sliding control (RHSC) technique for reducing the coldstart hydrocarbon (HC) emissions of automotive spark-ignited (SI) engines. The RHSC approach incorporates the potentials of sliding control (SC) and nonlinear model predictive control (NMPC) to employ the future informati...
In this paper, a nonlinear model predictive controller (NMPC) with a multiagent heuristic optimizer, which is called dynamic particle swarm optimization (DPSO), is proposed to reduce the cold-start hydrocarbon (HC) emission of an automotive spark-ignited engine. In general, the cold-start HC emission reduction has been proven to be a very challengi...
Verification and validation (V&V) are essential stages in the design cycle of industrial controllers to remove the gap between the designed and implemented controller. In this study, a model-based adaptive methodology is proposed to enable easily verifiable controller design based on the formulation of a sliding mode controller (SMC). The proposed...
In this research, a high-performance predictive controller is developed for automotive coldstart emission reductions. The proposed control scheme combines a hybrid switching predictive controller (HSPC) with proportional integral derivative (PID) gains to simultaneously minimize the cumulative hydrocarbon emissions (HCcum) and the control input var...
Vehicle control systems need to prognosticate future vehicle states in order to improve energy efficiency. This paper compares four approaches that are used to identify the parameters of a longitudinal vehicle dynamics model used for the prediction of vehicle tractive forces. All of the identification approaches build on a standard Kalman filter. M...
The key toward realizing no-impact gear shifting for dual clutch transmission (DCT) lies in the coordination control between the engine and dual clutches, as well as the accurate closed-loop control of torque transmitted by each clutch and the output torque of the engine. However, the implementation and control precision of closed-loop control are...
Target search using autonomous robots is an important application for both civil and military scenarios. In this paper, a model predictive control (MPC)-based probabilistic search method is presented for a ground robot to localize a stationary target in a dynamic environment. The robot is equipped with a binary sensor for target detection, of which...
In this paper, a robust control architecture is proposed for lane-keeping and obstacle avoidance of autonomous ground vehicles. A two-level hierarchical controller is used to separate the planning and tracking problems. At the higher-level, we solve a nonlinear model predictive control (MPC) problem with an oversimplified point-mass model. The desi...
Motion planning of human-companion robots is a challenging problem and its solution has numerous applications. This paper proposes an autonomous motion planning framework for human-companion robots to accompany humans in a socially desirable manner, which takes into account the safety and comfort requirements. An Interacting Multiple Model-Unscente...
In the current study, a novel intelligent control algorithm is proposed for the efficient reduction of coldstart hydrocarbon (HC) emissions of an automotive engine. The proposed intelligent controller inherits the computational advantages of two advanced techniques, namely hybrid switching predictive controller and extreme learning machine (ELM). T...
In this paper we present a new approach of using input-output linearization to control a single input, single output, input-affine nonlinear non-minimum phase system. We will show that, if the linearized system is stabilizable, we can redefine the output of the system such that the input-output linearized system is locally asymptotically stable. Fu...
This paper presents a new controller for prevention of unintended roadway departures using model predictive control (MPC). The uncertainty with the driver's behavior is taken into account as the Gaussian disturbance. Correspondingly , we impose a lower bound on the probability of the vehicle remaining within the lane. Using current information of t...
In this paper, a method of robust model predictive control for constrained linear discrete-time systems with bounded disturbances is presented. The approach is based on modifying the concept of discrete-time integral sliding mode control into an optimal constrained control problem. By introducing an additional sliding mode control term into the sta...
The high level modeling (HLM) paradigm is a rapid modeling method that is being developed by the Toyota Motor Corporation. Emphasizing the use of physical conservation laws, it streamlines the model finding process for control applications. While recent works show the successful application of the high level modeling approach to physical systems of...
Recent advances in the traffic monitoring systems have made traffic velocity information accessible in real time. This paper proposes a supervised predictive energy management framework aiming to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV) by incorporating dynamic traffic feedback data. Compared with conventiona...
Sliding mode control (SMC) is one of the few controller design methodologies that can be applied to highly nonlinear and uncertain systems. In most mechanical applications, a smoothed version of SMC that we call 'sliding control' is employed to keep the system trajectories close to but not necessarily on a stable differential/difference manifold. I...
Reduction of hydrocarbon emissions during the engine cold start process is a major design and control goal in the automotive industry in recent years, with considerable impact on not only the vehicle's fuel economy, but the environment as well. The key to producing the most emission efficient powertrain system is the ability to drive the engine var...
Automotive controllers are often first designed in a simulation environment using continuous time models of the controller and vehicle plant. Unfortunately, the controller's implementation in software and deployment onto a microcontroller has ramifications for performance and cost. In this paper, we use an automotive case study of a yaw moment cont...
Autonomous vehicles can risk dangerous rollover if they corner without taking roll motion into consideration.
This paper proposes a control algorithm to follow a curved road while simultaneously preventing rollover. Model predictive control is applied to minimize roll motion throughout cornering. The prediction of vehicle state is based on a four-w...
Autonomous vehicle has been one of the highly investigated topics in both academia and industry. A car that can drive without the driver's intervention changes not only the dynamics of the automobile industry but also everyday life of individuals. This paper demonstrates the road tracking capability of a steering controller that is designed based o...
The main contribution of this paper is the development of a nonlinear multiple-input, multiple-output (MIMO) tracking controller design using a discrete time sliding control approach. A Lyapunov stability analysis is used to prove the asymptotic stability of both the output errors as well as the parameter estimation errors. The application of the “...
Recent advances in traffic monitoring systems have made real-time traffic velocity data ubiquitously accessible for drivers. This paper develops a traffic data-enabled predictive energy management framework for a power-split plug-in hybrid electric vehicle (PHEV). Compared with conventional model predictive control (MPC), an additional supervisory...
Verification and Validation (V&V) are essential stages in the design cycle of automotive controllers to remove the gap between the designed and implemented controller. In this paper, an early model-based methodology is proposed to reduce the V&V time and improve the robustness of the designed controllers. The application of the proposed methodology...
Domestic grazing animals follow simple, scalable rules to assign themselves trajectories to cover a pasture. We explain how to adapt these rules for an information gathering system based on a realistic robot motion model and Kalmanfilter based evidence grid that accounts for both bandwidth and sensor limitations. Our results show that this algorith...
When human and robotic agents work together, the challenge in assigning tasks lies in exploiting human strengths, such as expertise and intuition, while still managing the heterogeneous agent team in a near-optimal way. An extension to the Gale-Shapley stable matching algorithm that combines a sequential greedy approach is proposed to apply to task...
This paper proposes a framework for autonomous vehicles to collaborate with human agents as peers in task completion scenarios. In this framework, the autonomous vehicles utilize the Bayesian inference method to determine human intention. An optimal task allocation that minimizes the mission completion time while respecting the intention of the hum...
Model predictive control (MPC) is a popular technique for the development of active safety systems. However, its high computational cost prevents it from being implemented on lower-cost hardware. This paper presents a computationally efficient predictive controller for lane keeping assistance systems. The controller shares control with the driver,...
Simply run each matlab file to reproduce the results of:
@INPROCEEDINGS{Hamann2014,
author = {Hamann, H. and Hedrick, J.K. and Rhode, S. and Gauterin, F.},
title = {Tire Force Estimation for a Passenger Vehicle with the Unscented
Kalman Filter},
booktitle = {Intelligent Vehicles Symposium (IV), 2014 IEEE},
year = {2014},
note = {in submission},
ab...
We propose a nonlinear estimator for information-theoretic control design, which is usually employed for control of mobile sensor teams. Specifically, we consider estimation problems with marginalized sub-structures. The localization of a diffusive source via concentration measurements gives rise to such problems. We derive a Bayesian estimator for...
We propose a nonlinear estimator for information-theoretic control design, which is usually employed for control of mobile sensor teams. Specifically, we consider estimation problems with marginalized sub-structures. The localization of a diffusive source via concentration measurements gives rise to such problems. We derive a Bayesian estimator for...
This paper proposes a novel algorithm to identify three inertial parameters: sprung mass, yaw moment of inertia, and longitudinal position of the center of gravity. A four-wheel nonlinear vehicle model with roll dynamics and a correlation between the inertial parameters is used for a dual unscented Kalman filter to simultaneously identify the inert...
The main purpose of this paper is to present a new path planning framework, called directP ath, aimed to handle a priori environment knowledge efficiently. Our algorithm is an expert system designed to specifically exploit that knowledge, and then eventually collaborate with other frameworks in order to improve overall path planning performances. I...
Wide-spread use of the model-based design process in automotive powertrain system development is increasing the need for accurate dynamic models. Model-based design inherently requires these models to be rapidly producible and easily verifiable. The High Level Modeling (HLM) approach and corresponding High Level Modeling Tool (HLMT) have been devel...
Automotive powertrain control strategies are a key component of the software design process for vehicle systems. During implementation of control algorithms on real systems, errors often arise that prove costly if they are not detected until the Verification & Validation process. Thus, it is advantageous to mitigate potential uncertainties early on...
A robust method to estimate tire forces for a passenger vehicle with the Unscented Kalman Filter (UKF) is provided. Only standard vehicle sensors were used and no a priori knowledge of tire and road properties was required. The estimator uses the bicycle model and a random walk tire force model. The tire force estimates were compared to a CarSim re...
This paper presents a novel predictive control approach based on the unscented transformation with recursive feasibility analysis and an experimental validation for lane keeping of semi-autonomous vehicles. The optimization problem to be solved is nonlinear with stochastic disturbances and probability constraints on states. The unscented transforma...
We consider a single-pursuer-multiple-evader pursuit-evasion game in which a team of evaders aims to delay the capture by a faster pursuer. We extend our previous open-loop formulation (and its solution) of the game to incorporate more realistic settings: A pursuer with uncertain position and evaders with limited turning rates. The formulation prov...
This paper extends the design and analysis methodology of dynamic surface control (DSC) in Song and Hedrick, 2011, for a more general class of nonlinear systems. When rotational mechanical systems such as lateral vehicle control and robot control are considered for applications, sinusoidal functions are easily included in the equation of motions. I...
Automotive powertrain control strategies are implemented on digital systems with finite sampling rates. If an infinite sampling rate is assumed during design, the resulting control law can exhibit degraded performance and instability when digitally implemented. In this work, a discrete-time sliding mode formulation is developed to overcome this har...
The hardware used for software implementation on a physical system introduces uncertainty into the controller. If neglected during design, this uncertainty can lead to poor controller performance, resulting in significant design and verification iterations. In this work, the effect of sampling time, quantization, and fixed-point computation are dir...
This paper presents a multi-objective safety system that is capable of avoiding unintended collisions with stationary and moving road obstacles, vehicle control loss as well as unintended roadway departures. The safety system intervenes only when there is an imminent safety risk while full control is left to the driver otherwise. The problems of as...
Contemporary safety systems, such as obstacle avoidance and lane-keeping assistance, require good approximations of vehicle inertial properties, such as sprung mass and yaw moment of inertia, which can vary significantly based on the number of passengers, seating arrangement, and luggage. This paper demonstrates the implementation of two model-base...
A robust control design is proposed for the lane-keeping and obstacle avoidance of semiautonomous ground vehicles. A robust Model Predictive Controller (MPC) is used in order to enforce safety constraints with minimal control intervention. An uncertain driver model is used to obtain sets of predicted vehicle trajectories in closed-loop with the pre...
This paper presents the design of a novel active safety system preventing unintended roadway departures. The proposed framework unifies threat assessment, stability, and control of passenger vehicles into a single combined optimization problem. A nonlinear model predictive control (MPC) problem is formulated, where nonlinear vehicle dynamics, in cl...
A novel direct yaw moment controller is developed in this paper. A hierarchical control architecture is adopted in the controller design. In the upper controller, a driver model and a vehicle model are used to obtain the driver's intention and the vehicle states, respectively. The upper controller determines the desired yaw moment by means of slidi...
Lateral tire deflection enables the estimation of the tire-road friction coefficient. Vehicle steering, such as driving on a curved highway, can influence the friction coefficient estimation. This paper demonstrates an algorithm to estimate the tire-road friction coefficient when a vehicle is steering. The relationship between the friction coeffici...
Reduction of cold start hydrocarbon (HC) emission requires a proper compromise between low engine-out HC emission and fast light-off of the three way catalytic converter (TWC). In this paper a model based approach is used to design and optimize a hybrid switching system for reducing HC emission of a mid-sized passenger car during the cold start pha...