Model predictive control (MPC) is widely used for path tracking of autonomous vehicles due to its ability to handle various types of constraints. However, a considerable predictive error exists because of the error of mathematics model or the model linearization. In this paper, we propose a framework combining the MPC with a learning-based error estimator and a feedforward compensator to improve the path tracking accuracy. An extreme learning machine is implemented to estimate the model based predictive error from vehicle state feedback information. Offline training data is collected from a vehicle controlled by a model-defective regular MPC for path tracking in several working conditions, respectively. The data include vehicle state and the spatial error between the current actual position and the corresponding predictive position. According to the estimated predictive error, we then design a PID-based feedforward compensator. Simulation results via Carsim show the estimation accuracy of the predictive error and the effectiveness of the proposed framework for path tracking of an autonomous vehicle.
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；University of Saarland（萨尔大学）；Technical University of Berlin（柏林工业大学）
The aim of this work is to control the longitudinal position of an autonomous vehicle with an internal combustion engine. The powertrain has an inherent dead-time characteristic and constraints on physical states apply since the vehicle is neither able to accelerate arbitrarily strong, nor to drive arbitrarily fast. A model predictive controller (MPC) is able to cope with both of the aforementioned system properties. MPC heavily relies on a model and therefore a strategy on how to obtain multiple linear state space prediction models of the nonlinear system via input/output data system identifi…
Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. The model needs to capture the system behavior in multiple flight regimes and operating conditions, including those producing highly nonlinear effects such as aerodynamic forces and torques, rotor interactions, or possible system configuration modifications. Classical approaches rely on handcrafted models and struggle to generalize and scale to capture these effects. In this paper, we present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadr…
Delft University of Technology（代尔夫特理工大学）；University of Leuven (KU Leuven)（鲁汶大学）
This paper proposes a new robust trajectory tracking error-based control approach for unmanned ground vehicles. A trajectory tracking error-based model is used to design a linear model predictive controller and its control action is combined with feedforward and robust control actions. The experimental results show that the proposed control structure is capable to let a tractor-trailer system track both linear and curvilinear target trajectories with low tracking error.
University of Toronto（多伦多大学）；University of Tartu（塔尔图大学）
This paper proposes a Model Predictive Control (MPC) algorithm for target tracking amongst static and dynamic obstacles. Our main contribution lies in improving the computational tractability and reliability of the underlying non-convex trajectory optimization. The result is an MPC algorithm that runs real-time on laptops and embedded hardware devices such as Jetson TX2. Our approach relies on novel reformulations for the tracking, collision, and occlusion constraints that induce a multi-convex structure in the resulting trajectory optimization. We exploit these mathematical structures using t…
We consider the problem of bridging the gap between geometric tracking control theory and implementation of model predictive control (MPC) for robotic systems operating on manifolds. We propose a generic on-manifold MPC formulation based on a canonical representation of the system evolving on manifolds. Then, we present a method that solves the on-manifold MPC formulation by linearizing the system along the trajectory under tracking. There are two main advantages of the proposed scheme. The first is that the linearized system leads to an equivalent error system represented by a set of minimal …
Institute for Dynamic Systems and Control, ETH Zürich（苏黎世联邦理工学院动态系统与控制研究所）
This paper presents an offset-free model predictive controller for fast and accurate control of a spherical soft robotic arm. In this control scheme, a linear model is combined with an online disturbance estimation technique to systematically compensate model deviations. Dynamic effects such as material relaxation resulting from the use of soft materials can be addressed to achieve offset-free tracking. The tracking error can be reduced by 35% when compared to a standard model predictive controller without a disturbance compensation scheme. The improved tracking performance enables the realiza…
California Institute of Technology（加州理工学院）；ETH Zurich（苏黎世联邦理工学院）；Northeastern University（东北大学）
The feasibility of performing airborne and ground manipulation, perception, and reconnaissance using wheeled rovers, unmanned aerial vehicles, CubeSats, SmallSats and more have been evaluated before. Among all of these solutions, balloon-based systems possess merits that make them extremely attractive, e.g., a simple operation mechanism and endured operation time. However, there are many hurdles to overcome to achieve robust loitering performance in balloon-based applications. We attempt to identify design and control challenges, and propose a novel robotic platform that allows for the applica…
Harbin Institute of Technology Shenzhen（哈尔滨工业大学深圳校区）
To promote the widespread use of mobile robots in diverse fields, the performance of trajectory tracking must be ensured. To address the constraints and nonlinear features associated with mobile robot systems, we apply nonlinear model predictive control (MPC) to realize the trajectory tracking of mobile robots. Specifically, to alleviate the online computational complexity of nonlinear MPC, this paper devises a lattice piecewise affine (PWA) approximation method that can approximate both the nonlinear system and control law of explicit nonlinear MPC. The kinematic model of the mobile robot is …
We propose to combine model predictive control with deep learning for the task of accurate human motion tracking with a robot. We design the MPC to allow switching between the learned and a conservative prediction. We also explored online learning with a DyBM model. We applied this method to human handwriting motion tracking with a UR-5 robot. The results show that the framework significantly improves tracking performance.
University of Michigan（密歇根大学）；Cornell University（康奈尔大学）
This paper reports on a new error-state Model Predictive Control (MPC) approach to connected matrix Lie groups for robot control. The linearized tracking error dynamics and the linearized equations of motion are derived in the Lie algebra. Moreover, given an initial condition, the linearized tracking error dynamics and equations of motion are globally valid and evolve independently of the system trajectory. By exploiting the symmetry of the problem, the proposed approach shows faster convergence of rotation and position simultaneously than the state-of-the-art geometric variational MPC based o…
Norwegian University of Science and Technology（挪威科技大学）；SINTEF DIGITAL（SINTEF数字研究所）
In this article we show how the model predictive path following controller allows robotic manipulators to stop at obstructions in a way that model predictive trajectory tracking controllers cannot. We present both controllers as applied to robotic manipulators, simulations for a two-link manipulator using an interior point solver, consider discretization of the optimal control problem using collocation or Runge-Kutta, and discuss the real-time viability of our implementation of the model predictive path following controller.
This paper presents a novel trajectory tracker for autonomous quadrotor navigation in dynamic and complex environments. The proposed framework integrates a distributional Reinforcement Learning (RL) estimator for unknown aerodynamic effects into a Stochastic Model Predictive Controller (SMPC) for trajectory tracking. Aerodynamic effects derived from drag forces and moment variations are difficult to model directly and accurately. Most current quadrotor tracking systems therefore treat them as simple `disturbances' in conventional control approaches. We propose Quantile-approximation-based Dist…
Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles
Systems and Control Laboratory, HUN-REN Inst. for Computer Science and Control（系统与控制实验室，HUN-REN计算机科学与控制研究所）；Control Systems Group, Eindhoven University of Technology（控制系统组，埃因霍温理工大学）；Vehicle Research Center, Széchenyi István University（车辆研究中心，塞梅尔维斯大学）；ETH Zürich（苏黎世联邦理工学院）
Tongji University（同济大学）；New York University（纽约大学）；Tsinghua University（清华大学）
As a crucial robotic perception capability, visual tracking has been intensively studied recently. In the real-world scenarios, the onboard processing time of the image streams inevitably leads to a discrepancy between the tracking results and the real-world states. However, existing visual tracking benchmarks commonly run the trackers offline and ignore such latency in the evaluation. In this work, we aim to deal with a more realistic problem of latency-aware tracking. The state-of-the-art trackers are evaluated in the aerial scenarios with new metrics jointly assessing the tracking accuracy …
University of Leuven (KU Leuven)（鲁汶大学）；Nanyang Technological University（南洋理工大学）
This paper addresses the trajectory tracking problem of an autonomous tractor-trailer system by using a decentralized control approach. A fully decentralized model predictive controller is designed in which interactions between subsystems are neglected and assumed to be perturbations to each other. In order to have a robust design, a tube-based approach is proposed to handle the differences between the nominal model and real system. Nonlinear moving horizon estimation is used for the state and parameter estimation after each new measurement, and the estimated values are fed to robust tube-base…
National Institute of Informatics（国立信息学研究所）；Hunan University（湖南大学）
In the path planning problem of autonomous application, the existing studies separately consider the path planning and trajectory tracking control of the autonomous vehicle and few of them have integrated the trajectory planning and trajectory control together. To fill in this research gap, this study proposes an integrated trajectory planning and trajectory control method. This paper also studies the collision avoidance problem of autonomous by considering static and dynamic obstacles. Simulation results have been presented to show the effectiveness of the proposed control method.
Real-Time Nonlinear Model Predictive Control of Heavy-Duty Skid-Steered Mobile Platform for Trajectory Tracking Tasks
Unit of Automation Technology and Mechanical Engineering, Faculty of Engineering and Natural Sciences, Tampere University（自动化技术与机械工程单元，工程与自然科学学院，塔尔皮亚大学）
Multi-Reference Path Tracking Control for an Agricultural Tractor with Nonlinear Model Predictive Control
Chair of Agrimechatronics, Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich（慕尼黑工业大学慕尼黑机器人与机器智能研究所（MIRMI）农业机电一体化教席）
Motion planning for autonomous vehicles requires spatio-temporal motion plans (i.e. state trajectories) to account for dynamic obstacles. This requires a trajectory tracking control process which faithfully tracks planned trajectories. In this paper, a control scheme is presented which first optimizes a planned trajectory and then tracks the optimized trajectory using a feedback-feedforward controller. The feedforward element is calculated in a model predictive manner with a cost function focusing on driving performance. Stability of the error dynamic is then guaranteed by the design of the fe…
IIIT-Hyderabad（海得拉巴国际信息技术学院）；TCS Research（塔塔咨询服务研究院）；University of Michigan（密歇根大学）
This paper proposes a novel controller framework that provides trajectory tracking for an Aerial Manipulator (AM) while ensuring the safe operation of the system under unknown bounded disturbances. The AM considered here is a 2-DOF (degrees-of-freedom) manipulator rigidly attached to a UAV. Our proposed controller structure follows the conventional inner loop PID control for attitude dynamics and an outer loop controller for tracking a reference trajectory. The outer loop control is based on the Model Predictive Control (MPC) with constraints derived using the Barrier Lyapunov Function (BLF) f…
MR-MPCC: Model Predictive Contouring Control for Robotic Manipulator Tracking under Moving Reference Frames
University of Technology Sydney（悉尼科技大学）；Robotics Institute, University of Technology Sydney（悉尼科技大学机器人研究所）；University of Edinburgh（爱丁堡大学）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）
University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；Shanghai Jiao Tong University（上海交通大学）
This paper presents an image-based visual servo control (IBVS) method for a first-person-view (FPV) quadrotor to conduct aggressive aerial tracking. There are three major challenges to maneuvering an underactuated vehicle using IBVS: (i) finding a visual feature representation that is robust to large rotations and is suited to be an optimization variable; (ii) keeping the target visible without sacrificing the robot's agility; and (iii) compensating for the rotational effects in the detected features. We propose a complete design framework to address these problems. First, we employ a rotation…
School of Mechanical and Electrical Engineering, Guangzhou University（广州大学机械与电气工程学院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）
To reduce the contour error of the end-effector of a robotic manipulator during trajectory tracking, a dual-mode synchronization predictive control is proposed. Firstly, the dynamic model of n-DoF robotic manipulator is discretized by using the Taylor expansion method, and the mapping relationship between the joint error in the joint space and the contour error in task space is constructed. Secondly, the synchronization error and the tracking error in the joint space are defined, and the coupling error of joints is derived through the coupling coefficient . Thirdly, a dual-mode synchronization…
