This work proposes a nonlinear model predictive control-based guidance strategy for unmanned surface vehicles, focused on path following. The application of this strategy, in addition to overcome drawbacks of previous line-of-sight-based guidance laws, intends to enable the application of predictive strategies also to the low-level control, responsible for tracking the references provided by the guidance strategy. The stability and robustness of the proposed strategy are theoretically discussed. Furthermore, given the non-negligible computational cost of such nonlinear predictive guidance strategy, a practical nonlinear model predictive control strategy is also applied in order to reduce the computational cost to a great extent. The effectiveness and advantages of both proposed strategies over other nonlinear guidance laws are illustrated through a complete set of simulations.
In this paper, two cooperative guidance laws based on two-point boundary value are designed to deal with the problem of cooperative encirclement and simultaneous attack under condition of both known target acceleration and unknown target acceleration. The only requirement for the multi-attacker communication network is that it contains a directed spanning tree. The guidance laws can function properly as long as at least one attacker can observed the target. The acceleration components along the attacker-target line of sight in the novel guidance laws can reduce the relative remaining distance …
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.
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…
In this article we present a unified framework based on receding horizon techniques that can be used to design the three tasks (guidance, navigation and path-planning) which are involved in the autonomy of unmanned vehicles. This tasks are solved using model predictive control and moving horizon estimation techniques, which allows us to include physical and dynamical constraints at the design stage, thus leading to optimal and feasible results. In order to demonstrate the capabilities of the proposed framework, we have used Gazebo simulator in order to drive a Jackal unmanned ground vehicle (U…
Université de Sherbrooke（谢布鲁克大学）；University of Malaya（马来亚大学）
Recently worldwide interest is growing toward commercial, military or scientific Unmanned Surface Vehicle (USV) and hence there is required to develop their guidance, navigation, and control (GNC) systems. Real USVs are a relatively new advent, so the drawbacks of each model will be modified during the time. The proposition of an environmentally friendly as well as high efficient USV's design are the main purposes of this paper to guide future researches. Power management between renewable sources and storage units is considered. Furthermore, suitable and modern sensors are applied for state e…
The hybrid electric system has good potential for unmanned tracked vehicles due to its excellent power and economy. Due to unmanned tracked vehicles have no traditional driving devices, and the driving cycle is uncertain, it brings new challenges to conventional energy management strategies. This paper proposes a novel energy management strategy for unmanned tracked vehicles based on local speed planning. The contributions are threefold. Firstly, a local speed planning algorithm is adopted for the input of driving cycle prediction to avoid the dependence of traditional vehicles on driver's ope…
Guidance and Control of Unmanned Surface Vehicles via HEOL
ENSTA；UMR 6285；IPP Lab-STICC；ENI Brest；UMR CNRS 6027；IRDL；CRAN (CNRS, UMR 7039)；Université de Lorraine；LIX (CNRS, UMR 7161)；École polytechnique；LJLL (CNRS, UMR 7238)；Sorbonne Unversité；AL.I.E.N.
Beijing Jiaotong Univeristy（北京交通大学）；Beijing Institude of Petrochemical Technology（北京石油化工学院）
In this paper, two novel practical methods of Reinforcement Learning from Demonstration (RLfD) are developed and applied to automatic berthing control systems for Unmanned Surface Vessel. A new expert data generation method, called Model Predictive Based Expert (MPBE) which combines Model Predictive Control and Deep Deterministic Policy Gradient, is developed to provide high quality supervision data for RLfD algorithms. A straightforward RLfD method, model predictive Deep Deterministic Policy Gradient (MP-DDPG), is firstly introduced by replacing the RL agent with MPBE to directly interact wit…
In this paper the problem of guidance formulation for autonomous soft landing of unmanned aerial vehicles on stationary, moving, or accelerating / maneuvering ground vehicles at desired approach angles in both azimuth and elevation is considered. Nonlinear engagement kinematics have been used. While integrated nonlinear controllers have been developed in the literature for this purpose, in practical implementations the controller inputs often need modification of the existing autopilot structure, which is challenging. In order to avoid that a higher-level guidance algorithm is designed in this…
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…
Learning to Guide: Guidance Law Based on Deep Meta-learning and Model Predictive Path Integral Control
School of Automation Science and Electrical Engineering, Beihang University（北京航空航天大学自动化科学与电气工程学院）；Navigation and Control Technology Research Institute of China North Industries Group Corporation（中国北方工业集团有限公司导航与控制技术研究院）
Massachusetts Institute of Technology（麻省理工学院）；University of Leuven (KU Leuven)（鲁汶大学（KU Leuven））；Boston University（波士顿大学）；Iowa State University（爱荷华州立大学）
This paper focuses on the trajectory tracking control problem for an articulated unmanned ground vehicle. We propose and compare two approaches in terms of performance and computational complexity. The first uses a nonlinear mathematical model derived from first principles and combines a nonlinear model predictive controller (NMPC) with a nonlinear moving horizon estimator (NMHE) to produce a control strategy. The second is based on an input-state linearization (ISL) of the original model followed by linear model predictive control (LMPC). A fast real-time iteration scheme is proposed, impleme…
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 …
In this work, we propose a novel missile guidance algorithm that combines deep learning based trajectory prediction with nonlinear model predictive control. Although missile guidance and threat interception is a well-studied problem, existing algorithms' performance degrades significantly when the target is pulling high acceleration attack maneuvers while rapidly changing its direction. We argue that since most threats execute similar attack maneuvers, these nonlinear trajectory patterns can be processed with modern machine learning methods to build high accuracy trajectory prediction algorith…
The main focus of the work presented in this thesis is to develop an optimal control based formation flying control strategy for high precision formation flying of small satellites that have restricted computation and storage capacity. Using the recently developed model predictive static programming (MPSP), and Generalized MPSP algorithm a suboptimal guidance logic is presented for the formation flying of small satellites. The proposed guidance scheme is valid both for high eccentricity chief satellite orbits as well as the large separation distance between the chief and deputy satellites. Com…
Distributed model predictive control (DMPC) is often used to tackle path planning for unmanned aerial vehicle (UAV) swarms. However, it requires considerable computations on-board the UAV, leading to increased weight and power consumption. In this work, we propose to offload path planning computations to multiple ground-based computation units. As simultaneously communicating and recomputing all trajectories is not feasible for a large swarm with tight timing requirements, we develop a novel event-triggered DMPC that selects a subset of most relevant UAV trajectories to be replanned. The resul…
This paper tackles the multi-objective optimization of the cost functional of a path-following model predictive control for vehicle longitudinal and lateral control. While the inherent optimal character of the model predictive control and the direct consideration of constraints gives a very powerful tool for many applications, is the determination of an appropriate cost functional a non-trivial task. This results on the one hand from the number of degrees of freedom or the multitude of adjustable parameters and on the other hand from the coupling of these. To overcome this situation a Bayesian…
In order for automated mobile vehicles to navigate in the real world with minimal collision risks, it is necessary for their planning algorithms to consider uncertainties from measurements and environmental disturbances. In this paper, we consider analytical solutions for a conservative approximation of the mutual probability of collision between two robotic vehicles in the presence of such uncertainties. Therein, we present two methods, which we call unitary scaling and principal axes rotation, for decoupling the bivariate integral required for efficient approximation of the probability of co…
This paper proposes a formation control method for two underactuated unmanned surface vessels (USVs) to follow curved paths in the presence of ocean currents. By uniting a line-of-sight (LOS) guidance law and the null-spacebased behavioral control (NSB) framework, we achieve curved path following of the barycenter, while maintaining the desired vessel formation. The closed-loop dynamics are investigated using cascaded systems theory, and it is shown that the closed-loop system is USGES and UGAS, while the underactuated sway dynamics remains bounded. Both simulation and experimental results are…
Cooperative Integrated Estimation-Guidance for Simultaneous Interception of Moving Targets
Intelligent Systems & Control (ISaC) Lab, Department of Aerospace Engineering, Indian Institute of Technology Bombay（智能系统与控制实验室，航空航天工程系，印度理工学院班加罗尔）；Guidance, Autonomy, Learning, and Control for Intelligent Systems (GALACxIS) Lab, Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati（智能系统引导、自主、学习与控制实验室，航空航天工程与工程力学系，辛辛那提大学）
Geometric Look-Angle Shaping Strategy for Enclosed Inspection
SYSTEC – Research Centre for Systems and Technologies（SYSTEC系统与技术研究中心）；ARISE – Advanced Production and Intelligent Systems（ARISE先进生产与智能系统）；Faculdade de Engenharia, Universidade do Porto（工程学院，葡萄牙波尔图大学）
Data-Driven Predictive Planning and Control for Aerial 3D Inspection with Back-face Elimination
KIOS Research and Innovation Centre of Excellence (KIOS CoE)（KIOS研究与创新中心（KIOS卓越中心））；Department of Electrical and Computer Engineering, University of Cyprus（电子与计算机工程系，塞浦路斯大学）
Path planning in an uncertain environment is a key enabler of true vehicle autonomy. Over the past two decades, numerous approaches have been developed to account for errors in the vehicle path while navigating complex and often uncertain environments. An important capability of such planning is the prediction of vehicle dispersion covariances about a candidate path. This work develops a new closed-loop linear covariance (CL-LinCov) framework applicable to a wide range of autonomous system architectures. Important features of the developed framework include the (1) separation of high-level gui…
