速度测量不确定性影响下的无人车路径跟踪预瞄控制EI北大核心CSCD

预瞄距离是无人车路径跟踪预瞄控制系统设计中的一个关键参数,其选取往往依赖于车辆行驶速度.然而,车速的测量不可避免存在偏差,这必将影响系统的预瞄控制性能.本文针对这一问题,在充分考虑车速测量不确定性对预瞄距离选取影响的基础上,提出一种基于线性参数变化(LPV)系统的鲁棒H∞路径跟踪预瞄控制算法.首先,选取车速和预瞄距离为调度变量,将原系统转化为一种具有可变权重因子的LPV系统.然后,针对无人车系统状态的不完全可测性,设计一种基于观测器的鲁棒H∞控制器.最后,给出保证闭环系统鲁棒H∞性能的凸优化求解条件.仿真结果验证了本文提出控制算法的有效性.     

未知复杂环境中的无人机平滑飞行路径规划

针对无人机实时路径规划问题,提出了一种基于双层决策的平滑路径规划方法,以弥补现有方法在复杂飞行环境中对路径平滑性优化的不足,增强路径的易跟踪性.本文首先给出路径平滑性度量,然后建模上、下层决策目标、威胁规避与无人机性能约束并引入变长规划时间,进而设计基于双层决策的路径规划模型.规划过程中通过嵌入启发式优化策略来进一步改善路径的全局与局部平滑度,并提高路径搜索效率.大量复杂场景中的仿真及与现有经典方法的对比结果表明:该方法能够实时避开复杂危险区域,规划适合飞行的、较短的平滑路径.     

Adaptive neural control for cooperative path following of marine surface vehicles: state and output feedback

This paper addresses the cooperative path-following problem of multiple marine surface vehicles subject to dynamical uncertainties and ocean disturbances induced by unknown wind, wave and ocean current. The control design falls neatly into two parts. One is to steer individual marine surface vehicle to track a predefined path and the other is to synchronise the along-path speed and path variables under the constraints of an underlying communication network. Within these two formulations, a robust adaptive path-following controller is first designed for individual vehicles based on backstepping and neural network techniques. Then, a decentralised synchronisation control law is derived by means of consensus on along-path speed and path variables based on graph theory. The distinct feature of this design lies in that synchronised path following can be reached for any undirected connected communication graphs without accurate knowledge of the model. This result is further extended to the output feedback case, where an observer-based cooperative path-following controller is developed without measuring the velocity of each vehicle. For both designs, rigorous theoretical analysis demonstrate that all signals in the closed-loop system are semi-global uniformly ultimately bounded. Simulation results validate the performance and robustness improvement of the proposed strategy.     

多自主船协同路径跟踪的自适应动态面控制

为实现多自主船含模型不确定与未知风浪流干扰下的协同路径跟踪控制,提出了一种基于神经网络自适应动态面控制的协同路径跟踪算法.该算法采用单隐层(SHL)神经网络逼近模型不确定性以及海洋环境干扰,所引入的动态面设计技术显著降低了控制算法的复杂性.同时将网络通信约束考虑在内,通过设计分散式协同控制律有效地降低了信息通讯量.Lyapunov稳定性分析证明了闭环系统所有的状态和信号是有界的,并且通过选择合适的设计参数可使跟踪误差为任意小.对比仿真结果验证了所提方法的有效性.     

A two‐step control approach for docking of autonomous underwater vehicles

This paper presents a novel integrated guidance and control strategy for docking of autonomous underwater vehicles. The approach to the base, and hence the control design, is divided in two steps: (i) in the first, at higher speed, the vehicle dynamics is assumed to be underactuated, and an appropriate control law is derived to steer the vehicle towards the final docking path, achieving convergence to zero of the appropriate error variables for almost all initial conditions; (ii) in the second stage, at low speed, the vehicle is assumed to be fully actuated, and a robust control law is designed that achieves convergence to zero of the appropriate error variables for all initial conditions, in the presence of parametric model uncertainty. Simulations are presented illustrating the performance of the proposed controllers, including model uncertainty and sensor noise. Copyright © 2014 John Wiley & Sons, Ltd.     

Adaptive predictive path following control based on least squares support vector machines for underactuated autonomous vessels

Since vessel dynamics could vary during maneuvering because of load changes, speed changing, environmental disturbances, aging of mechanism, etc., the performance of model‐based path following control may be degraded if the controller uses the same motion model all the time. This article proposes an adaptive path following control method based on least squares support vector machines (LS‐SVM) to deal with parameter changes of the motion model. The path following controller consists of two components: the online identification of varying parameters and model predictive control (MPC) using the adaptively identified models. For the online parameter identification, an improved online LS‐SVM identification method is proposed based on weighted LS‐SVM. Specifically, the objective function of LS‐SVM is modified to decrease the errors of parameter estimation, an index is proposed to detect the possible model changes, which speeds up the rate of parameter convergence, and the sliding data window strategy is used to realize the online identification. MPC is combined with the line‐of‐sight guidance to track straight line reference paths. Finally, case studies are conducted to verify the effectiveness of the proposed path following adaptive controller. Typical parameter varying scenarios, such as rudder aging, current variations and changes of the maneuverability are considered. Simulation results show that the proposed method can handle the above situations effectively.     

Fault‐tolerant path‐following control for in‐wheel‐motor‐driven autonomous ground vehicles with differential steering

Over the past several decades, the automobile industry has denoted significant research efforts to developing in‐wheel‐motor‐driven autonomous ground vehicles (IWM‐AGVs) with active front‐wheel steering. One of the most fundamental issues for IWM‐AGVs is path following, which is important for automated driving to ensure that the vehicle can track a target‐planned path during local navigation. However, the path‐following task may fail if the system experiences a stuck fault in the active front‐wheel steering. In this paper, a fault‐tolerant control (FTC) strategy is presented for the path following of IWM‐AGVs in the presence of a stuck fault in the active front‐wheel steering. For this purpose, differential steering is used to generate differential torque between the left and right wheels in IWM‐AGVs, and an adaptive triple‐step control approach is applied to realize coordinated lateral and longitudinal path‐following maneuvering. The parameter uncertainties for the cornering stiffness and external disturbances are considered to make the vehicles robust to different driving environments. The effectiveness of the proposed scheme is evaluated with a high‐fidelity and full‐car model based on the veDYNA‐Simulink joint platform.     

Robust fuzzy 3D path following for autonomous underwater vehicle subject to uncertainties

This paper addresses a three-dimensional (3D) path following control problem for underactuated autonomous underwater vehicle (AUV) subject to both internal and external uncertainties. A two-layered framework synthesizing the 3D guidance law and heuristic fuzzy control is proposed to achieve robust adaptive following along a predefined path. In the first layer, a 3D guidance controller for underactuated AUV is presented to guarantee the stability of path following in the kinematics stage. In the second layer, a heuristic adaptive fuzzy algorithm based on the guidance command and feedback linearization Proportional-Integral-Derivative (PID) controller is developed in the dynamics stage to account for the nonlinear dynamics and system uncertainties, including inaccuracy modelling parameters and time-varying environmental disturbances. Furthermore, the sensitivity analysis of the heuristic fuzzy controller is presented. Against most existing methods for 3D path following, the proposed robust fuzzy control scheme reduces the design and implementation costs of complicated dynamics controller, and relaxes the knowledge of the accuracy dynamics modelling and environmental disturbances. Finally, numerical simulation results validate the effectiveness of the proposed control framework and illustrate the outperformance of the proposed controller as well.     

Immersion and invariance-based integrated guidance and control for unmanned aerial vehicle path following

An integrated guidance and control scheme is proposed on path following for the unmanned aerial vehicle. It is capable of handling the coupled non-linearities of the path's kinematics and the aircraft's dynamics independently. In the path coordinate, the guidance law is designed based on a nominal model, and the non-linearity of the path's kinematics is taken into full consideration. In the time coordinate, the flight control law is designed as a feed-forward controller, and it can guarantee the robustness of the guidance law with respect to the actual aircraft's dynamics. Instead of only employing Lyapunov method, the concept of immersion and invariance is also applied to explicitly analyse the stability in both time and path coordinates, and the asymptotic stability of the closed-loop system can be guaranteed. What is more, the regulation-based and immersion-based adaptive technologies are synthetically utilised to handle the unknown parameters. Finally, the numerical simulations demonstrate the effectiveness of the developed integrated guidance and control scheme.     

Observer-based path following control for autonomous vehicles with localization errors and tire slip effects

This paper investigates the problem of path following control for an autonomous vehicle subject to the localization errors and the tire slip effects. First, by analyzing the effects of localization errors, a loss-of-effectiveness actuator model is formulated, and a new chain form model is constructed for path following system of the vehicle. Then, the polytopic model is proposed to characterize the nonlinearity of the system, and an observer-based path following controller is designed by satisfying both the $H_{\infty }$ criterion and $L_1$ criterion. Finally, the path following controller design problem is converted into an optimization problem, which can be solved readily through convex optimization techniques. The effectiveness of the proposed control strategy is verified by simulation results.     

用于清洗绝缘子的四旋翼无人机抗回冲力控制

本文主要针对利用四旋翼无人机清洗绝缘子时受到的回冲力干扰及姿态控制问题,提出了一种用于清洗绝缘子的无人机抗回冲力控制方法.对于无人机系统,本文运用非线性控制方法中的反步法来设计姿态控制器,使其达到输入状态稳定,并对外部扰动具有鲁棒性.本文首先根据无人机运动模型建立了其动力学方程.之后,运用动量定理和流体力学中的伯努利方...     

Distributed hierarchical control for multiple vertical takeoff and landing UAVs with a distance‐based network topology

A distributed formation control algorithm is proposed for multiple vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) in this paper. The neighboring information is obtained by active measurements rather than by nonactive communications as done in the current literatures. By properly expanding the distance‐based network topology structure between each pair of UAVs, a distributed algorithm is proposed such that the switching topology remains connected along the time. Since a VTOL UAV system is typically underactuated, a hierarchical idea is introduced to derive the control design procedure. More specifically, the studied formation control problem of multiple VTOL UAVs is first transformed into the consensus problem of their corresponding error systems. Then, a command force and an applied torque are synthesized for each VTOL UAV such that the error systems reach a consensus. It is demonstrated in terms of Lyapunov theory that the proposed distributed hierarchical control algorithm guarantees the formation realization of multiple VTOL UAVs while maintaining the network connectivity. Finally, simulations are performed to validate the theoretical results.     

基于自适应神经网络的AUVs路径跟踪控制

为解决由于随时间变化水动力阻尼引起的参数变化和不确定性的问题,提出了基于径向基函数神经网络的未知评估算法,引入自适应算法以保证神经网络权值的最优评估.基于Lyapunov稳定性理论,设计一种自适应神经网络控制器以保证路径跟踪系统中所有误差状态都趋于稳定.为了验证该控制器的可行性,对系统施加如位置误差、方向误差等虚拟干扰,证明该控制器可将误差消减为零.另一方面,机器人在以恒定的速度行驶时,每个航点被指定一个适合半径的圆弧可以保证其有较高的精度.为了评估路径跟踪控制器的性能,提出直线型和直线加圆弧型路径方案.仿真结果表明,该控制器可以有效地消除机器人非线性和模型不确定性造成的干扰.     

Model predictive and non-cooperative dynamic game fault recovery control strategies for a network of unmanned underwater vehicles

In this paper, the problem of control and fault recovery for a team of autonomous underwater vehicles in the presence of loss of effectiveness (LOE) actuator faults is addressed. Towards this end, two different fault recovery control strategies based on the model predictive control technique as well as the dynamic game theory are proposed and developed. Given the allowable information that can be exchanged among the agents, both centralised and semi-decentralised recovery control schemes are considered and their associated corresponding fault recovery strategies are developed. The proposed active fault recovery control strategies incorporate both the online inaccurate as well as delayed actuator fault estimates to reconfigure the nominal (healthy state) controllers. The effectiveness of the proposed semi-decentralised fault recovery control schemes is quantitatively investigated through extensive simulation case studies considering various LOE actuator fault severities in one or more unmanned vehicles as well as fault detection and isolation module imperfections such as fault estimation error and time delays in detecting the faults. The simulation results demonstrate and illustrate that our proposed semi-decentralised recovery control scheme can maintain acceptable degraded tracking and formation keeping performance of both the faulty and healthy agents in the team with lower computational and communication bandwidth requirements as well as lower or fairly close control effort cost as compared to the centralised control recovery scheme.     

固定翼无人机自动着陆的一体化制导控制

本文基于制导控制一体化方法的思想,将滑模变结构控制和自抗扰控制技术结合于动态面控制结构中,提出一种固定翼无人机自动着陆方法.在建立六自由度无人机模型、无人机和目标点间的相对视线角度模型的基础上,在动态面控制框架下加入滑模变结构控制来设计制导控制一体化方法.在此过程中加入自抗扰控制技术,提高了系统对未建模部分、参数的不确定性和外界干扰的鲁棒性,并抑制了滑模变结构控制的抖振.该方法使得无人机在平稳地飞向目标点的同时能够满足着陆视线角度的约束.文中详细论述设计思想和设计方法,最后通过仿真验证说明本文方法的有效性.     

A survey on tracking control of unmanned underwater vehicles: Experiments-based approach

This paper aims to provide a review of the conceptual design and theoretical framework of the main control schemes proposed in the literature for unmanned underwater vehicles (UUVs). Additionally, the objective of the paper is not only to present an overview of the recent control architectures validated on UUVs but also to give detailed experimental-based comparative studies of the proposed control schemes. To this end, the main control schemes, including proportional–integral–derivative (PID) based, sliding mode control (SMC) based, adaptive based, observation-based, model predictive control (MPC) based, combined control techniques, are revisited in order to consolidate the principal efforts made in the last two decades by the automatic control community in the field. Besides implementing some key tracking control schemes from the classification mentioned above on Leonard UUV, several real-time experimental scenarios are tested, under different operating conditions, to evaluate and compare the efficiency of the selected tracking control schemes. Furthermore, we point out potential investigation gaps and future research trends at the end of this survey.     

平行机器人与平行无人系统:框架、结构、过程、平台及其应用

本文将基于ACP（Artificial societies,computational experiments,parallel execution）的平行系统思想与机器人领域相结合,形成一种软硬件相结合的框架,为无人机、无人车、无人船在复杂环境中实验、学习与实际工作提供便捷、安全的平台,即平行无人系统.本文从平行机器人的基本概念出发,提出平行无人系统的基本框架,并介绍了各模块的基本功能与实现方法,探讨了其中的关键技术.然后本文围绕无人机、无人车、无人船三个方面展望了无人平行系统在实际中的应用和所面临的挑战,提出了平行无人系统的未来发展方向.     

A single dynamic observer-based module for design of simultaneous fault detection,isolation and tracking control scheme

The problem of simultaneous fault detection, isolation and tracking (SFDIT) control design for linear systems subject to both bounded energy and bounded peak disturbances is considered in this work. A dynamic observer is proposed and implemented by using the H_∞/H_?/L₁ formulation of the SFDIT problem. A single dynamic observer module is designed that generates the residuals as well as the control signals. The objective of the SFDIT module is to ensure that simultaneously the effects of disturbances and control signals on the residual signals are minimised (in order to accomplish the fault detection goal) subject to the constraint that the transfer matrix from the faults to the residuals is equal to a pre-assigned diagonal transfer matrix (in order to accomplish the fault isolation goal), while the effects of disturbances, reference inputs and faults on the specified control outputs are minimised (in order to accomplish the fault-tolerant and tracking control goals). A set of linear matrix inequality (LMI) feasibility conditions are derived to ensure solvability of the problem. In order to illustrate and demonstrate the effectiveness of our proposed design methodology, the developed and proposed schemes are applied to an autonomous unmanned underwater vehicle (AUV).     

Advanced model predictive control framework for autonomous intelligent mechatronic systems: A tutorial overview and perspectives

This paper presents a review on the development and application of model predictive control (MPC) for autonomous intelligent mechatronic systems (AIMS). Starting from the conceptual analysis of “mechatronics”, we analyze the characteristics and control system design requirements of AIMS. In order to fulfill the design requirements, we propose to develop a unified MPC framework for AIMS. The main MPC schemes, covering MPC basics, robust MPC, distributed MPC, Lyapunov-based MPC, event-based MPC, network-based MPC, switched MPC, fast MPC, are reviewed with an attempt to document some of the key achievements in the past decades. Furthermore, we provide the review and analysis of MPC applications to three types of mechatronic systems, including unmanned aerial vehicles (UAVs), autonomous marine vehicles (AMVs), and autonomous ground robots (AGRs). Some promising research directions and concluding remarks are presented.