汽车转向/防抱死制动协同控制

为了解决汽车转向过程中防抱死制动稳定性问题,提出一种新的协同控制系统.该协同控制结构由转向控制器和制动控制器组成.在转向控制中设计滑模鲁棒自适应控制器和横摆力矩控制器力求改善汽车动态响应,鲁棒自适应性和稳定性.此外定义协同误差,建立汽车协同误差模型并设计汽车防抱死制动鲁棒自适应控制系统.为了减少转向系统和制动系统之间的补偿控制律难以确定的困难,提出耦合误差补偿原理与同一给定控制相结合的新的耦合控制策略.最后用仿真结果验证所设计控制算法的有效性.     

Cooperative control of multiple surface vessels in the presence of ocean currents and parametric model uncertainty

This paper addresses the problem of cooperative path‐following of multiple autonomous vehicles. Stated briefly, the problem consists of steering a group of vehicles along specified paths while keeping a desired spatial formation. For a given class of autonomous surface vessels, it is shown how Lyapunov‐based techniques and graph theory can be brought together to design a decentralized control structure, where the vehicle dynamics and the constraints imposed by the topology of the inter‐vehicle communication network are explicitly taken into account. To achieve path‐following for each vehicle, a nonlinear adaptive controller is designed that yields convergence of the trajectories of the closed‐loop system to the path in the presence of constant unknown ocean currents and parametric model uncertainty. The controller derived implicitly compensates for the effect of the ocean current without the need for direct measurements of its velocity. Vehicle cooperation is achieved by adjusting the speed of each vehicle along its path according to information exchanged on the positions of a subset of the other vehicles, as determined by the communication topology adopted. Global stability and convergence of the closed‐loop system are guaranteed. Illustrative examples are presented and discussed. Copyright © 2009 John Wiley & Sons, Ltd.     

Improved adaptive lane‐keeping control for four‐wheel steering vehicles without lateral velocity measurements

Previously, we developed an adaptive lane‐keeping controller for vehicles without using lateral velocity measurements. However, the construction of the controller is very complex and not suitable for practical applications. To overcome this problem, we propose an improved adaptive lane‐keeping controller that is much simpler than the conventional controller. To obtain the improved controller, we propose an improved vehicle dynamic expression. In this dynamic expression, fewer elements are estimated adaptively as containing unknown vehicle parameters compared with the conventional expression. Moreover, a control performance improvement method is described theoretically. Finally, we report numerous simulations that validated the effectiveness of the improved controller. Copyright © 2017 John Wiley & Sons, Ltd.     

Robust wavelet network control for a class of autonomous vehicles to track environmental contour line

We address the problem of environmental contour line tracking for a class of autonomous vehicles. A reference velocity is designed for the autonomous vehicles to do contour line tracking. Based on Lashall invariance principle, an ideal controller is designed for the vehicle with ideal model and ideal information about the environmental concentration function to track the desired contour line. For the vehicle with possibly modeling uncertainty, we combine a neural controller containing a wavelet neural network (WNN) identifier with a robust control to construct a robust adaptive WNN control for the vehicle to track the desired environmental contour line. Then we give theoretical proof of the efficiency of the designed robust adaptive WNN control. Simulation results and conclusion are presented and discussed.     

电动车制动防抱死自适应鲁棒控制系统

为了改善电动车的可操作性和稳定性,本文进行电动车制动防抱死控制系统的研究.首先针对汽车纵向四轮模型,设计自适应鲁棒控制器.所提出的自适应控制器和自适应鲁棒控制律不仅保证了闭环系统的稳定性,而且实现了所期望的性能.然后通过试验结果证明了控制算法的有效性.尽管道路条件的变化不同,电动车制动防抱死控制系统表现出满意的控制性能.     

Control of multivehicle systems in the presence of uncertain dynamics

In this paper, we present a cooperative control architecture for high-order multivehicle systems having non-identical nonlinear uncertain dynamics. The proposed methodology consists of a local cooperative controller and a vehicle-level controller for each vehicle. The former controller receives the relative output measurements of the neighbouring vehicles in order to solve a containment problem formulated on a leader–follower framework. Specifically, the leaders generate trajectories in which the vehicles (followers) converge to the convex hull formed by those of the leaders. For a special case with one leader, this controller synchronises the output of the vehicles with the output of the leader. The latter controller receives the internal-state measurements for suppressing the nonlinear uncertain dynamics of the vehicle by using a decentralised adaptive control approach. The interaction topology between vehicles is described by undirected graphs and extensions to directed graphs are further discussed. The stability and convergence properties of the proposed cooperative control architecture are analysed by using the results from linear algebra and the Lyapunov theory. Several numerical examples are provided to demonstrate the efficacy of the proposed cooperative control architecture.     

Adaptive Fuzzy Sliding Mode Controller for the Kinematic Variables of an Underwater Vehicle

This paper address the kinematic variables control problem for the low-speed manoeuvring of a low cost and underactuated underwater vehicle. Control of underwater vehicles is not simple, mainly due to the non-linear and coupled character of system equations, the lack of a precise model of vehicle dynamics and parameters, as well as the appearance of internal and external perturbations. The proposed methodology is an approach included in the control areas of non-linear feedback linearization, model-based and uncertainties consideration, making use of a pioneering algorithm in underwater vehicles. It is based on the fusion of a sliding mode controller and an adaptive fuzzy system, including the advantages of both systems. The main advantage of this methodology is that it relaxes the required knowledge of vehicle model, reducing the cost of its design. The described controller is part of a modular and simple 2D guidance and control architecture. The controller makes use of a semi-decoupled non-linear plant model of the Snorkel vehicle and it is compounded by three independent controllers, each one for the three controllable DOFs of the vehicle. The experimental results demonstrate the good performance of the proposed controller, within the constraints of the sensorial system and the uncertainty of vehicle theoretical models.     

Data-iteration cooperative adaptive cruise control of mixed heterogeneous vehicle platoons with unknown dynamic characteristics

This paper considers the cooperative adaptive cruise control (CACC) problem of mixed heterogeneous vehicle platoons composed of human-driven and CACC vehicles with unknown dynamic characteristics and an alternative to CACC for platooning of the heterogeneous vehicle platoon is presented. Adaptive dynamic programming is firstly used to learn the dynamic characteristics of acceleration of vehicles from the sampled data. Then the data-iteration optimal CACC controller is computed to ensure that each CACC vehicle can reach a desired inter-vehicle distance and desired common velocity with no prior knowledge of the dynamics of vehicles in the mixed platoon. Moreover, the string stability of the mixed vehicle platoon is derived by establishing some sufficient conditions on the acceleration transfer function of adjacent vehicles. Two simulation experiments of a six-vehicle mixed platoon are used to illustrate the effectiveness of the proposed CACC method.     

基于模糊神经网络水下机器人直接自适应控制

提出了基于广义动态模糊神经网络的水下机器人直接自适应控制方法, 该控制方法既不需要预先知道模糊神经结构, 也不需要预先的训练阶段, 完全通过在线自适应学习算法构建水下机器人的逆动力学模型. 首先, 本文提出了基于这种网络结构的水下机器人直接自适应控制器, 然后, 利用 Lyapunov 稳定理论, 证明了基于该控制器的水下机器人控制系统闭环稳定性, 最后, 采用某水下机器人模型仿真验证了该控制方法的有效性.     

基于自适应MPC算法的轨迹跟踪控制研究

为了保证智能车辆在低附着且变速条件下跟踪控制的精确性和稳定性,提出一种基于自适应模型预测控制（MPC）的轨迹跟踪控制算法。针对低附着条件下轨迹跟踪存在行驶稳定性较差的问题,对车辆动力学模型添加侧偏角软约束,分别设计有无添加侧偏角约束的MPC控制器。仿真结果表明,添加侧偏角约束后MPC控制器性能更优,车辆行驶稳定性得到有效提高。在此基础上,又提出了一种自适应的轨迹跟踪控制策略,能够根据车辆速度的变化,实时产生预测时域[(Hp)],分别设计自适应的MPC控制器与4组定值[Hp]的MPC控制器。仿真结果表明,基于自适应模型预测控制的轨迹跟踪控制算法在提高低附着且变速条件下智能车辆轨迹跟踪控制的精度和稳定性方面具有一定的有效性和先进性。     

基于干扰观测器的非线性不确定系统自适应滑模控制

本文研究了一类基于非线性干扰观测器的多输入多输出非线性不确定系统的边界层自适应滑模控制方法并应用于近空间飞行器高精度姿态控制.考虑系统存在不确定性和外部干扰上界未知的情况,设计了基于干扰观测器的边界层自适应滑模控制器,以消除传统滑模控制中的"抖振"现象,使跟踪误差趋近于零.同时,利用李雅普洛夫方法严格证明了闭环系统的稳定性.最后将所研究的自适应滑模控制方法,应用于某近空间飞行器的姿态控制中,仿真结果表明在不确定性和外部干扰作用下能保证姿态控制的稳定性,对参数不确定具有较好的鲁棒性.     

基于模型预测控制的电动车辆队列控制研究

针对车辆队列建模时参数不确定导致控制存在误差的问题,以及队列中跟随车辆稳定性问题,分析车辆纵向动力学,设计一个鲁棒MPC控制器和滑移率控制器来提高队列车辆的控制精度和稳定性.首先对纵向MPC控制器进行改进,提高车辆队列控制精度；同时为防止跟随车辆的轮胎打滑,设计一个MPC滑移率控制器对跟随车辆的轮胎滑移率进行控制约束,保证了跟随车辆的纵向稳定性.最后,进行仿真实验验证其有效性.仿真实验结果表明,与传统的LQR、MPC控制器相比,改进的鲁棒MPC纵向控制器控制精度更高,同时MPC滑移率控制器可防止跟随车辆的轮胎打滑,保证了跟随车辆的纵向稳定性.     

高速转向工况下的无人驾驶车辆路径跟踪

为提高无人驾驶车辆在高速转向工况下的路径跟踪精度与行驶稳定性,基于三自由度单轨车辆模型与模型预测控制理论,分析前轮转角约束对车辆跟踪精度与行驶稳定性的影响,提出一种自适应于侧向附着力的路径跟踪控制方法.以Pacejka'89魔术公式轮胎模型为基础,分析轮胎纵向受力,以此推算轮胎的侧向附着力,从而建立前轮转角约束随车辆状...     

基于容错学习的智能车辆路径跟踪控制

针对复杂驾驶环境中智能车辆的路径跟踪问题,提出了一种新型自适应容错学习控制策略。为了便于路径跟踪控制器的设计,首先,创新地将车辆动力学转化为一种参数化形式。在此基础上,考虑到车辆动力系统的欠驱动特性,我们控制器设计中引入一种新型处理非方输入矩阵的方法。该方法不仅拓展了自适应学习算法的应用范围,同时也为处理一般非方动力系统提供了一种新思路。其次,针对行驶过程中车辆损耗或意外可能导致的故障,我们提出了一种容错学习机制。此外,利用Lyapunov理论给出了该控制方法的严格收敛性证明,并通过实例验证了该方法的有效性。事实证明,所提控制策略不仅可以有效地处理车辆动力系统固有的非线性性、不确定性以及各种外界干扰,同时还能较好地应对汽车执行器故障带来的影响。     

Adaptive steering control without using measurements of lateral velocity of vehicles

In this paper, we propose an adaptive steering controller without using measurements of lateral velocity of vehicles. At first, we show a new dynamic expression suitable for the design of a stable adaptive steering controller without using the lateral velocity. Next, we develop an ideal vehicle model. In the designed ideal vehicle model, a good steering performance can be achieved even if the drivers’ steering characteristics vary. Finally, an adaptive steering controller is developed, so that the actual vehicles can track ideal vehicle model and realize good steering performance.     

联合通信资源分配的网联车协同自适应巡航 时滞反馈控制

考虑网联车辆队列在路段通信资源受限下的协同自适应巡航控制(CACC)问题,提出一种联合通信资源分配的网联车辆协同自适应巡航时滞反馈控制方法.首先,在头车-前车跟随的通信拓扑结构下,通过网联车辆队列中各车辆间的通信链路数量、该路段可使用的通信资源和当前时刻车辆间的间距误差建立二分图,根据车辆间的间距误差来调度有限的通信资源,将通信资源合理分配给有较大间距误差的跟随车辆;其次,利用非对称PD控制协议和网联车辆队列时滞纵向模型,应用线性矩阵不等式技术计算网联车CACC控制器,进一步得到车辆队列弦稳定性的充分条件;最后,通过Matlab/CarSim联合仿真验证该方法的有效性.     

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.     

基于非线性L1自适应动态逆的飞行器姿态角控制

钊对常规动态逆控制器不能有效抵消系统中的不确定性这一缺点,提出了一种非线性L_1自适应动态逆控制方法.该方法能够克服常规动态逆的不足,在保证系统鲁棒性的前提下,提升飞行器姿态角控制效果.首先,采用时标分离原理,将姿态角控制系统分为内外两个回路:外回路采用常规动态逆控制器,用于姿态角的跟踪控制;内回路采用非线性L_1自适应控制器,用于角速率的控制.其中,L_1自适应控制器由静态反馈控制器和自适应控制器组成:静态反馈控制器通过状态反馈实现,用于保证内回路的稳定和具有期望的闭环特性;自适应控制器由状态观测器、自适应律和控制律组成,用于抵消系统中的不确定性.其次,对所提控制方法的稳定性进行了分析,结果证明了该控制方法能够保证内回路的稳定和外回路的误差有界.最后,在综合考虑多种不确定性的情况下,将本文提出的非线性L_1自适应动态逆控制方法用于某无人飞行器姿态角控制,仿真结果验证了该控制方法的有效性和鲁棒性.     

Neuro-control of unmanned underwater vehicles

Unmanned underwater vehicles (UUVs) typically operate in uncertain and changing environments. Since the dynamics of UUVs are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions, a high-performance control system of a UUV is needed to have the capacities of learning and adaptation to the variations in the UUV's dynamics. This paper presents the utilization of an adaptive neuro-control scheme as a controller for controlling a UUV in six degrees of freedom. No prior offline training phase and no explicit knowledge of the structure of the vehicle are required, and the proposed scheme exploits the advantages of both neural network control and adaptive control. Asymptotic convergence of the UUV's tracking errors and stability of the presented control system is guaranteed on the basis of the Lyapunov theory. In this paper, neural network architectures based on radial basis functions and multilayer structures have been used to evaluate the performance of the adaptive controller via computer simulation.     

Adaptive cruise control of a HEV using sliding mode control

This paper presents adaptive cruise control of a hybrid electric vehicle. First, the mathematical model of the vehicle is formulated. Next, a classical controller is applied to the vehicle model. Swarm optimisation is implemented for self parameter tuning of the controller. The model is simulated and the result of the response to a variable speed is analysed. The results reveal that the controller is not a powerful means to manage the rapid transformation of the desire set point. Accordingly, a sliding mode controller is developed next. The performance of this controller is compared with the classical controller.