基于经验迁移的赛车学习预测控制研究

为提高车辆控制算法对不同道路的适应能力,在原有学习预测控制算法的基础上,本文提出一种基于经验迁移的赛车学习预测控制策略.基于所建立的赛车曲线坐标系模型,记录小车在历史赛道上的行驶轨迹,将其作为采样安全集.采样安全集蕴含了车辆行驶的经验信息.在新赛道上,通过与采样安全集内曲率相近的轨迹进行特征匹配,找出新赛道的虚拟路径跟踪轨迹.然后,对虚拟路径跟踪轨迹附近的采样点进行坐标变换,将历史轨迹转换为新赛道的虚拟采样轨迹,实现对历史赛道上的行驶经验的迁移.构造了迁移学习预测控制(TLMPC),使小车在新的赛道上能够通过学习预测控制器以更快的速度行驶.本文在4个典型赛道上进行了仿真,结果表明所设计的控制策略控制效果有明显提升.与LMPC相比, 10次迭代结果中单圈耗时至少减少了1.2 s.     

基于经典PID算法的智能车系统研究

如何根据各种传感器输入的信息快速识别出前方道路的情况,是智能车系统控制领域研究的难点。本文用MC9SDG128B作为核心处理器,完成智能车电源、驱动、数据采集处理和测速等模块的设计与实现,在此基础上提出了基于经典PID路况识别的控制算法。通过大量的实践和调试总结出PID算法的各项系数参考表,使我们的智能车系统在参赛时最终平均车速达到1.75m/s,入弯道时最大速度达到2m/s,基本上满足全程高速运行的要求。     

Adaptive Flocking Control with a Minority of Informed Agents

In this paper, we address the flocking problem of multiple dynamic mobile agents with a virtual leader in a dynamic proximity network. To avoid fragmentation, we propose a novel flocking algorithm that consists of both an adaptive controller for followers and a feedback controller for the virtual leader. Based on our algorithm, all agents in the group can form a network, maintain connectivity, and track the virtual leader, even when only a minority of agents have access to the information of the virtual leader. Finally, several convincing simulation results are provided that demonstrate 2‐D flocking of a group of agents using the proposed algorithm.     

矿车立体调度及定位电控系统设计

大型煤矿矿车数量、类型较多,存在矿车出入库空间如何充分利用的问题。文章提出了一种矿车立体调度及定位电控系统的设计方案,给出了基于丝杠传动方式的矿车出入库立体调度及定位规划设计;结合该系统架构,介绍了运动控制器SIMATIC CU320的电控配置、PLC单元与现场检测单元的配置及系统的网络组态。调试结果表明,该系统实现了稳定、准确的矿车调度及定位,提高了车位利用率。     

Designing high-level decision making systems based on fuzzy if–then rules for a point-to-point car racing game

In this paper, fuzzy rule-based systems are applied to a point-to-point car racing game. In the point-to-point car racing game, two car agents compete with each other for taking waypoints. There are three waypoints in the car racing field, each of which is assigned a number that indicates the order to take. The control process of car agents is modeled as a non-holonomic system where there are two input variables (acceleration and steering) for controlling the position, angle and velocity of the car agents. Fuzzy rule-based systems are used to make a high-level decision where the target waypoint to take is determined. Since a fuzzy rule-based system for the high-level decision making is generated in the manner of supervised learning, a set of training patterns should be given for the construction of the fuzzy rule-based systems. In this paper we examine two methods to obtain such a set of training patterns. We also examine two representations of input vectors for the fuzzy rule-based systems. We discuss the effect of obtained training patterns and the input representation on the performance of the fuzzy rule-based systems. After discussing and analyzing the experimental results, we present an adaptive framework of fuzzy rule-based systems. The performance of adaptive fuzzy rule-based systems is then examined based on the results of their non-adaptive version. A series of computational experiments are performed to show the learning ability of the adaptive fuzzy rule-based systems.     

Development of a predictive system for car fuel consumption using an artificial neural network

A predictive system for car fuel consumption using a back-propagation neural network is proposed in this paper. The proposed system is constituted of three parts: information acquisition system, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors which will effect the fuel consumption of a car in a practical drive procedure, however, in the present system the impact factors for fuel consumption are simply decided as make of car, engine style, weight of car, vehicle type and transmission system type which are used as input information for the neural network training and fuel consumption forecasting procedure. In the fuel consumption forecasting, to verify the effect of the proposed predictive system, an artificial neural network with back-propagation neural network has a learning capability for car fuel consumption prediction. The prediction results demonstrated that the proposed system using neural network is effective and the performance is satisfactory in fuel consumption prediction.     

A forecasting system for car fuel consumption using a radial basis function neural network

A predictive system for car fuel consumption using a radial basis function (RBF) neural network is proposed in this paper. The proposed work consists of three parts: information acquisition, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors affecting the fuel consumption of a car in a practical drive procedure, in the present system the relevant factors for fuel consumption are simply decided as make of car, engine style, weight of car, vehicle type and transmission system type which are used as input information for the neural network training and fuel consumption forecasting procedure. In fuel consumption forecasting, to verify the effect of the proposed RBF neural network predictive system, an artificial neural network with a back-propagation (BP) neural network is compared with an RBF neural network for car fuel consumption prediction. The prediction results demonstrated the proposed system using the neural network is effective and the performance is satisfactory in terms of fuel consumption prediction.     

Driving as a human: a track learning based adaptable architecture for a car racing controller

We present the evolution and current state of the Mr. Racer car racing controller that excelled at the corresponding TORCS competitions of the last years. Although several heuristics and black-box optimization methods are employed, the basic idea of the controller architecture has been to take over many of the mechanisms human racing drivers apply. They learn the track geometry, plan ahead, and wherever necessary, adapt their plans to the current circumstances quickly. Mr. Racer consists of several modules that have partly been adapted and optimized separately, where the final tuning is usually done with respect to a certain racing track during the warmup phase of the TORCS competitions. We also undertake an experimental evaluation that investigates how the controller profits from adding some of the modules to a basic configuration and which modules are most important for reaching the best possible performance.     

An evolutionary tuned driving system for virtual car racing games: The AUTOPIA driver

This work presents a driving system designed for virtual racing situations. It is based on a complete modular architecture capable of automatically driving a car along a track with or without opponents. The architecture is composed of intuitive modules, with each one being responsible for a basic aspect of car driving. Moreover, this modularity of the architecture will allow us to replace or add modules in the future as a way to enhance particular features of particular situations. In the present work, some of the modules are implemented by means of hand‐designed driving heuristics, whereas modules responsible for adapting the speed and direction of the vehicle to the track's shape, both critical aspects of driving a vehicle, are optimized by means of a genetic algorithm that evaluates the performance of the controller in four different tracks to obtain the best controller in a large number of situations; the algorithm also penalizes controllers that go out of the track, lose control, or get damaged. The evaluation of the performance is done in two ways. First, in runs with and without adversaries over several tracks. And second, the architecture was submitted as a participant to the 2010 Simulated Car Racing Competition, which in end won laurels. © 2012 Wiley Periodicals, Inc.     

基于单片机的汽车电子油门控制器的设计与实现

通过对油门控制器技术的分析,结合其发展应用现状,设计了针对汽车油门开度调节,以及油门与刹车自动切换的电子油门控制器,并对汽车的智能控制进行了设计研究。通过对汽车发动机建立数学模型,分析了以单片机为核心的硬件设计原理和软件控制算法如何采用达林算法解决其时变滞后的问题。     

放射性污染物粘膜铺设车控制系统设计

放射性污染物粘膜铺设车是核事故应急的一种主要装备。为解决放射性污染物粘膜铺设车多工位实时操控需要,设计了由主控屏、辅控屏和手持终端三种控制终端共同组成上位机的控制系统。该系统以主控屏作为交互中心,通过标准RS232接口与下位机通信;辅控屏和手持终端作为主控屏外设,分别通过CET-5E网线和蓝牙将数据实时传输至主控屏,实现不同工位下的可靠操控。下位机主控制器以ARM7作为主处理器,采用Modbus通信协议与上位机通信,便于系统后续拓展开发。实验表明,该多终端操控系统不受辐射环境干扰,通信稳定,操作便捷,实时性高,符合粘膜铺设车作业工况要求。这种通过主屏地址映射实现多终端控制的方式,有利于降低主控制器仲裁压力、减少数据冗余、提高系统时效,适合在各种特种车辆的控制系统中推广应用。     

A fuzzy-rule-based driving architecture for non-player characters in a car racing game

Videogame-based competitions have been the target of considerable interest among researchers over the past few years since they provide an ideal framework in which to apply soft computing techniques. One of the most popular competitions is the Simulated Car Racing Competition which, thanks to the realism implemented by recent car simulators, provides an excellent test bed for the application of autonomous driving techniques. The present work describes the design and implementation of a car controller able to deal with competitive racing situations. The complete driving architecture consists of six simple modules, each one responsible for a basic aspect of car driving. Three modules use simple functions to control gear shifting, steering movements, and pedal positions. A fourth manages speed control by means of a simple fuzzy system. The other two modules are in charge of (i) adapting the driving behaviour to the presence of other cars, and (ii) implementing a basic ‘inter-lap’ learning mechanism in order to remember key track segments and adapt the speed accordingly in future laps. The controller was evaluated in two ways. First, in runs without adversaries over several track designs, our controller allowed some of the longest distances to be covered in a set time in comparison with data from other previous controllers, and second, as a participant in the 2009 Simulated Car Racing Competition which it ended up winning.     

FSAE赛车车架的强度和刚度分析

为保证吉林大学SAE方程式(Formula SAE,FSAE)赛车能够安全参赛,介绍其车架设计方案,利用MSC Patran建立车架的有限元模型,得出该车架在多种工况下的强度值以及扭转和弯曲刚度值;将分析所得数据与已知数据进行对比,证明该方案满足设计要求,为FSAE赛车的安全参赛提供理论保证.     

基于增强现实技术的AR赛车游戏

AR赛车是一款融入了增强现实技术的3D赛车游戏。在赛车游戏中运用增强现实技术．使得游戏有了全新的控制方式和场景效果。在游戏控制方式上,玩家不再需要用键盘来控制赛车,而是通过在摄像头前操纵有特定标识图案的纸片来控制赛车;在场景效果上,AR赛车不再像传统游戏那样始终处于虚拟场景中,而是采用真实场景与虚拟物体相结合。这些特点将会给玩家带来全新的游戏体验。     

Cooperative car parking using vehicle-to-vehicle communication: An agent-based analysis

In this paper, we introduce a decentralized car parking approach for vast car park areas based on cooperation among vehicles through vehicle-to-vehicle communication, called Cooperative Car Parking (CoPark). In CoPark, the task of finding car parking spaces inside a large car parking area is done via smart agents in vehicles opportunistically cooperating with each other to locate parking spaces as near as possible to the final destination with reduced searching time. We comprehensively investigate a range of car parking circumstances and situations in our simulations to evaluate the proposed CoPark approach. We show that by strategic cooperation between agents in the CoPark approach, greater satisfaction can be achieved in terms of individual and social benefits, in the form of reduced search times for car park spaces and reduced walking distances from where cars are parked to a destination building.     

Neural-Network-Based State Feedback Control of a Nonlinear Discrete-Time System in Nonstrict Feedback Form

In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired tracking performance for the control of an unknown, second-order, nonlinear discrete-time system expressed in nonstrict feedback form. In the first approach, two feedforward NNs are employed in the controller with tracking error as the feedback variable whereas in the adaptive critic NN architecture, three feedforward NNs are used. In the adaptive critic architecture, two action NNs produce virtual and actual control inputs, respectively, whereas the third critic NN approximates certain strategic utility function and its output is employed for tuning action NN weights in order to attain the near-optimal control action. Both the NN control methods present a well-defined controller design and the noncausal problem in discrete-time backstepping design is avoided via NN approximation. A comparison between the controller methodologies is highlighted. The stability analysis of the closed-loop control schemes is demonstrated. The NN controller schemes do not require an offline learning phase and the NN weights can be initialized at zero or random. Results show that the performance of the proposed controller schemes is highly satisfactory while meeting the closed-loop stability.      

Evolutionary testing of autonomous software agents

A system built in terms of autonomous software agents may require even greater correctness assurance than one that is merely reacting to the immediate control of its users. Agents make substantial decisions for themselves, so thorough testing is an important consideration. However, autonomy also makes testing harder; by their nature, autonomous agents may react in different ways to the same inputs over time, because, for instance they have changeable goals and knowledge. For this reason, we argue that testing of autonomous agents requires a procedure that caters for a wide range of test case contexts, and that can search for the most demanding of these test cases, even when they are not apparent to the agents’ developers. In this paper, we address this problem, introducing and evaluating an approach to testing autonomous agents that uses evolutionary optimisation to generate demanding test cases. We propose a methodology to derive objective (fitness) functions that drive evolutionary algorithms, and evaluate the overall approach with two simulated autonomous agents. The obtained results show that our approach is effective in finding good test cases automatically.     

Fault-tolerant control of wind turbines with hydrostatic transmission using Takagi–Sugeno and sliding mode techniques

In this paper, a Takagi–Sugeno Sliding Mode Observer for actuator fault diagnosis and fault-tolerant control scheme of wind turbines with hydrostatic transmission are presented. It will be shown that sliding mode techniques have the advantages that several actuator faults of the wind turbine drive train can be simultaneously reconstructed with one and the same observer and directly applied for fault compensation. Furthermore, a simple compensation approach is implemented by subtracting the reconstructed faults obtained from the (faulty) inputs. These corrected inputs act on the system as virtual actuators, such that the originally designed controller for the nominal, i.e. fault-free situation, can still be used. The fault reconstruction and fault tolerant control strategy are tested in simulations with several faults of different types.     

Autonomous car driving by a humanoid robot

Enabling a humanoid robot to drive a car requires the development of a set of basic primitive actions. These include walking to the vehicle, manually controlling its commands (e.g., ignition, gas pedal, and steering) and moving with the whole body to ingress/egress the car. We present a sensor‐based reactive framework for realizing the central part of the complete task, consisting of driving the car along unknown roads. The proposed framework provides three driving strategies by which a human supervisor can teleoperate the car or give the robot full or partial control of the car. A visual servoing scheme uses features of the road image to provide the reference angle for the steering wheel to drive the car at the center of the road. Simultaneously, a Kalman filter merges optical flow and accelerometer measurements to estimate the car linear velocity and correspondingly compute the gas pedal command for driving at a desired speed. The steering wheel and gas pedal reference are sent to the robot control to achieve the driving task with the humanoid. We present results from a driving experience with a real car and the humanoid robot HRP‐2Kai. Part of the framework has been used to perform the driving task at the DARPA Robotics Challenge.     

Finite-time coordination in multiagent systems using sliding mode control approach

Finite-time stability in dynamical systems theory involves systems whose trajectories converge to an equilibrium state in finite time. In this paper, we use the notion of finite-time stability to apply it to the problem of coordinated motion in multiagent systems. Specifically, we consider a group of agents described by fully actuated Euler–Lagrange dynamics along with a leader agent with an objective to reach and maintain a desired formation characterized by steady-state distances between the neighboring agents in finite time. We use graph theoretic notions to characterize communication topology in the network determined by the information flow directions and captured by the graph Laplacian matrix. Furthermore, using sliding mode control approach, we design decentralized control inputs for individual agents that use only data from the neighboring agents which directly communicate their state information to the current agent in order to drive the current agent to the desired steady state. Sliding mode control is known to drive the system states to the sliding surface in finite time. The key feature of our approach is in the design of non-smooth sliding surfaces such that, while on the sliding surface, the error states converge to the origin in finite time, thus ensuring finite-time coordination among the agents in the network. In addition, we discuss the case of switching communication topologies in multiagent systems. Finally, we show the efficacy of our theoretical results using an example of a multiagent system involving planar double integrator agents.