Lateral control of autonomous vehicles based on fuzzy logic

Autonomous vehicles have attracted considerable attention in the research community and industry. This paper addresses a problem in designing lateral control law and develops a strategy to determine the given speed of autonomous vehicles. An improved method for calculating the lateral offset and heading angle error is proposed to reduce the impact of reference path data noise. Multiple fuzzy inference engines are used to design the steering controller and determine the given driving speed, including the forward and backward directions. The stability condition is given to guide the design of fuzzy inference engines. Satisfactory simulation and experimental results have been obtained from different reference paths.     

Modeling, simulation and fuzzy control of an anthropomorphic robot arm by using Dymola

Analysis and fuzzy control of an anthropomorphic robot arm on a special trajectory is the subject of this paper. These types of systems are used in cutting operations on materials, joining materials by welding, material handling in remote and dangerous environments, packing of foods, inspection/testing electronic parts or medical products. This robot arm realizes the handling motion on a special trajectory. In this study, the first three links of Mitsubishi RV-2AJ Industrial Robot, are like an anthropomorphic arm, have been modeled and simulated by using Dymola. Kinematic equations have been obtained and mathematical model of this system has been formed by using Lagrange’s Equations. Fuzzy logic controller for the joint angles for the motion trajectory has been designed and the simulation results have been presented at the end of the study.     

A polynomial fuzzy neural network for modeling and control

This paper introduces a novel neurofuzzy system based on polynomial fuzzy neural network (PFNN) architecture. A PFNN consists of a set of if-then rules with appropriate membership functions (MFs) whose parameters are optimized via a hybrid genetic algorithm. A polynomial neural network is employed in the defuzzification scheme to improve output performance and to select appropriate rules. A performance criterion for model selection is defined to overcome the overfitting problem in the modeling procedure. For a performance assessment of the PFNN inference system, two well-known problems are employed for a comparison with other methods. The results of these comparisons show that the PFNN inference system out-performs the other methods and exhibits robustness characteristics. This work was presented in part at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999     

Three-dimensional fuzzy logic system for process modeling and control

The traditional fuzzy logic system (FLS) can only model and control the process in two-dimensional nature. Many of real-world systems are of multidimensional features, such as, thermal and fluid processes with spatiotemporal dynamics, biological systems, or decision-making processes that contain stochastic and imprecise uncertainties. These types of systems are difficult for the traditional FLS to model and control because they require a third dimension for spatial or probabilistic information. The type-2 fuzzy set provides the possibility to develop a three-dimensional fuzzy logic system for modeling and controlling these processes in three-dimensional nature.     

Design of genetic-fuzzy control strategy for parallel hybrid electric vehicles

Hybrid Electric Vehicles (HEVs) generate the power required to drive the vehicle via a combination of internal combustion engines and electric generators. To make HEVs as efficient as possible, proper management of the different energy elements is essential. This task is performed using the HEV control strategy. The HEV control strategy is the algorithm according to which energy is produced, used and saved. This paper describes a genetic-fuzzy control strategy for parallel HEVs. The genetic-fuzzy control strategy is a fuzzy logic controller that is tuned by a genetic algorithm. The objective is to minimize fuel consumption and emissions, while enhancing or maintaining the driving performance characteristics of the vehicle. The tuning process is performed over three different driving cycles including NEDC, FTP and TEH-CAR. Results from the computer simulation demonstrate the effectiveness of this approach in reducing fuel consumption and emissions without sacrificing vehicle performance.     

Fuzzy path tracking control for automatic steering of vehicles

In this paper, the path tracking (PT) control for automatic steering of vehicles is studied. The Takagi–Sugeno (T–S) fuzzy model of vehicle obtained from a nonlinear model is considered and a fuzzy controller is designed. The stability analysis is discussed using Lyapunov’s approach combined with the linear matrix inequalities (LMI) approach. Finally, simulation results are given to demonstrate the controller’s effectiveness.     

Fuzzy observer design using linear matrix inequalities for fuzzy closed-loop control systems

Linear observers play an important role in modern control theory and practice. A systematic design method of fuzzy observers would be important for fuzzy control as well. The fuzzy observer is designed by solving linear matrix inequalities (LMI) that represent control performance such as disturbance rejection and robust stability. Our approach in designing the fuzzy observer is based on the LMI formulation of the stability conditions for closed-loop Takagi-Sugeno (T-S) fuzzy systems, when states are not available for measurement of feedback. We present a new approach, which is to design an observer based on fuzzy implications, with fuzzy sets in the antecedents, and an asymptotic observer in the consequents. Each fuzzy rule is responsible for observing the states of a locally linear subsystem. An example illustrates the design procedure.     

Design of fuzzy PID controllers using modified triangular membership functions

A design method for fuzzy proportional-integral-derivative (PID) controllers is investigated in this study. Based on conventional triangular membership functions used in fuzzy inference systems, the modified triangular membership functions are proposed to improve a system’s performance according to knowledge-based reasonings. The parameters of the considered controllers are tuned by means of genetic algorithms (GAs) using a fitness function associated with the system’s performance indices. The merits of the proposed controllers are illustrated by considering a model of the induction motor control system and a higher-order numerical model.     

Comparison and application of three integral-improved methods on conventional fuzzy control strategy

This paper presents three integral methods of a fuzzy knowledge-based control system, to diminish the steady-state output errors. The three controllers, viz the three-input fuzzy controller, fuzzy plus integral controller and are fuzzy-PI controller, have been described and are applied to the two systems. The first is a zero-type-number first-order system whose input is step, and the other is a linearized one-type-number fifth-order electrohydraulic servo system whose input is ramp. Responses of both systems have steady-state errors without integral control. Simulation results have shown that all these three integral methods can effectively eliminate steady-state errors. The three-input fuzzy controller that produces the least overshoot and shortest rise time is the first choice for producing rapid dynamic response and minimum output errors. Fuzzy-PI controller is the second best for producing small output errors.     

并联混合动力汽车模糊逻辑控制策略的建模和仿真

提出了基于模糊逻辑控制扭矩分配策略,建立了各功能组件模型.并利用ADVIS0R2002仿真平台,完成了该模糊逻辑扭矩控制策略和电气辅助控制策略仿真比较.结果表明,本文提出的模糊逻辑控制策略对提高混合动力汽车的动力性和燃油经济性,改善尾气的排放有明显的作用.     

Simplifying the automatic design of a fuzzy logic controller using evolutionary programming

With the availability of a wide range of Evolutionary Algorithms such as Genetic Algorithms, Evolutionary Programming, Evolutionary Strategies and Differential Evolution, every conceivable aspect of the design of a fuzzy logic controller has been optimized and automated. Although there is no doubt that these automated techniques can produce an optimal fuzzy logic controller, the structure of such a controller is often obscure and in many cases these optimizations are simply not needed. We believe that the automatic design of a fuzzy logic controller can be simplified by using a generic rule base such as the MacVicar-Whelan rule base and using an evolutionary algorithm to optimize only the membership functions of the fuzzy sets. Furthermore, by restricting the overlapping of fuzzy sets, using triangular membership functions and singletons, and reducing the number of parameters to represent the membership functions, the design can be further simplified. This paper describes this method of simplifying the design and some experiments performed to ascertain its validity.     

基于MATLAB（SIMULINK）的模糊控制系统计算机仿真

将ＭＡＴＬＡＢ的ＦＵＺＺＹ ＴＯＯＬＢＯＸ中的模糊推理系统（Ｆｕｚｚｙ Ｉｎｔｅｒｅｎｃｅ Ｓｙｓｔｅｍ）编辑器与ＳＩＭＵＬＩＮＫ有机结合起来,充分利用它们各自的优势,方便地实现了模糊控制系统（ＦＣＳ）的计算机仿真。从而克服了对复杂控制律无法直接应用ＳＩＭＵＬＩＮＫ进行仿真的缺点,了ＳＩＭＵＬＩＮＫ的应用范围。     

Dynamic switching based fuzzy control strategy for a class of distributed parameter system

In this work, a dynamic switching based fuzzy controller combined with spectral method is proposed to control a class of nonlinear distributed parameter systems (DPSs). Spectral method can transform infinite-dimensional DPS into finite ordinary differential equations (ODEs). A dynamic switching based fuzzy controller is constructed to track reference values for the multi-inputs multi-outputs (MIMO) ODEs. Only a traditional fuzzy logic system (FLS) and a rule base are used in the controller, and membership functions (MFs) for different ODEs are adjusted by scaling factors. Analytical models of the dynamic switching based fuzzy controller are deduced to design the scaling factors and analyze stability of the control system. In order to obtain a good control performance, particle swarm optimization (PSO) is adopted to design the scaling factors. Moreover, stability of fuzzy control system is analyzed by using the analytical models, definition of the stability and Lyapunov stability theory. Finally, a nonlinear rod catalytic reaction process is used as an illustrated example for demonstration. The simulation results show that performance of proposed dynamic switching based fuzzy control strategy is better than a multi-variable fuzzy logic controller.     

A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot

Ant Colony Optimization is a population-based meta-heuristic that exploits a form of past performance memory that is inspired by the foraging behavior of real ants. The behavior of the Ant Colony Optimization algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired optimization algorithms. The present paper explores a new fuzzy approach for diversity control in Ant Colony Optimization. The main idea is to avoid or slow down full convergence through the dynamic variation of a particular parameter. The performance of different variants of the Ant Colony Optimization algorithm is analyzed to choose one as the basis to the proposed approach. A convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence is created. Encouraging results on several traveling salesman problem instances and its application to the design of fuzzy controllers, in particular the optimization of membership functions for a unicycle mobile robot trajectory control are presented with the proposed method.     

基于Kriging方法的动态地形反向多级细化研究

针对层次细节模型（LOD）只能实现数据精度低于原始DEM数据的动态地形建模过程,提出了基于Kriging方法的动态地形反向细化方法（Levels Of Backward Detail,LOBD）。该方法提取出需反向细化区域,判断多级细化级别,分析区域的空间属性相关性,根据最优条件拟合变异函数,根据Kriging方法对目标区域进行插值,生成精度高于原始DEM数据精度的区域数据。设计了按独立树结构的插值数据动态存储方式。建模实验表明,新的动态建模方法的细节层次可以比LOD方法至少增加两级,并且保持渲染效率基本不变。     

Adaptive fuzzy control of MIMO nonlinear systems with fuzzy dead zones

In this paper, the problem of adaptive fuzzy tracking control is investigated for a class of multi-input multi-output nonlinear systems with fuzzy dead zones. The virtual control gain functions and uncertain functions considered in the studied system are all unknown. Fuzzy logic systems are employed to approximate the unknown functions. With the combination of adaptive backstepping design technique and dynamic surface control method, the problem caused by differentiating nonlinear functions repeatedly is avoided. Furthermore, only one adaptive parameter needs to be updated online for each subsystem, which reduces the computation burden considerably. The presented controller not only guarantees the desired control performance, but also guarantees the boundedness of all closed-loop signals. Simulation results are shown to demonstrate the effectiveness of the proposed algorithm.     

模糊控制的电动车复合电源能量管理研究

针对由蓄电池与超级电容器组成的电动汽车复合电源,为减缓蓄电池功率变化,提出了模糊逻辑控制能量管理策略,建立了蓄电池-超级电容复合电源系统仿真模型,在ADVISOR中进行仿真,得到了复合电源最佳荷电状态初始值.结果表明:对比于逻辑门限控制,模糊逻辑控制的蓄电池功率变化缓慢,减轻了蓄电池的负担.     

高速公路实时交通流模糊逻辑优化控制系统设计

高速公路交通流特征复杂多变,受到诸多影响因素的共同作用,如天气、道路状况、车辆类型等等。因此,如何获取交通流逻辑控制条件并实现精准调节难度较大。为此,本研究设计新的高速公路实时交通流模糊逻辑优化控制系统。对于系统的硬件部分,设计车流控制模块、无线传输车载终端、实时交通流逻辑调度模块、模糊控制器模块,并确定各个模块之间实时连接关系。对于系统的软件部分,求解交通流逻辑控制条件,联合高速公路实时通行相位条件,调节模糊控制器,并根据高速公路交通路网模型,推导交通流优化控制条件的求解表达式,完成高速公路实时交通流模糊逻辑优化控制系统的设计。实验结果表明,本系统可以解决逻辑协调错误的问题,且能够有效控制高速公路车流量。     

Adaptive fuzzy fault-tolerant attitude control of spacecraft

This paper investigates the attitude control of spacecraft in the presence of unknown mass moment of inertia matrix, external disturbances, actuator failures, and control input constraints. A robust adaptive controller is proposed with the utilization of fuzzy logic and backstepping techniques. The unit quaternion is employed to describe the attitude of spacecraft for global representation without singularities. The system uncertainty is estimated by introducing a fuzzy logic system. The adaptive mechanism has only two parameters to be adapted on-line because the adaptive law of the proposed controller is derived from the norm of the weight matrix. The stability of the closed-loop system is guaranteed by Lyapunov direct approach. Results of numerical simulations state that the proposed controller is successful in achieving high attitude performance in the presence of parametric uncertainties, external disturbances, actuator failures, and control input constraints.     

Nonlinear modeling and adaptive fuzzy control of MCFC stack

To improve availability and performance of fuel cells, the operating temperature of molten carbonate fuel cells (MCFC) stack should be controlled within a specified range. However, the most existing models of MCFC are not ready to be applied in synthesis. In this paper, a radial basis function neural networks identification model of MCFC stack is developed based on the input–output sampled data. A novel adaptive fuzzy control procedure for the temperature of MCFC stack is also developed. The parameters of the fuzzy control system are regulated by back-propagation algorithm, and the rule database of the fuzzy system is also adaptively adjusted by the nearest-neighbor-clustering algorithm. Finally using the neural networks model of MCFC stack, the simulation results of the control algorithm are presented. The results show the effectiveness of the proposed modeling and design procedures for MCFC stack based on neural networks identification and the novel adaptive fuzzy control.