Position control of electro-hydraulic actuator system using fuzzy logic controller optimized by particle swarm optimization

The position control system of an electro-hydraulic actuator system (EHAS) is investigated in this paper. The EHAS is developed by taking into consideration the nonlinearities of the system: the friction and the internal leakage. A variable load that simulates a realistic load in robotic excavator is taken as the trajectory reference. A method of control strategy that is implemented by employing a fuzzy logic controller (FLC) whose parameters are optimized using particle swarm optimization (PSO) is proposed. The scaling factors of the fuzzy inference system are tuned to obtain the optimal values which yield the best system performance. The simulation results show that the FLC is able to track the trajectory reference accurately for a range of values of orifice opening. Beyond that range, the orifice opening may introduce chattering, which the FLC alone is not sufficient to overcome. The PSO optimized FLC can reduce the chattering significantly. This result justifies the implementation of the proposed method in position control of EHAS.     

A precise robust fuzzy control of robots using voltage control strategy

Fuzzy control of robot manipulators with a decentralized structure is facing a serious challenge. The state-space model of a robotic system including the robot manipulator and motors is in non-companion form, multivariable, highly nonlinear, and heavily coupled with a variable input gain matrix. Considering the problem, causes and solutions, we use voltage control strategy and convergence analysis to design a novel precise robust fuzzy control (PRFC) approach for electrically driven robot manipulators. The proposed fuzzy controller is Mamdani type and has a decentralized structure with guaranteed stability. In order to obtain a precise response, we regulate a fuzzy rule which governs the origin of the tracking space. The proposed design is verified by stability analysis. Simulations illustrate the superiority of the PRFC over a proprotional derivative like (PD-like) fuzzy controller applied on a selective compliant assembly robot arm (SCARA) driven by permanent magnet DC motors.     

Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization

In many real-world problems involving pattern recognition, system identification and modeling, control, decision making, and forecasting of time-series, available data are quite often of uncertain nature. An interesting alternative is to employ type-2 fuzzy sets, which augment fuzzy models with expressive power to develop models, which efficiently capture the factor of uncertainty. The three-dimensional membership functions of type-2 fuzzy sets offer additional degrees of freedom that make it possible to directly and more effectively account for model’s uncertainties. Type-2 fuzzy logic systems developed with the aid of evolutionary optimization forms a useful modeling tool subsequently resulting in a collection of efficient “If-Then” rules.The type-2 fuzzy neural networks take advantage of capabilities of fuzzy clustering by generating type-2 fuzzy rule base, resulting in a small number of rules and then optimizing membership functions of type-2 fuzzy sets present in the antecedent and consequent parts of the rules. The clustering itself is realized with the aid of differential evolution.Several examples, including a benchmark problem of identification of nonlinear system, are considered. The reported comparative analysis of experimental results is used to quantify the performance of the developed networks.     

Type-2 fuzzy sliding mode control without reaching phase for nonlinear system

A new sliding mode control (SMC) algorithm for the nth order nonlinear system suffering from parameters uncertainty and subjected to an external perturbation is proposed. The algorithm employs a time-varying switching plane. At the initial time t=t₀, the plane passes through the point determined by the system initial conditions in the error state space. Afterwards, the plane moves to the origin of the state space. Since the nonlinear system is sensible to the perturbations and uncertainties during the reaching phase, the elimination of such phase yields in a considerable amelioration of system robustness. Switching plane is chosen such that: (1) the reaching phase is eliminated, (2) the nonlinear system is insensitive to the external disturbance and the model uncertainty from the initial time (3) the convergence of the tracking error to zero. Furthermore, a Type-2 fuzzy system is used to approximate system dynamics (assumed to be unknown) and to construct the equivalent controller such that: (1) all signals of closed-loop system are uniformly ultimately bounded, (2) the problems related to adaptive fuzzy controllers like singularity and constraints on the control gain are resolved. To ensure the robustness of the overall closed-loop system, analytical demonstration using Lyapunov theorem is considered. Finally, a robot manipulator is considered as a real time system in order to confirm the efficiency of the proposed approach. The experimentation is done for both regulation and tracking control problems.     

区间Type-2 T-S间接自适应模糊控制

本文在Type-1 T-S间接自适应模糊控制器的基础上,利用Type-2模糊系统理论,提出了区间Type-2 T-S间接自适应模糊控制器的设计方法.由于该系统的规则前件是区间Type-2模糊集合,后件为精确数,使构造的控制方法既具备Type-2模糊集处理诸多不确定性的特点,能够减少由于规则不确定对系统的影响,同时又具有T-S模糊模型后件为各输入变量的线性组合的特点,可以提高系统的建模精度,减少系统的规则数等优点.本文利用Lyapunov合成方法,研究了在所有变量一致有界的意义下,闭环系统的全局稳定性,分析了区间Type-2 T-S间接自适应模糊控制系统的收敛性,并给出了系统参数的自适应律.通过倒立摆跟踪模型进行仿真,验证其有效性和优越性.     

基于混沌思想模糊自适应参数策略的粒子群优化算法*

粒子群优化算法是一种进化计算技术。提出一种基于混沌思想的模糊自适应参数策略的粒子群优化算法,它利用模糊策略较强的适应能力及混沌运动遍历性、随机性等特点,对标准粒子群优化算法进行了改进,并证明了算法的收敛性。对几种典型测试函数的测试结果表明,模糊自适应参数策略的引入,有效提高了算法收敛的速度,且混沌思想改善了对多维空间的全局搜索能力,能有效避免早熟现象。     

Coordinated controller tuning of a boiler turbine unit with new binary particle swarm optimization algorithm

Coordinated controller tuning of the boiler turbine unit is a challenging task due to the nonlinear and coupling characteristics of the system. In this paper, a new variant of binary particle swarm optimization (PSO) algorithm, called probability based binary PSO (PBPSO), is presented to tune the parameters of a coordinated controller. The simulation results show that PBPSO can effectively optimize the control parameters and achieves better control performance than those based on standard discrete binary PSO, modified binary PSO, and standard continuous PSO.     

Type-2 fuzzy multi-intersection traffic signal control with differential evolution optimization

This paper proposes a multi-agent type-2 fuzzy logic control (FLC) method optimized by differential evolution (DE) for multi-intersection traffic signal control. Type-2 fuzzy sets can deal with models’ uncertainties efficiently because of its three-dimensional membership functions, but selecting suitable parameters of membership functions and rule base is not easy. DE is adopted to decide the parameters in the type-2 fuzzy system, as it is easy to understand, simple to implement and possesses low space complexity. In order to avoid the computational complexity, the expert rule base and the parameters of membership functions (MF) are optimized by turns. An eleven-intersection traffic network is studied in which each intersection is governed by the proposed controller. A secondary layer controller is set in every intersection to select the proper phase sequence. Furthermore, the communication among the adjacent intersections is implemented using multi-agent system. Simulation experiments are designed to compare communicative type-2 FLC optimized by DE with type-1 FLC, fixed-time signal control, etc. Experimental results indicate that our proposed method can enhance the vehicular throughput rate and reduce delay, queue length and parking rate efficiently.     

无线传感器网络k重覆盖优化策略

为了实现无线传感器网络k重覆盖范围的最大化,提出了一种基于粒子群算法的无线传感器k重覆盖优化策略,提高了k重覆盖率,进而提高节点的利用率,延长无线传感器网络的寿命。同时,在保证网络覆盖精度的前提下,选择最合适的参数,这样既保证了最优的覆盖结果又最大限度地节约了计算量,延长了网络的寿命。通过仿真实验分析了粒子群算法的相关参数对覆盖性能指标的影响。实验结果表明,基于粒子群算法的k重覆盖策略有效地优化了网络的k重覆盖性能。     

Position tracking of a 3-PSP parallel robot using dynamic growing interval type-2 fuzzy neural control

Parallel robots have complicated structures as well as complex dynamic and kinematic equations, rendering model-based control approaches as ineffective due to their high computational cost and low accuracy. Here, we propose a model-free dynamic-growing control architecture for parallel robots that combines the merits of self-organizing systems with those of interval type-2 fuzzy neural systems. The proposed approach is then applied experimentally to position control of a 3-PSP (Prismatic–Spherical–Prismatic) parallel robot. The proposed rule-base construction is different from most conventional self-organizing approaches by omitting the node pruning process while adding nodes more conservatively. This helps preserve valuable historical rules for when they are needed. The use of interval type-2 fuzzy logic structure also better enables coping with uncertainties in parameters, dynamics of the robot model and uncertainties in rule space. Finally, the adaptation structure allows learning and further adapts the rule base to changing environment. Multiple simulation and experimental studies confirm that the proposed approach leads to fewer rules, lower computational cost and higher accuracy when compared with two competing type-1 and type-2 fuzzy neural controllers.     

Robust tracking control for a class of electrically driven flexible-joint robots without velocity measurements

This article addresses the motion tracking control for a class of flexible-joint robotic manipulators actuated by brushed direct current motors. This class of electrically driven flexible-joint robots is perturbed by time-varying parametric uncertainties and external disturbances. A novel observer-based robust dynamic feedback tracking controller without velocity measurements will be developed such that the resulting closed-loop system is locally stable, all the states and signals are bounded and the trajectory tracking errors can be made as small as possible. Only the measurements of link position and armature current are required for feedback and so the number of sensors in the practical implementation of the developed control scheme can be greatly reduced. The observer structure is of reduced order in the sense that the observer is constructed only to estimate the velocity signals and whose dimension is half of the dimension of flexible-joint robots. Especially, for the set-point regulation problem, the developed controller is simplified to a linear time-invariant controller. Consequently, the robust tracking control scheme developed in this study can be extended to handle a broader class of uncertain electrically driven flexible-joint robots and the developed robust control schemes possess the properties of computational simplicity and easy implementation. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control algorithms.     

Type-2 fuzzy logic based urban traffic management

This paper presents a multi-agent system based on type-2 fuzzy decision module for traffic signal control in a complex urban road network. The distributed agent architecture using type-2 fuzzy set based controller was designed for optimizing green time in a traffic signal to reduce the total delay experienced by vehicles. A section of the Central Business District of Singapore simulated using PARAMICS software was used as a test bed for validating the proposed agent architecture for the signal control. The performance of the proposed multi-agent controller was compared with a hybrid neural network based hierarchical multi-agent system (HMS) controller and real-time adaptive traffic controller (GLIDE) currently used in Singapore. The performance metrics used for evaluation were total mean delay experienced by the vehicles to travel from source to destination and the current mean speed of vehicles inside the road network. The proposed multi-agent signal control was found to produce a significant improvement in the traffic conditions of the road network reducing the total travel time experienced by vehicles simulated under dual and multiple peak traffic scenarios.     

Optimal two-degree-of-freedom fuzzy control for locomotion control of a hydraulically actuated hexapod robot

Locomotion control of legged robots is a very challenging task because very accurate foot trajectory tracking control is necessary for stable walking. An electro-hydraulically actuated walking robot has sufficient power to walk on rough terrain and carry a heavier payload. However, electro-hydraulic servo systems suffer from various shortcomings such as a high degree of nonlinearity, uncertainty due to changing hydraulic properties, delay due to oil flow and dead-zone of the proportional electromagnetic control valves. These shortcomings lead to inaccurate analytical system model, therefore, application of classical control techniques result into large tracking error. Fuzzy logic is capable of modeling mathematically complex or ill-defined systems. Therefore, fuzzy logic is becoming popular for synthesis of control systems for complex and nonlinear plants. In this investigation, a two-degree-of-freedom fuzzy controller, consisting of a one-step-ahead fuzzy prefilter in the feed-forward loop and a PI-like fuzzy controller in the feedback loop, has been proposed for foot trajectory tracking control of a hydraulically actuated hexapod robot. The fuzzy prefilter has been designed by a genetic algorithm (GA) based optimization. The prefilter overcomes the flattery delay caused by the hydraulic dead-zone of the electromagnetic proportional control valve and thus helps to achieve better tracking. The feedback fuzzy controller ensures the stability of the overall system in the face of model uncertainty associated with hydraulically actuated robotic mechanisms. Experimental results exhibit that the proposed controller manifests better foot trajectory tracking performance compared to single-degree-of-freedom (SDF) fuzzy controller or optimal classical controller like state feedback LQR controller.     

Interval Type-2 fuzzy voter design for fault tolerant systems

A voting scheme constitutes an essential component of many fault tolerant systems. Two types of voters are commonly used in applications of real-valued systems: the inexact majority and the amalgamating voters. The inexact majority voter effectively isolates erroneous modules and is capable of reporting benign outputs when a significant disagreement is detected. However, an application specific voter threshold must be provided. On the other hand, amalgamating voter, such as the weighted average voter, reduces the influence of faulty modules by averaging the input values together. Unlike the majority voters, amalgamating voters are not capable of producing benign outputs. In the past, a Type-1 (T1) fuzzy voting scheme was introduced, allowing for both smooth amalgamation of voter inputs and effective signalization of benign outputs. The presented paper proposes an extension to the fuzzy voting scheme via incorporating Interval Type-2 (IT2) fuzzy logic. The IT2 fuzzy logic allows for an improved handling of uncertain assumptions about the distributions of noisy and erroneous inputs which are essential for correct design of the fuzzy voting scheme. The proposed voter design features robust performance when the uncertainty assumptions dynamically change over time. The IT2 fuzzy voter architecture was compared against the average voter, inexact majority voter, and the T1 fuzzy voter using a refined experimental harness. The reported results demonstrate improved availability, safety and reliability of the presented IT2 fuzzy voting scheme.     

控制系统的辨识建模及微粒群优化设计

针对控制系统的传递函数建模与控制器的参数优化问题,提出了基于Prony和微粒群优化（PSO）算法的设计方案。首先在被控对象的输入端施加一个脉冲信号,然后对其输出信号进行Prony分析,得出该被控对象的传递函数,最后采用改进PSO算法进行控制器的参数优化设计。基于辨识的Prony算法可快速准确得出被控对象的传递函数;基于T-S模型模糊自适应的改进PSO算法（T-SPSO算法）依据种群当前最优性能指标和惯性权重自适应惯性权重取值,较好解决了PSO算法的早熟问题,可以更好地优化控制器参数。该方案实现了控制系统的精确建模与优化设计,仿真结果验证了所提方案的有效性。     

Chaos particle swarm optimization and T-S fuzzy modeling approaches to constrained predictive control

Predictive control of systems is very much related to the efficiency and cost of systems, as well as to the quality of systems outcomes. However, it is difficult to achieve optimal predictive control because most predictive controls for systems have characteristics of randomness, strong and complex constraints, large delay time, fuzziness, and nonlinearity. Conventional methods of solving constrained nonlinear optimization problems for predictive control are mainly based on quadratic programming, which is quite sensitive to initial values, easy to trap in local minimal points, and requires large computational effort. In recent years, T-S fuzzy modeling has been found to be an effective approach in performing predictive control. Intelligent optimization algorithms, such as chaos optimization algorithm (COA) and particle swarm optimization (PSO), have been shown to have faster convergence and higher iterative accuracy than those based on conventional optimization methods. In this paper, chaos particle swarm optimization (CPSO), which involves combining the strengths of COA and PSO, and T-S fuzzy modeling are proposed as approaches to perform constrained predictive control. Predictive control of temperature of continued hyperthermic celiac perfusion for medical treatment based on the proposed approaches was carried out. Simulation tests were conducted to evaluate the performance of temperature control based on T-S fuzzy modeling and CPSO. Test results indicate that the T-S fuzzy model based on CPSO outperforms models based on generalized predictive control, COA, and PSO.     

Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms

We describe a tracking controller for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on type-2 fuzzy logic theory and genetic algorithms. Computer simulations are presented confirming the performance of the tracking controller and its application to different navigation problems.     

Adaptive type-2 fuzzy sliding mode controller for SISO nonlinear systems subject to actuator faults

In this paper, an adaptive type-2 fuzzy sliding mode control to tolerate actuator faults of unknown nonlinear systems with external disturbances is presented. Based on a redundant actuation structure, a novel type-2 adaptive fuzzy fault tolerant control scheme is proposed using sliding mode control. Two adaptive type-2 fuzzy logic systems are used to approximate the unknown functions, whose adaptation laws are deduced from the stability analysis. The proposed approach allows to ensure good tracking performance despite the presence of actuator failures and external disturbances, as illustrated through a simulation example.     

一种改进的区间二型模糊控制器设计

针对二型模糊控制器设计中出现的降型计算方法损失不确定性信息的问题,提出一种改进的区间二型模糊控制器．该控制器在充分利用二型模糊推理结果的前提下,对区间模糊输出进行再次优化,其优化指标可直接与被控系统性能相关,由此可得到更有利于提高系统整体性能的准确输出量．最后,将改进的控制器用于汽车非线性悬架系统的控制,仿真结果验证了所提出方法的有效性．     

Control of a non-isothermal continuous stirred tank reactor by a feedback-feedforward structure using type-2 fuzzy logic controllers

A control system that uses type-2 fuzzy logic controllers (FLC) is proposed for the control of a non-isothermal continuous stirred tank reactor (CSTR), where a first order irreversible reaction occurs and that is characterized by the presence of bifurcations. Bifurcations due to parameter variations can bring the reactor to instability or create new working conditions which although stable are unacceptable. An extensive analysis of the uncontrolled CSTR dynamics was carried out and used for the choice of the control configuration and the development of controllers. In addition to a feedback controller, the introduction of a feedforward control loop was required to maintain effective control in the presence of disturbances. Simulation results confirmed the effectiveness and the robustness of the type-2 FLC which outperforms its type-1 counterpart particularly when system uncertainties are present.