Optimal Fuzzy Control System Research Articles

A Novel Method for Path Planning of Mobile Robots via Fuzzy Logic and ant Colony Algorithem in Complex Daynamic Environments

Researches on mobile robot path planning with meta-heuristic methods to improve classical approaches have grown dramatically in the recent 35 years. Because routing is one of the NP-hard problems, an ant colony algorithm that is a meta-heuristic method has had no table success in this area. In this paper, a new approach for solving mobile robot navigation in dynamic environments, based on the heuristic feature of an optimized ant colony algorithm is proposed. Decision-making influenced by the distances between the origin and destination points and the angle variance to the nearest obstacles. Ideal paths are selected by the fuzzy logic. The proposed ant colony algorithm will optimize the fuzzy rules’ parameters that have been using to On-line (instant) path planning in dynamic environments. This paper presents a new method that can plan local routs all over the area and to guide the moving robot toward the final track. Using this algorithm, mobile robots can move along the ideal path to the target based on the optimal fuzzy control systems in different environments, especially in dynamic and unknown environments.

Optimal fuzzy control system design for car-following behaviour based on the driver–vehicle unit online delays in a real traffic flow

Promoting safety and comfort in driving and reducing the traffic, the pollution and the energy consumption are the main purposes of using many control systems in different driving processes. Car following is the dominant and effective behaviour in traffic flow, the automatization of which is crucial to achieving the aforementioned goals. In this paper, a novel optimal fuzzy control system is designed so that the follower vehicle maintains a safe distance from the vehicle in front of it in a traffic queue with a reduction in the energy consumption. This controller is superior in that it considers the online delays in the reaction of the driver–vehicle unit in the design. The instantaneous delay is estimated using the stimulus–reaction idea based on a real car-following data set. Tuning the controller is achieved by using the linear quadratic regulator gains in the fuzzy scaling gains. Considering the reaction delay enables the controller to be used in an advanced driver assistance system to obtain simultaneously a higher degree of safety, greater energy saving and more freedom for the driver. Fewer errors and more optimality in the results demonstrated the better performance of the proposed control system in comparison with those of a real driver and other controllers.

Novel Adaptive Charged System Search algorithm for optimal tuning of fuzzy controllers

This paper proposes a novel Adaptive Charged System Search (ACSS) algorithm for the optimal tuning of Takagi–Sugeno proportional–integral fuzzy controllers (T–S PI-FCs). The five stages of this algorithm, namely the engagement, exploration, explanation, elaboration and evaluation, involve the adaptation of the acceleration, velocity, and separation distance parameters to the iteration index, and the substitution of the worst charged particles’ fitness function values and positions with the best performing particle data. The ACSS algorithm solves the optimization problems aiming to minimize the objective functions expressed as the sum of absolute control error plus squared output sensitivity function, resulting in optimal fuzzy control systems with reduced parametric sensitivity. The ACSS-based tuning of T–S PI-FCs is applied to second-order servo systems with an integral component. The ACSS algorithm is validated by an experimental case study dealing with the optimal tuning of a T–S PI-FC for the position control of a nonlinear servo system.

Genetic algorithm-based optimal fuzzy control system for the MT 25 microtron

This paper deals with the design of the control system for an RF cyclic electron accelerator with a cavity resonator, a classical type of microtron. This type of accelerator has until now been controlled manually. The control system is based on a Mamdani-type fuzzy regulator. The fuzzy regulator is set with the aid of an operator description and also a mathematical model of the microtron. The final control system is optimized with the help of genetic algorithms. The normalizing and denormalizing section of the fuzzy controller and also the shape of the fuzzy values of the fuzzy variables are optimized.

Semi-active fuzzy control of a wind-excited tall building using multi-objective genetic algorithm

In this study, a multi-objective optimal fuzzy control system for the response reduction of a wind-excited tall building has been proposed. A semi-active tuned mass damper (STMD) is used for vibration control of a 76-story benchmark building subjected to wind load. An STMD consists of a 100kN magnetorheological (MR) damper and its natural period is tuned to the first-mode natural period of vibration of the example building structure. The damping force of the MR damper is controlled by a fuzzy logic controller. A multi-objective genetic algorithm is used for optimization of the fuzzy logic controller. Both the 75th floor acceleration response of the structure and the stroke of the STMD have been used as the objective functions for this multi-objective optimization problem. Because a multi-objective optimization approach provides a set of Pareto-optimal solutions, an engineer is able to select an appropriate design for the specific performance requirement. For a comparative study, a sky–ground hook control algorithm is employed for control of the STMD. Based on numerical results, it has been shown that the proposed control system can effectively reduce the STMD motion as well as building responses compared to the comparative sky–ground hook control algorithm. In addition, the control performance of the STMD controlled by the optimal fuzzy controller is superior to that of the passive TMD and is comparable to an active TMD, but with a significant reduction in power consumption.

Optimal fuzzy control system using the cross-entropy method. A case study of a drilling process

This paper focuses on the optimal tuning of fuzzy control systems using the cross-entropy precise mathematical framework. The design of an optimal fuzzy controller for cutting force regulation in a network-based application and applied to the drilling process is described. The key issue is to obtain optimal fuzzy controller parameters that yield a fast and accurate response with minimum overshoot by minimising the integral time absolute error (ITAE) performance index. Simulation results show that the cross-entropy method does find the optimal solution (i.e. input scaling factors) very accurately, and it can be programmed and implemented very easily (few setting parameters). The results of a comparative study demonstrate that optimal tuning with the cross-entropy method provides a good transient response (without overshoot) and a better error-based performance index than simulated annealing [17], the Nelder-Mead method [14] and genetic algorithms [33]. The experimental results demonstrate that the proposed optimal fuzzy control provides outstanding transient response without overshoot, a small settling time and a minimum steady-state error. The application of optimal fuzzy control reduces rapid drill wear and catastrophic drill breakage due to the increasing and oscillatory cutting forces that occur as the drill depth increases.

An optimal fuzzy control system in a network environment based on simulated annealing. An application to a drilling process

This paper shows a strategy for the optimal tuning of a fuzzy controller in a networked control system using an offline simulated annealing approach. The optimal tuning of the fuzzy controller using a maximum known delay is based on the integral time absolute error ( ITAE) performance index. The goal is to obtain the optimal tuning parameters for the input scaling factors where the ITAE performance index is minimized. In this study, a step change in the force reference signal is considered a disturbance, and the goal is to assess how well the system follows set-point changes using the ITAE criterion. In order to improve the efficiency of high-performance drilling processes while preserving tool life, the current study focuses on the design and implementation of an optimal fuzzy-control system for drilling force. Simulation results demonstrate good convergence properties of the proposed strategy. Experimental tests of the drilling of two materials (GGG40 and 17-4 PH) corroborate the excellent transient response and the minimum overshoot predicted by the simulation results. Thus, the optimal fuzzy control system reduces the influence of the increase in cutting force that occurs at larger drill depths, eliminating the risk of rapid drill wear and catastrophic drill breakage.

Networked Fuzzy Control System for a High-Performance Drilling Process

The high-performance drilling (HPD) process has a significant impact on production in many industries, such as the automotive, die/mold and aerospace industries. However, cutting conditions for drilling are generally chosen from a machining-data handbook, requiring operator experience and skill. In order to improve drilling efficiency while preserving tool life, the current study focuses on the design and implementation of a simple, optimal fuzzy-control system for drilling force. The main topic of this study is the design and implementation of a networked fuzzy controller. The control system consists of a two-input (force error and change of error), single-output (feed-rate increment) fuzzy controller with nine control rules, the sup-product compositional operator for the compositional rule of inference, and the center of area as the defuzzification method. The control algorithm is connected to the process through a multipoint interface (MPI) bus, a proprietary programming, and communication interface for peer-to-peer networking that resembles the PROFIBUS protocol. The output (i.e., feed-rate) signal is transmitted through the MPI; therefore, network-induced delay is unavoidable. The optimal tuning of the fuzzy controller using a maximum known delay is based on the integral time absolute error (ITAE) criterion. The goal is to obtain the optimal tuning parameters for the input scaling factors while minimizing the ITAE performance index. In this study, a step in the force reference signal is considered a disturbance, and the goal is to assess how well the system follows set-point changes using the ITAE criterion. The optimization is performed using the Nelder–Mead simplex (direct search) method. The main advantage of the approach presented herein is the design of a simple fuzzy controller using a known maximum allowable delay to deal with uncertainties and nonlinearities in the drilling process and delays in the network-based application. The results demonstrate that the proposed control strategy provides an excellent transient response without overshoot and a slightly higher drilling time than the CNC working alone (uncontrolled). A major issue in high performance drilling is the increase in cutting force and torque that occurs as the drill depth increases. Therefore, the fuzzy-control system reduces the influence of these factors, thus eliminating the risk of rapid drill wear and catastrophic drill breakage.

A STABLE SELF‐LEARNING OPTIMAL FUZZY CONTROL SYSTEM

The issue of developing a stable self‐learning optimal fuzzy control system is discussed in this paper. Three chief objectives are accomplished: 1) To develop a self‐learning fuzzy controller based on genetic algorithms. In the proposed methodology, the concept of a fuzzy sliding mode is introduced to specify the system response, to simplify the fuzzy rules and to shorten the chromosome length. The speed of fuzzy inference and genetic evolution of the proposed strategy, consequently, is higher than that of the conventional fuzzy logic control. 2) To guarantee the stability of the learning control system. A hitting controller is designed to achieve this requirement. It works as an auxiliary controller and supports the self‐learning fuzzy controller in the following manner. When the learning controller works well enough to allow the system state to lie inside a pre‐defined boundary layer, the hitting controller is disabled. On the other hand, if the system tends to diverge, the hitting controller is turned on to pull the state back. The system is therefore stable in the sense that the state is bounded by the boundary layer. 3) To explore a fuzzy rule‐base that can minimize a standard quadratic cost function. Based on the fuzzy sliding regime, the problem of minimizing the quadratic cost function can be transformed into that of deriving an optimal sliding surface. Consequently, the proposed learning scheme is directly applied to extract the optimal fuzzy rulebase. That is, the faster the hitting time a controller has and the shorter the distance from the sliding surface the higher fitness it possesses. The superiority of the proposed approach is verified through simulations.

Optimal Fuzzy Control Design Using Simulated Annealing and Application to Feedwater Heater Control

A methodology for designing membership functions for fuzzy controllers has been developed and demonstrated with application to feedwater heater level control. This method, namely simulated annealing, assumes that the rule base is determined by an expert who is knowledgeable about the process to be controlled. Although this method is applicable to any type of fuzzy controller, max-min center-average fuzzy controllers with triangular and trapezoidal membership functions were used due to the ease of implementation of this combination. This method essentially performs a random search for the parameters of the membership functions that yield the minimum squared error between the plant outputs and their setpoints for a given test signal as a disturbance. A major dimensionality reduction is accomplished through the identification of some requirements on membership functions. A significant improvement is made in handling membership function constraints that allows the use of every generated solution in the search process. The proposed methodology was applied to the control of cascade-arranged feedwater heaters that are currently controlled by individual pneumatic proportional-only controllers. An optimal fuzzy control system was developed for controlling the levels in this system for a typical load-following transient. The optimal fuzzy controller was found to improve rise time and settling time and to decrease the overshoot in the desired level.