Design and analysis of a fuzzy logic controller

The fuzzy logic controller (FLC) presented by Siler and Ying (1989) is discussed here and is proved to be equivalent to a non-fuzzy, nonlinear, proportional-integral (PI) controller. Some characteristic properties of this fuzzy logic controller are then investigated. The achievable performance of such a specific fuzzy controller is examined and found to be not necessarily better than that of the conventional, linear, non-fuzzy PI controller. Various extended designs of the basic FLC, including the FLC with dual control laws and the three-piece FLC, are then presented to enhance control performance. These extensions can provide servo-control performance. These extensions can provide servo-control performance superior to that of the basic FLC design, as illustrated by simulation results. Finally a highly nonlinear neutralization process is advanced to demonstrate the applicability of the various FLCs to industrial process control.     

Design of a PID-like compound fuzzy logic controller

The paper describes a novel method for the design of a fuzzy logic controller (FLC) with near-optimal performance for a variety of operating conditions. The approach is based on the analysis of the system behaviour in the error state-space. The final control structure, in a form of a compound FLC, is arrived at in two stages. The first stage encompasses design and tuning of a PID-like fuzzy controller. The second stage consists of placing an additional fuzzy controller, of a structure similar to that of the first one, in parallel with the PID-like fuzzy controller designed in the first stage. The resulting compound controller is characterised with high performance in the wide range of operating conditions, and with small number of parameters that can be adjusted using simple optimisation methods. The controller is developed and tested for a plant comprising a vector controlled induction motor drive.     

Real-time multi-rate HIL simulation platform for evaluation of a jet engine fuel controller

A new Hardware-In-the-Loop (HIL) platform is developed for testing of a turbojet engine fuel control system using a multi-rate simulation platform. The HIL equipment consists of an industrial PC and a commercial I/O board for jet engine simulation as the controlled process and an Electronic Control Unit (ECU) as the fuel controller. The controlled process consisting of actuator, physical process and sensors is fully simulated in HIL simulation. However, the high resolution signals of some components in the HIL simulation cause the real-time simulation to become difficult due to the need of small time-steps. As a result, the disparity between the jet engine model sampling rate and these high resolution signals requires a multi-rate simulation. In this study, a multi step size simulation is developed using multiple processors. These processors are designed to synchronize the status of the engine model with the control system as well as to convert the raw data of the I/O boards to actual input and output signals in real-time. These features make the HIL equipment more effective and flexible. The HIL environment is proved to be an efficient tool to develop various control functions and to validate the software and hardware of the engine fuel control system.     

A fuzzy logic controller for an ABS braking system

Anti-blocking system (ABS) brake controllers pose unique challenges to the designer: a) For optimal performance, the controller must operate at an unstable equilibrium point, b) Depending on road conditions, the maximum braking torque may vary over a wide range, c) The tire slippage measurement signal, crucial for controller performance, is both highly uncertain and noisy, d) On rough roads, the tire slip ratio varies widely and rapidly due to tire bouncing, and e) The braking system contains transportation delays which limit the control system bandwidth. A digital controller design was chosen which combines a fuzzy logic element and a decision logic network. The controller identifies the current road condition and generates a command braking pressure signal, based on current and past readings of the slip ratio and brake pressure. The controller detects wheel blockage immediately and avoids excessive slipping. The ABS system performance is examined on a quarter vehicle model with nonlinear elastic suspension. The parallelity of the fuzzy logic evaluation process ensures rapid computation of the controller output signal, requiring less time and fewer computation steps than controllers with adaptive identification. The robustness of the braking system is investigated on rough roads and in the presence of large measurement noise. This paper describes design criteria, and the decision and rule structure of the control system. The simulation results present the system's performance on various road types and under rapidly changing road conditions     

Design of a hybrid fuzzy logic proportional plus conventionalintegral-derivative controller

Presents approaches to the design of a hybrid fuzzy logic proportional plus conventional integral-derivative (fuzzy P+ID) controller in an incremental form. This controller is constructed by using an incremental fuzzy logic controller in place of the proportional term in a conventional PID controller, By using the bounded-input/bounded-output “small gain theorem”, the sufficient condition for stability of this controller is derived. Based on the condition, we modify the Ziegler and Nichols' approach to design the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. When a plant can be described by any modeling method, the fuzzy P+ID controller can be determined by an optimization technique. Finally, this controller is used to control a nonlinear system. Numerical simulation results demonstrate the effectiveness of the fuzzy P+ID controller in comparison with the conventional PID controller, especially when the controlled object operates under uncertainty or in the presence of a disturbance     

Design and implementation of a hybrid fuzzy logic controller for a quadrotor VTOL vehicle

Helicopters have generated considerable interest in both the control community due to their complex dynamics, and in military community because of their advantages over regular aerial vehicles. In this paper, we present the modeling and control of a four rotor vertical take-off and landing (VTOL) unmanned air vehicle known as quadrotor aircraft. This model has been generated using Newton-Euler equations. In order to control the helicopter, classical PD (proportional derivative) and Hybrid Fuzzy PD controllers have been designed. Although fuzzy control of various dynamical systems has been presented in literature, application of this technology to quadrotor helicopter control is quite new. A quadrotor helicopter has nonlinear characteristics where classical control methods are not adequate especially when there are time delays, disturbances and nonlinear vehicle dynamics. On the other hand, Fuzzy control is nonlinear and it is thus suitable for nonlinear system control. Matlab Simulink has been used to test, analyze and compare the performance of the controllers in simulations. For the evaluation of the autonomous flight controllers, some experiments were also performed. For this purpose, an experimental test stand has been designed and manufactured. This study showed that although, both of the classical PD and the Fuzzy PD controllers can control the system properly, the Fuzzy PD controllers performed slightly better than the classical PD controllers, and have benefits such as better disturbance rejection, ease of building the controllers.     

仿手工雕刻的非线性模糊控制器的设计

本文介绍了一种自学习非线性模糊控制器的设计，以及它在雕刻加工中的具体应用。     

Design and implementation of the tree-based fuzzy logic controller

In this paper, a tree-based approach is proposed to design the fuzzy logic controller. Based on the proposed methodology, the fuzzy logic controller has the following merits: the fuzzy control rule can be extracted automatically from the input-output data of the system and the extraction process can be done in one-pass; owing to the fuzzy tree inference structure, the search spaces of the fuzzy inference process are largely reduced; the operation of the inference process can be simplified as a one-dimensional matrix operation because of the fuzzy tree approach; and the controller has regular and modular properties, so it is easy to be implemented by hardware. Furthermore, the proposed fuzzy tree approach has been applied to design the color reproduction system for verifying the proposed methodology. The color reproduction system is mainly used to obtain a color image through the printer that is identical to the original one. In addition to the software simulation, an FPGA is used to implement the prototype hardware system for real-time application. Experimental results show that the effect of color correction is quite good and that the prototype hardware system can operate correctly under the condition of 30 MHz clock rate.     

Design and application of an analog fuzzy logic controller

In this paper, we present an analog fuzzy logic hardware implementation and its application to an autonomous mobile system. With a simple structure the fabricated fuzzy controller shows good performance in processing speed and area consumption. Accomplished with 13 reconfigurable rules, a speed of up to 6 MFLIPS has been achieved. To stress the advantages of the new architecture, speed and flexibility, the same control strategy is implemented on the new analog fuzzy controller and on a digital multipurpose microcontroller in software. The results of the two implementations show that the analog approach is not only faster but also flexible enough to compete with digital fuzzy approaches     

Optimal PID-type fuzzy logic controller for a multi-input multi-output active magnetic bearing system

The performance of the fuzzy controllers depends highly on the proper selection of some design parameters which is usually tuned iteratively via a trial and error process based primarily on engineering intuition. With the recent developments in the area of global optimization, it has been made possible to obtain the optimal values of the design parameters systematically. Nevertheless, it is well known that unless a priori knowledge is available about the optimization search-domain, most of the available time-domain objective functions may result in undesirable solutions. It is consequently important to provide guidelines on how these parameters affect the closed-loop behavior. As a result, some alternative objective functions are presented for the time-domain optimization of the fuzzy controllers, and the design parameters of a PID-type fuzzy controller are tuned by using the proposed time-domain objective functions. Finally, the real-time application of the optimal PID-type fuzzy controller is investigated on the robust stabilization of a laboratory active magnetic bearing system. The experimental results show that the designed PID-type fuzzy controllers provide much superior performances than the linear on-board controllers while retaining lower profiles of control signals.     

A PWM fuzzy logic controller

Researchers usually implement fuzzy inference systems in software on digital computers or microprocessors. This approach copes with most problems, however real-time systems often require very short time responses. In this case, a hardware implementation becomes the only solution. This 1.5-μm CMOS implementation uses a current mode circuit to generate input membership functions and processes inferences using pulse width modulation     

Architecture of a testable analog fuzzy logic controller

The authors discuss problems of testability of analog fuzzy logic controllers implemented as VLSI chips. Enhancements to standard architecture of fuzzy logic controllers which facilitate testing are proposed. To improve controllability and observability of internal nodes, analog switching blocks are introduced together with some additional circuitry. These blocks allow one to test each basic cell of a fuzzy logic controller (e.g., membership function cell, MINIMAX cell, etc.) separately. The analog switching blocks do not contribute to the power consumption in a working chip end therefore can be used in low-power analog fuzzy logic controllers     

Design of an optimal fuzzy logic controller using response surfacemethodology

When the fuzzy logic controller (FLC)-designed based on the plant model-is applied to the real control system, satisfactory control performance may not be attained due to modeling errors from the plant model. Also, a controller that is designed under specific circumstances may not show satisfactory control performance when applied to other circumstances. In such cases, the control parameters of the controller must be adjusted to enhance control performance. Until now, the trial and error method has been used, consuming much time and effort. Also, the set of adjusted values is not guaranteed to be optimal. To resolve such problems, response surface methodology (RSM), a method of adjusting the control parameters of the controller, is suggested. This method is more systematic than the previous trial and error method, thus, optimal solutions can be provided with less tuning. First, the initial values of the control parameters are determined through the plant model and the optimization algorithm. Then designed experiments are performed in the region around the initial values, determining the optimal values of the control parameters that satisfy both the rise time and overshoot simultaneously     

Design of a force reflection controller for telerobot systems using neural network and fuzzy logic

This paper presents a new method for selecting the force-reflection gain in a position-force type bilateral teleoperation system. The force-reflection gain greatly affects the task performance of a teleoperation system; too small gain results in poor task performance while too large gain results in system instability. The maximum boundary of the gain guaranteeing the stability greatly depends upon characteristics of the elements in the system such as: a master arm which is combined with the human operator's hand and the environments with which the slave arm contacts. In normal practice, it is, therefore, very difficult to determine such maximum boundary of the gain. To overcome this difficulty, this paper proposes a force-reflection gain selecting algorithm based on artificial neural network and fuzzy logic. The method estimates characteristics of the master arm and the environments by using neural networks and, then, determines the force-reflection gain from the estimated characteristics by using fuzzy logic. In order to show the effectiveness of the proposed algorithm, a series of experiments are conducted under various conditions of teleoperation using a laboratory-made telerobot system.     

Vague controller: A generalization of fuzzy logic controller

In this research, a vague controller (VC) is synthesized by using the notion of vague sets, which are a generalization of fuzzy sets and characterized by a truth-membership function and a falsity-membership function. The vague sets follow the basic set operations and logic operations defined for fuzzy sets, and are superior to fuzzy sets in that they could deal with the uncertainty encountered in real-world applications in a more natural way. Depending on the vague sets, the VC is developed as a generalization of fuzzy logic controller (FLC). The design procedures of the VC, which allow an arbitrary number of input variables, and each variable could have a distinct number of linguistic values, are outlined in this paper. In order to compensate the effort in constructing two series of membership functions for vague sets and to ease the difficulties in designing VCs, a new means of designating membership functions for VCs is also presented in this article. This method constructs a set of membership functions systematically by using only two parameters: number of linguistic values of a linguistic variable and shrinking factor. The membership functions generated by this method, shrinking-span membership functions (SSMFs), have different spans over the universe of discourse and, therefore, are more rational and more practical from the human expert's point of view.     

Extended fuzzy logic controller for high speed train

In this paper, two dynamic models of high-speed train are presented, namely a single-mass (SM) model and an unit-displacement multi-particle (UDMP) model. Based on the former, a direct fuzzy logic controller is designed, and on the latter, a new fuzzy controller incorporating the implication logic is designed. Three sets of relevant numerical simulation are provided to demonstrate the effectiveness of the proposed control schemes through comparison.     

A fuzzy logic based multi-agents controller

This paper presents a fuzzy logic based controller (Multi-Agents System Controller (MASC)) which regulates the number of agents released to the network on a Multi-Agents Systems (MASs). A fuzzy logic (FL) model for the controller is as presented. The controller is a two-inputs-one-output system. The controllability is based on the network size (NTZ) and the available bandwidth (ABD) which are the inputs to the controller, the controller’s output is number of agents (ANG). The model was simulated using SIMULINK software. The simulation result is presented and it shows that ABD is the major constraint for the number of agents released to the network.     

RTOS在涡喷发动机控制系统中的应用

本文介绍了MicroStar RTOS在微型涡轮喷气发动机控制系统中的应用.微型涡轮喷气发动机控制系统是一个典型的处理事务较为复杂、对实时性和可靠性有较严格要求的嵌入式系统,采用RTOS可大大提高开发效率.     

RTOS在涡喷发动机控制系统中的应用

本文介绍了MicroStar RTOS在微型涡轮喷气发动机控制系统中的应用。微型涡轮喷气发动机控制系统是一个典型的处理事务较为复杂、对实时性和可靠性有较严格要求的嵌入式系统,采用RTOS可大大提高开发效率。