A distributed fuzzy logic controller for an autonomous vehicle

Autonomous vehicles can be used in a variety of applications such as hazardous environments or intelligent highway systems. Fuzzy logic is an appropriate choice for this application as it can describe human behavior well. This paper proposes two fuzzy logic controllers for the steering and the velocity control of an autonomous vehicle. The two controllers are divided into separate modules to mimic the way humans think while driving. The steering controller is divided into four modules; one module drives the vehicle toward the target while another module avoids collision with obstacles. A third module drives the vehicle through mazes. The fourth module adjusts the final orientation of the target. The velocity controller is divided into three modules; the first module speeds up the vehicle to reach the target and slows it down as it moves toward the target. The second module controls the velocity in the neighborhood of obstacles. A third module controls the velocity of the vehicle as it turns sharp corners. A method for automatic tuning of the first module of the velocity controller is proposed to stabilize the velocity of the vehicle as it approaches the target. Two examples to demonstrate the interaction among the seven control modules are included. Results of the simulation are compared with those in the literature. © 2004 Wiley Periodicals, Inc.     

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     

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.     

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.     

A robust fuzzy logic controller for robot manipulators withuncertainties

Owing to load variation and unmodeled dynamics, a robot manipulator can be classified as a nonlinear dynamic system with structured and unstructured uncertainties. In this paper, the stability and robustness of a class of the fuzzy logic control (FLC) is investigated and a robust FLC is proposed for a robot manipulator with uncertainties. In order to show the performance of the proposed control algorithm, computer simulations are carried out on a simple two-link robot manipulator.     

A high-speed, reconfigurable fuzzy logic controller

The modular architecture and reconfigurable inference engine of this analog fuzzy controller offer more flexibility than existing implementations. Its high inference speed and small size make the controller suitable for embedded system applications. The journal issue contains a concise summary of this article. The complete article is linked to Micro's home page on the World Wide Web (http://www.computer.org/pubs/micro/micro.htm)     

A multiregion fuzzy logic controller for nonlinear process control

Although a fuzzy logic controller is generally nonlinear, a PI-type fuzzy controller that uses only control error and change in control error is not able to detect the process nonlinearity and make a control move accordingly. In this paper, a multiregion fuzzy logic controller is proposed for nonlinear process control. Based on prior knowledge, the process to be controlled is divided into fuzzy regions such as high-gain, low-gain, large-time-constant, and small-time-constant. Then a fuzzy controller is designed based on the regional information. Using an auxiliary process variable to detect the process operating regions, the resulting multiregion fuzzy logic controller can give satisfactory performance in all regions. Rule combination and controller tuning are discussed. Application of the controller to pH control is demonstrated     

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     

Design of a fuzzy logic controller for a jet engine fuel system

The design, implementation and evaluation of two types of fuzzy logic controllers (FLC) are presented. The system under consideration is the control of fuel delivery in a jet engine test bench. Two methods of designing FLCs were experimented with. The first method included the development of a tool that inputs the rules, membership functions, and outputs the appropriate consequences. The second method was based on bivariante curve development and scaling. The evaluations of the proposed controllers were performed with an existing proportional–integral (PI) controller. Both of the design methodologies were proven to be superior in comparison with the conventional controller currently utilized for the control of combustion pressure on jet engines.     

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 system for channel equalization

We present a new method for channel equalization using fuzzy logic. The membership functions are derived from the training data set, and a method to estimate the delay of the communication channel is proposed. The performance of the fuzzy equalizer is compared with that of a transversal filter equalizer. It is shown using simulations that the transversal filter requires a much larger training set to achieve the same error rate. It is also shown, using simulations, that the fuzzy equalizer performs better in the presence of channel nonlinearities     

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     

A predictive fuzzy logic controller with an adaptive loop for the manufacture of resin adhesives

Control of exothermic reaction in manufacturing of resin adhesives has been performed, for long, through human reasoning and expertise. Human involvement has been a source of control errors, which affect the quality of resin product. An earlier paper on this topic has incorporated fuzzy logic control (FLC) in the control loop of the manufacturing process to support and accommodate human expertise and to avoid human errors in maintaining a sustained product quality. A one-step predictor has also been included in the FLC loop to compensate for the inherent time delay in the process system. The current research substantiates the FLC methodology and proposes further modifications in the fuzzy logic controller in order to overcome some shortfalls in the earlier system such as reduced speed of control and varying steady state errors in temperature responses. The main reason for these shortfalls is some unmeasurable parameters of the system that vary slowly and unpredictably. An approach of adaptive control is found appropriate in compensating such parameter variations and is applied as an outer loop to the earlier FLC system. Several experiments performed on this system confirm that the new proposal is effective in overcoming the shortfalls of the earlier FLC system.     

A simplified version of mamdani's fuzzy controller: the natural logic controller

This paper proposes the natural logic controller (NLC) that it comes through a very important simplification of the Mamdani's fuzzy controller (MFC) allowing easy-design for single-input-single-output (SISO) regulation problems. Usually, fuzzy controllers are built with two classical signals of process: The error and its rate of change. They use a moderate number of fuzzy subsets and fuzzy rules. The main features of the NLC approach are that use the minimal fuzzy partition (only two fuzzy subsets per variable) and it use the minimal fuzzy rule base (only two rules). The nonlinear resulting fuzzy controller is the simplest one with an analytically well-defined, input-output mapping and accepting a linear approximation at origin. It allows easy extension to more than two signals of process. Some properties of nonlinear mapping of NLC are analyzed and some results are also presents on testing stability when NLC is used on a linear process. A special attention is addressed to the two inputs NLC case, where stability can be tested using the circle criterion. Finally, two application examples are discussed in details.     

A complementary fuzzy logic system

This article discusses complementary (C) fuzzy logic system that is one of continuous multiary logic systems that satisfies a complementary law differently from usual fuzzy logic systems. This article includes formulation of the C fuzzy logic system, derivation of tautologies, and mentions an example that typically shows a difference in inference computation between the C fuzzy logic system and a usual fuzzy logic system.     

A self-learning fuzzy logic controller using genetic algorithmswith reinforcements

This paper presents a new method for learning a fuzzy logic controller automatically. A reinforcement learning technique is applied to a multilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm through reinforcement learning architecture, called a genetic reinforcement fuzzy logic controller, can also learn fuzzy logic control rules even when only weak information such as a binary target of “success” or “failure” signal is available. In this paper, the adaptive heuristic critic algorithm of Barto et al. (1987) is extended to include a priori control knowledge of human operators. It is shown that the system can solve more concretely a fairly difficult control learning problem. Also demonstrated is the feasibility of the method when applied to a cart-pole balancing problem via digital simulations     

A fuzzy irrigation controller system

The paper presents a solution for an irrigation controller based on the fuzzy-logic methodology. First, it describes the general problem of irrigation. Then, it discusses the physical control model. Following the discussion and the formal presentation of the fuzzy controller, the paper provide examples that will show the simplicity in designing and constructing such a system and other advantages of using fuzzy logic in the feedback control problem.     

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.     

Model-based synthetic fuzzy logic controller for indirect bloodpressure measurement

In this paper, a new measurement system for the noninvasive monitoring of the continuous blood pressure waveform in the radial artery is presented. The proposed system comprises a model-based fuzzy logic controller, an arterial tonometer and a micro syringe device. The flexible diaphragm tonometer registers the continuous blood pressure waveform. To obtain accurate measurement without distortion, the tonometer's mean chamber pressure must be kept equal to the mean arterial pressure (MAP), the so-called optimal coupling condition, such that the arterial vessel has the maximum compliance. Since the MAP cannot be measured directly, to keep the optimal coupling condition becomes a tracking control problem with unknown desired trajectory. To solve this dilemma, a model-based fuzzy logic controller is designed to compensate the change of MAP by applying a counter pressure on the tonometer chamber through the micro syringe device. The proposed controller consists of a model-based predictor and a synthetic fuzzy logic controller (SFLC). The model-based predictor estimates the MAPs changing tendency based on the identified arterial pressure-volume model.