Design Of Logic Controller Research Articles

Specification, design, and implementation of logic controllers based on colored Petri net models and the standard IEC 1131. II. Design and implementation

This work aims at designing and implementing logic control structures that allow a combination of the advantages of classical techniques for the development of programmable logic controller code and elimination of disadvantages such as the lack of formal validation. This paper introduces a method to automatically generate code for a programmable logic controller from a validated textual description of this kind of Petri nets. A special compiler that generates code according to the standard IEC 1131 is discussed. The modeling and implementation of a logic control system for flexible assembly cell using the method describe serves as an example.

Specification, design, and implementation of logic controllers based on colored Petri net models and the standard IEC 1131. I. Specification and design

This work aims at designing and implementing logic control structures that allow a combination of the advantages of classical techniques for the development of programmable logic controller code and elimination of disadvantages such as the lack of formal validation. This paper introduces ordered color Petri nets in combination with some extensions as a modeling tool for logic controllers. The validation of both the model and the specifications of the logic control structures is then pursued through a structural analysis of the net. A special compiler that generates code according to the standard IEC 1131 is discussed. The modeling and implementation of a logic control system of a sector of a flexible assembly cell serves as an example.

A model reference & sensitivity model-based self-learning fuzzy logic controller as a solution for control of nonlinear servo systems

In this paper, the design, simulation and experimental verification of a self-learning fuzzy logic controller (SLFLC) suitable for the control of nonlinear servo systems are described. The SLFLC contains a learning algorithm that utilizes a second-order reference model and a sensitivity model related to the fuzzy controller parameters. The effectiveness of the proposed controller has been tested in the position control loops of two chopper-fed DC servo systems, first by simulation in the presence of a backlash nonlinearity, then by experiment in the presence of a gravity-dependent shaft load and fairly high static friction. The simulation and experimental results have proved that the SLFLC provides desired closed loop behavior and eliminates a steady-state position error.

A study on the discovery of relevant fuzzy rules using pseudobacterial genetic algorithm

This paper presents a new method for the discovery of relevant fuzzy rules using the pseudobacterial genetic algorithm (PBGA). The PBGA was proposed by the authors as a new approach combining a genetic algorithm with a local improvement mechanism inspired by a process in bacterial genetics, named bacterial operation. The presented system aims at the improvement of the quality of the generated fuzzy rules, producing blocks of effective rules and more compact rule bases. This is achieved by encoding the fuzzy rules in the chromosomes in a suitable form in order to make the bacterial operation more effective and by using a crossover operation that adaptively decides the cutting points according to the distribution of degrees of truth values of the rules. In this paper, first, results obtained when using the PBGA for a simple fuzzy modeling problem are presented and compared with other methods. Second, the PBGA is used in the design of a fuzzy logic controller for a semi-active suspension system. The results show the benefits obtained with this approach in both of the studied cases.

Differential Evolution Based Fuzzy Logic Controller for Nonlinear Process Control

This paper presents an unconventional approach to adaptive fuzzy logic controller (FLC) design wherein a new evolution strategy, Differential Evolution (DE) is used in the simultaneous design of membership functions and rule sets for fuzzy logic controllers. Differential Evolution is an exceptionally simple, fast, and robust population based search algorithm that is able to locate near-optimal solutions to difficult problems. This technique, which is similar to genetic algorithms, has been applied to the control of pH, which is a requirement in many chemical industries. Control of pH poses a difficult problem because of inherent nonlinearities and frequently changing process dynamics. This technique has been successfully implemented on a laboratory scale pH plant setup. The results have been compared with a simple GA based adaptive FLC where we have incorporated a search space smoothing function for achieving faster convergence and for ascertaining a global optimum. Results indicate that FLC’s augmented with DE’s offer a powerful alternative to GA based FLC’s. Results also show that the search space smoothing function helps in faster convergence of a GA.

Ontologically Controlled Autonomous Systems: Principles, Operations and Architecture

From the Publisher: Ontologically Controlled Autonomous Systems: Principles, Operations and Architecture, presents the main principles, operations and architecture involved in the design of a novel type of supervisory controller called an ontological controller. An ontological controller can be used to supervise any type of controller, however its intended applications are industrial-strength complex autonomous control systems using advanced programmable controllers. Ontologically Controlled Autonomous Systems: Principles, Operations and Architecture, presents for the first time a complete formal framework and results for ontological control. All results presented in the book originate from the practical industrial experience of the author. The intended readers for Ontologically Controlled Autonomous Systems: Principles, Operations and Architecture, are professional and students working in industrial control, discrete control, discrete-event systems, artificial intelligence, autonomous systems, programmable (logic) control design, robotics, real-time planning, safety-critical systems, Petri nets and PLC standards such as IEC1131.

Lyapunov-function-based design of fuzzy logic controllers and its application on combining controllers

This paper presents the design of fuzzy logic controllers (FLCs) for nonlinear systems with guaranteed closed-loop stability and its application on combining controllers. The design is based on heuristic fuzzy rules. Although each rule in the FLC refers to a stable closed-loop subsystem, the overall system stability cannot be guaranteed when all these rules are applied together. In this paper, it is proved that if each subsystem is stable in the sense of Lyapunov (ISL) under a common Lyapunov function, the overall system is also stable ISL. Since no fuzzy plant model is involved, the number of subsystems generated is relatively small, and the common Lyapunov function can be found more easily. To probe further, an application of this design approach to an inverted pendulum system that combines a sliding-mode controller (SMC) and a state feedback controller (SFC) is reported. Each rule in this FLC has an SMC or an SFC in the consequent part. The role of the FLC is to schedule the final control under different antecedents. The stability of the whole system is guaranteed by the proposed design approach. More importantly, the controller thus designed can keep the advantages and remove the disadvantages of the two conventional controllers.

Application of fuzzy logic control for continuous casting mold level control

This paper deals with the problem of molten metal level control in continuous casting. Under normal circumstances, proportional integral derivative (PID) control performs quite well, but abnormal conditions (in particular nozzle clogging/unclogging) require manual intervention. Indeed, when the flow of matter into the mold increases suddenly, the PID controller is not always able to prevent large level variations that can even lead to mold overflow. So, a fuzzy controller has been designed using the expert knowledge of the operators for controlling the process during disturbed phases. The paper discusses both the design of the fuzzy logic controller and its integration with the PID in a global control architecture. Results from simulation and successful online implementation are presented.

Design of a VMEbus-based programmable logic controller (PLC)

This paper presents an overall view of the architecture and design of a programmable logic controller (PLC). The main objectives of the work are to design, develop, and implement a versatile PLC processor module (PLCPM) based on an industrial open bus architecture called VMEbus (IEEE 1014 Versa Module Euro-standard). The controller is inserted inside the VME crate and controls the industrial process via input and output modules that reside in the crate. The PLCPM is designed to be an intelligent module through the use of a Motorola MC68000 CPU. A method of distributed arbitration protocol, based on an algorithmic state-machine design approach, is added to the design of this module. This facility makes PLCPM suitable to work inside a VMEbus environment. The PLCPM therefore becomes adequate for use in multiprocessing PLC systems. The controller uses a host personal computer (IBM-PC) as a versatile and indispensable system component for process development, monitoring, control and supervision. Software and firmware programs are developed and written for both host-PC and PLCPM using standard C-language and 68000 assembly language, respectively. This results in a sequential control algorithm for the PLCPM and windowing user interface for the host-PC. Ladder diagram programming language is supported by this user interface.

Design of new adaptive fuzzy logic controller for nonlinear plants with unknown or time-varying dead zones

An adaptive fuzzy logic controller (FLC) is designed for plants with unknown and/or time-varying dead zones. The steady-state control resolutions with perturbing action, which are different from the ones in the transient states, are used to cancel out the unknown and/or time-varying dead-zone effects. Automatically adjusted control resolutions play a key role as a fuzzy dead-zone inverse. The control resolutions of the control input variables are dependent on the scaling gains of the variables. Therefore, we can develop the fuzzy dead-zone inverse by reperturbing and adjusting the scaling gains adequately in the steady-states. The developed fuzzy logic controllers that are applied to the plants with unknown dead zones ensure their effectiveness even though the dead-zone characteristics are time varying.

A synchronous generator fuzzy excitation controller optimally designed with a genetic algorithm

Design of a fuzzy logic power system controller with satisfactory performance is not an easy task. The difficulties come from two aspects. First, design of a fuzzy logic controller mainly uses the experience of the human experts. To acquire enough heuristic knowledge from the domain experts and to represent this kind of knowledge appropriately with a set of fuzzy rules present difficulties. Second, it is difficult to appropriately tune the parameters used in the fuzzy logic controller. These parameters are commonly determined by a "trial and error" method which is rather time consuming. In this paper, genetic algorithm is introduced to design an optimal fuzzy logic controller. The proposed method has been used to design an optimal fuzzy logic excitation controller for a generating unit. Test results with the fuzzy logic controller show very satisfactory results.

Fuzzy control design for the trajectory tracking in phase plane

Based on the concept of sliding mode control, the paper designs a fuzzy logic control for a linear system to achieve the trajectory tracking in the phase plane. That is, the system's state (e, e/spl dot/) is controlled to track the prespecified trajectory which is composed of several nonisoclinal segments. Each segment of the prespecified trajectory denotes the relation between the tracking error e and the error change e/spl dot/ in the corresponding region of the phase plane. In the paper, the prespecified trajectory is regarded as a sliding surface. A direct piecewise linear output formulation of the fuzzy logic controller is synthesized to achieve the hitting motion such that the trajectory tracking is completed region by region. We summarize the above analysis to be a fuzzy logic control design algorithm and give a practical example to illustrate its applicability.

Cross breeding in genetic optimisation and its application to fuzzy logic controller design

This paper describes a simple genetic algorithm (GA) based on the idea of cross breeding ‘chromosomes’ from different solution populations. The proposed genetic algorithm has been tested against a standard GA on the problem of designing fuzzy logic controllers. The paper presents the simulation results obtained for the control of a second-order time-delayed plant. These demonstrate the effectiveness of the proposed algorithm.

Design of a fuzzy logic controller as a suction controller

A fuzzy logic controller (FLC) designed as a suction controller is proposed. Firstly, we prove that there exists a switching line in the FLC, and the slope of the switching line is determined by scaling factors. Secondly, a Lyapunov function is selected and sufficient conditions for the fuzzy control system (FCS) to be globally asymptotically stable is determined. Based on the sufficient conditions, a design procedure is suggested to guarantee that the FCS is globally asymptotically stable. Both a nonlinear oscillator and a cart-pole system are simulated to demonstrate the proposed FLC. We show that not only is the FLC similar to sliding mode control (SMC), but also a realizable method for a suction controller is described. In addition, a lemma is introduced to interpret that the rise time of the FCS can be adjusted by the scaling factors.

An improved fuzzy logic controller using genetic algorithmic techniques for building management systems

Abstract This paper presents the design and control performance of a fuzzy logic controller (FLC) based on the system’s output errors for a building management system (BMS). The system comprises a heating, ventilating and air conditioning (HVAC) plant which regulates the air temperature and relative humidity in a full-scale office room. The plant has 3 control inputs, namely the heater, cooler and humidifier. It has been shown that good set point regulation can be achieved by employing 5 fuzzy input set membership functions for each output error and 3 fuzzy output set membership functions for each incremental control input as well as having good relational rules to determine the control strategy. It is also shown that the performance can be further improved by employing genetic algorithms to search for the 9 optimal fuzzy set output membership functions while maintaining the 10 fuzzy input sets constant.

On-Line Gain Adjustment of a Robot Controller, Using Fuzzy Logic

A new method for on-line tuning the gains of classical controllers using fuzzy logic is proposed. The design of the fuzzy component of the controller is based on the knowledge of a classical controller's behavior. In the design of the fuzzy logic controller, an off-line neural network learning process and the human expertise are combined. The designed fuzzy-PD controller is compared with the classical PD and it is found that the fuzzy-PD controller saves energy without losing in performance. A two rotational degree of freedom robotic arm is used to demonstrate the effectiveness of the proposed control law.

Computer-Based Fuzzy Control of a Traction Motor in a Metro Vehicle

Abstract In this paper, proposed new control method of a separately excited dc traction motor using fuzzy logic. The design of a fuzzy logic controller for a dc motor drive, that produces the traction moment for light metro vehicle, is illustrated and implemented. The drive system has been simulated and results compared with those obtained by a conventional PID controller. The superior results in the drive performance which are achieved by fuzzy logic controller are provided. The proposed controller exhibits superior transient and steady-state performance compared to usual PID controllers. Rule-based fuzzy logic controller is implemented using a personal computer with a data acquisition card. The controller is implemented on a 486 PC using C++ program.

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.

Fuzzy variable structure control

A new methodology is presented to improve the design and tuning of a fuzzy logic controller (FLC) using variable structure control (VSC) theory. A VSC-type rule base is constructed and the fundamentals of FLC explored quantitatively by VSC theory. A very concise mathematical expression for the FLC is presented, in which the Lyapunov stability criterion can be applied to guide the design and tuning. This results in a simpler and more systematic procedure. Application of the method to higher order systems is made straight forward by applying a hierarchical technique. The validity of the design methodology is demonstrated by simulation.

Genetic algorithms with variable mutation rates: Application to fuzzy logic controller design

Three variable mutation rate strategies for improving the performance of genetic algorithms (GAs) are described. The problem of optimizing fuzzy logic controllers is used to evaluate a GA adopting these strategies against a GA employing a static mutation regime. Simulation results for a second-order time-delayed system controlled by fuzzy logic controllers (FLCs) obtained using the different GAs are presented.