Design Of Logic Controller Research Articles

A Novel Model Predictive-Fuzzy Logic Hybrid Controller Design for D-STATCOM in Wind Energy Distribution Systems

Wind energy is a resource that does not require any fuel or feedstock to be treated, refined or transported. It does not produce air pollution and hazardous waste. Because of these, wind energy, one of the major renewable energy sources, has been widely used in recent years. In this work, a three-phase distribution static synchronous compensator (D-STATCOM) is demonstrated for power quality enhancement in wind energy distribution systems. A five-level cascaded inverter is used to implement the D-STATCOM. D-STATCOM is controlled to compensate the reactive power, balances the load and eliminate harmonics. A novel control algorithm is based on the indirect current control system with synchronous reference frame theory for generating the gating pulses of Voltage Source Inverter (VSI). This proposed control system has one model predictive controller and two modified fuzzy logic controllers. The performance of the three-phase D-STATCOM is validated for power quality enhancement with different loads through software implementation using Matlab/Simulink environment. The effectiveness of the proposed approach is verified through simulation studies.

Design and comparison of a fuzzy logic controller for Active Queue Control

Network blockage administration is a complicated hindrance which needs quick to recover, maybe swift, and control schemes to acquire adequate working. Designing equipped blockage handling schemes for set of connections for computers is identified to be hard due to the difficulty of obtaining realistic, fine analytical models. It becomes really difficult to apply the conventional control methodology; that depend on the obtain-ability of these models. It has been reported that effective solutions for certain classes of control problems may be achieved by employing an Intelligent Fuzzy Logic Control methodology. Being specifically suitable for complex and non-linear systems; when it becomes difficult to obtain an analytical model, on the contrary it is made possible to capture the understanding of their behavior by linguistic models. As a result, many of the researchers have used fuzzy logic to devise efficient techniques for congestion control.

A Novel Fuzzy Self Tuning Technique of Single Neuron PID Controller for Brushless DC Motor

In this paper, a combination ANN/Fuzzy technique is used to design a Novel Fuzzy Single Neuron PID (NFSNPID) controller to achieve high performance brushless DC motor. The design steps include two parts. The first part uses the genetic algorithm (GA) to find the optimum parameters of Single Neuron PID (SNPID) controller, while the former deals with the design of fuzzy logic control to update the weights of SNPID control online. To demonstrate the designed controller effectiveness, a comparative study is made with between the NFSNPID, Conventional Fuzzy Single Neuron PID CFSNPID and SNPID. All controllers were used to drive, separately, the brushless DC motor against the sudden change of load and operating speed. The performed simulations show better results that motivate for further investigations.

Function Block for Rotary Kiln Coating Disintegration Detection

Condition monitoring is an important field that can boost manufacturing processes to produce high quality and more quantities. This work presents the design of new Programmable Logic Controller (PLC) Function Block (FB) dedicated to the instantaneous detection of any coating piece disintegration from the wall coating formed by the processed material sticking naturally on the inner side of the cement rotary kiln. The detection is based on the on-line analysis of the kiln spinning electrical motor’s signal. The FB makes use of differences between the adjacent signal’s measurements values and logic functions to hide false alarms for the user whenever they break out due to kiln velocity set point abrupt changes. In steady state process operation, the technique provides highly reliable and accurate detection. Its application can be extended to any similar instantaneous abrupt changes occurring in other manufacturing processes.

Fuzzy Logic-Based Energy Management System Design for Residential Grid-Connected Microgrids

This paper presents the design of a low complexity fuzzy logic controller of only 25-rules to be embedded in an energy management system for a residential grid-connected microgrid including renewable energy sources and storage capability. The system assumes that neither the renewable generation nor the load demand is controllable. The main goal of the design is to minimize the grid power profile fluctuations while keeping the battery state of charge within secure limits. Instead of using forecasting-based methods, the proposed approach use both the microgrid energy rate-of-change and the battery state of charge to increase, decrease, or maintain the power delivered/absorbed by the mains. The controller design parameters (membership functions and rule-base) are adjusted to optimize a pre-defined set of quality criteria of the microgrid behavior. A comparison with other proposals seeking the same goal is presented at simulation level, whereas the features of the proposed design are experimentally tested on a real residential microgrid implemented at the Public University of Navarre.

Intelligent control of an MR prosthesis knee using of a hybrid self-organizing fuzzy controller and multidimensional wavelet NN

A Magneto rheological (MR) rotary brake as a prosthesis knee is addressed here. To the gait of the amputee, the brake, automatically adapts knee damping coefficient using only local sensing of the knee torque and position. It is difficult to design a model-based controller, since the MR knee system has nonlinear and very complicated governing mathematical equations. Hence, a Hybrid self-organizing fuzzy controller and multidimensional wavelet neural network (HSFCMWNN) is proposed here to control the knee damping coefficient using of the inverse dynamics of the MR rotary damper. A Self-organizing fuzzy controller (SOFC) is also proposed and during the control process, the SOFC continually updates fuzzy rules, while at the beginning contains blank fuzzy rules. Therefore the problem of finding appropriate fuzzy rules and consequently membership functions for the design of a fuzzy logic controller is ignored by using of the SOFC. It is, however, difficult to select appropriate parameters (learning rate and weighting distribution) in the SOFC which are crucial for control of MR prosthesis knee. To deal with this drawback, a hybrid self-organizing fuzzy and multidimensional wavelet neural network is employed appropriately. The proposed strategy uses a Multidimensional wavelet neural network (MWNN) to adjust these parameters in real time, to achieve to optimal values. First, the MWNN is trained by Stochastic gradient algorithm (SGA) and then to reduce the learning cycle to be needed and the appropriate error, a learning procedure based on the Levenberg-Marquardt (LM) algorithm is adopted intentionally. Simulation results demonstrate that the HSFCMWNN performs better in control performance than that of the SOFC in improving gait of the amputee.

Decentralized Multi-UAV Flight Autonomy for Moving Convoys Search and Track

This brief is concerned with integrated autonomous takeoff, target search, task assignment, and tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) in urban environments. The problem is to design flight autonomy that is embedded onboard each UAV to enable autonomous flight coordination and distributed tasking. Control logic design based on a finite state automaton model, integrating four modes of operations, namely, the takeoff mode, the fly-to-area of operation mode, the search mode, and the tracking mode, is presented. Different from the state-of-the-art of recent research, this brief provides a preliminary research on the autonomous cooperative takeoff for miniature fixed-wing UAVs, by considering collision avoidance, communication failure, etc. To make UAVs autonomously and cooperatively search roads in the urban environments, an efficient improved search algorithm is proposed based on recent research on the coverage search in the literature. For the target tracking, using geometric relations (relative position, orientations, speed ratio, and minimal turning radius), a systematic algorithm is developed to generate an optimal online path. All the algorithms in this work are developed based on realistic miniature fixed-wing UAV dynamic models. The main focus of the brief is to test the developed control logic and also the algorithms. The proposed framework is evaluated by our 3-D multi-UAV test bed.

English

English

Automatic design of fuzzy logic controllers for medium access control in wireless body area networks – An evolutionary approach

Soft computing techniques including fuzzy logic have been successfully applied to wireless body area networks (WBANs). However, most of the existing research works rely on manual design of the fuzzy logic controller (FLC). To address this issue, in this paper, we propose an evolutionary approach to automate the design of FLCs for cross layer medium access control in WBANs. With the goal of improving network reliability while keeping the communication delay at a low level, we have particularly studied the usefulness of three coding schemes with different levels of flexibility during the evolutionary design process. The influence of fitness functions that measure the effectiveness of each possible FLC design has also been examined carefully in order to achieve a good balance between reliability and performance. Moreover, we have utilised surrogate models to improve the efficiency of the design process. In consideration of practical usefulness, we have further identified two main design targets. The first target is to design effective FLCs for a specific network configuration. The second target focuses on designing FLCs to function across a wide range of network settings. In order to examine the usefulness of our design approach, we have utilised two widely used evolutionary algorithms, i.e. particle swarm optimisation (PSO) and differential evolution (DE). The FLC designed by our approach is also shown to outperform some related algorithms as well as the IEEE 802.15.4 standard.

Design of Type-2 Fuzzy Logic Controller for Fluid Catalytic Cracking Unit

Modelling and controlling of fluid catalytic cracking unit (FCCU) is a challenging problem since it is a highly nonlinear and interacting process. Model of FCCU that describes the dynamic behaviour of reactor and regenerator has been developed by considering energy and material balance equations. Fuzzy logic control technique in the field of intelligent control has been used in this paper since it handles uncertainty very well. In this work, centralised Mamdani types T1-FLC and T2-FLC have been designed for FCCU to control the reactor and regenerator temperature (RT). The closed-loop simulation study for servo and servo with regulatory performance has been carried out for the two controllers and the performances have been analysed. The comparative study of these two control schemes reveals that the type-2 FLC gives better performance than type-1 FLC.

Design of Fuzzy Logic Controller for Reduction of Vibration in Full Car Model using Active Suspension System

Design of Fuzzy Logic Controller for Reduction of Vibration in Full Car Model using Active Suspension System

Fuzzy Logic Controllers for Specialty Vehicles Using a Combination of Phase Plane Analysis and Variable Universe Approach

For the control request of specialty vehicles, this paper combines a phase plane analysis with the variable universe fuzzy control technique. Moreover, we develop a new systemic design strategy for the adaptive fuzzy logic controller. With the phase plane analysis, the complete rule base that consists of few key rules can be objectively established, which avoids the irrationality introduced into the process via the subjectivity of the designer of the fuzzy controllers. Furthermore, with the variable universe, the design requirements of the membership functions can be relaxed. Meanwhile, the accuracy of the performance of the fuzzy logic controller can be enhanced despite the limited number of fuzzy rules in the rule base. Based on the Lyapunov stability theory, the stability of the close-loop system can be guaranteed. Simulation results of a double inverted pendulum demonstrate the feasibility of the design strategy for the adaptive fuzzy logic controller, which simplifies the design of the fuzzy logic controller and ensures control. The application of this design strategy can significantly lighten the burden for fuzzy logic controller designers and shorten the development period of the fuzzy logic controller.

Design of supervisor-based adaptive process fuzzy logic control

The industrial processes are nonlinear, subjected to disturbances, inertial, with time delay and parameters that change with the operating point, over the time or both. The adaptive fuzzy logic controllers (AFLCs) are promising in controlling of such plants ensuring a high system performance. Their complex structure and design and the computational load required restrict their wide application on industrial scale. An approach for the design of simple AFLCs is suggested on the basis of a main FLC and a FL supervisor (FLS) with input the online estimated current plant gain and output correction of selected scaling factors. The effect - a continuous adaptation of the control surface in response to plant changes, is equivalent to the sophisticated self-organising techniques. The needed input data are expert assessed ranges for the plant gain, plant delay and system error. The approach is applied for the design of process AFLCs for a chemical reactor and a two-tank plant using main FLCs of Mamdani type and parallel distributed compensation. The results from simulations show decreased overshoot, settling time, control effort and coupling in the whole range of operation and parameter changes. The comparison is made with a FLC system and a linear PI system.

Design of supervisor-based adaptive process fuzzy logic control

Design of supervisor-based adaptive process fuzzy logic control

Comments on “Design of two-layered fractional order fuzzy logic controllers applied to robotic manipulator with variable payload”

Sharma et al. have investigated the performance of two-layered fractional order fuzzy logic controller (TL-FOFLC) for a 2-link rigid planer robotic manipulator with payload. In this work, the performance of TL-FOFLC has been compared with two-layered FLC (TL-FLC), single-layered FLC (SL-FLC) and the conventional proportional-integral-derivative (PID) controllers, for trajectory tracking, model uncertainties and disturbance rejection. In this comment, it is pointed out that this work has several missing essential parameters, and therefore, it is not possible for the reader to validate all the claimed results of Sharma et al. (2016). Six numerical values, three gains for each of the used two PID controllers are found to be unreported in addition to the six gains for each of the used two SL-FLCs. Since the performances of the PIDs and the SL-FLCs are highly dependent on their tuned gains it is concluded that the reported performances of these controllers cannot be validated.

Design and Implementation of Sliding Mode Controller Plus Fuzzy Logic Controller for Discontinuous Conduction Mode Operation of Negative Output Elementary Super Lift Luo Converter

Design and Implementation of Sliding Mode Controller Plus Fuzzy Logic Controller for Discontinuous Conduction Mode Operation of Negative Output Elementary Super Lift Luo Converter

Fuzzy Logic Control of Soft-Switching DC-DC Converter

The paper presents a novel control technique for a phase-shifted PWM (PS-PWM) soft-switching dc-dc converter. The converter is a full bridge dc-dc converter with controlled output rectifier and an active snubber on the secondary side. To solve the problems with nonlinear nature of the dc-dc converter a fuzzy logic control is used. The fuzzy logic controller design and simulation is presented. The performance of the designed fuzzy logic controller is compared to classical PI controller.DOI: http://dx.doi.org/10.5755/j01.eie.22.5.16334

Experimental evaluation of a fuzzy logic controller on a quarter car test rig

This paper considers the experimental investigation of a multiple-input single-output fuzzy logic controller on an active suspension system based on a quarter car test rig. Prior to implementation of the controller, the quarter car test rig is described in detail. Afterward, the structure and design of the implemented fuzzy logic controller is presented. In this study, fuzzy logic control is preferred, since it does not require the exact mathematical model of the system and allows using expert knowledge about vehicle suspensions. Experimental results are presented and discussed via extensive time and frequency responses. Classical fuzzy logic controller is also utilized for comparison. Time responses of sprung mass displacements and accelerations, suspension deflections and actuator forces are compared for passive system and active controllers. Finally, the frequency responses of sprung mass displacements and accelerations are also compared. From the results it is deduced that the multiple-input single-output fuzzy logic controller performed well when compared with passive and classical fuzzy logic controlled systems.

Fuzzy logic controllers design for omnidirectional mobile robot navigation

This paper presents a new design approach for an intelligent navigation algorithm for omnidirectional mobile robots. Unlike the previous works dealing with the navigation of omnidirectional robots that are focused on only the estimation of the final position, the main contribution of the present study is summed up in the fact that the robot has to reach the final desired position with a predefined final steering angle. This latter improvement is a part of researches carried out on Intelligent Transport Systems (ITS). Taking into consideration the drawbacks of proportional integral (PI) control when applied to omnidirectional robot navigation, we develop an approach to design a fuzzy logic PI controller (Fuzzy-PI).Preliminary simulation and experimental results using the Fuzzy-PI controller have shown limitations as the robot performed a larger path when the desired final angle increased. Thus, a deepen study have concluded that the Fuzzy-PI system cannot control at the same time the linear and angular velocities. To overcome these drawbacks, we propose to replace the previous intelligent navigation system by two independent controllers. The designed Fuzzy-PI controllers can adjust their parameters (KP, KI) to reduce the error caused by the dynamic changes and navigation challenges of omnidirectional robot. A navigation algorithm cannot be efficient without obstacle avoidance system. To achieve this goal, we have developed a third fuzzy controller to fulfill this task.To evaluate the real performances of fuzzy controllers for navigation and obstacles avoidance, simulation and experimental tests are performed for one, two and three obstacles. The obtained results confirm that the omnidirectional robot could navigate in an unstructured and unknown obstacles environment with better performances and efficiency.

Maximum power point tracking using a variable antecedent fuzzy logic controller

Maximum power point tracking (MPPT) techniques are popularly used for maximizing the output of solar panels by continuously tracking the maximum power point of their P-V curves, which depend both on the panel temperature and the input insolation. Various MPPT algorithms have already been studied in literature, including perturb and observe (P&O), hill climbing, incremental conductance and neural networks. In particular, fuzzy logic control (FLC) is an another popular technique which achieves a significantly improved performance in MPPT in terms of response speed and no oscillations about the maximum power point (MPP). Unfortunately, a major issue that arises in classical FLC based MPPT algorithms is the lack of versatility to rapidly changing environmental conditions such as the applied irradiance. This paper presents an alternative design of an adaptive MPPT fuzzy logic controller which utilizes simple formulae instead of complex learning algorithms to adjust the antecedents. To verify the proposed MPPT system, a customized off the shelf solar panel is connected to a SEPIC converter and the overall system is both simulated on Simulink and experimentally verified. The resulting response is shown to be fast and stable in comparison to previous designs which used fixed fuzzy logic antecedents that need to be manually modified whenever the environmental conditions change or if a different solar panel is used.