Fuzzy-based Control Research Articles

A High Reliable Multicast Routing Algorithm for 2D and 3D Mesh-based NoCs with Fuzzy-based Load Control

A High Reliable Multicast Routing Algorithm for 2D and 3D Mesh-based NoCs with Fuzzy-based Load Control

Adaptive fuzzy-based composite anti-disturbance control for a class of switched nonlinear systems with unknown backlash-like hysteresis

For a class of switched nonlinear systems with unmatched external disturbances and unknown backlash-like hysteresis, an adaptive fuzzy-based control strategy is proposed to handle the anti-disturbance issue. The unmatched external disturbances come from a switched exosystem. Our aim is to achieve the output tracking performance and the disturbance attenuation by using the adaptive fuzzy-based composite anti-disturbance control technique. First, based on the fuzzy logics, we design a switching adaptive fuzzy disturbance observer to estimate unmatched external disturbances. Second, a composite switching adaptive anti-disturbance controller is constructed. By means of the backstepping technique, disturbance estimations are added in each virtual control to offset the unmatched disturbances, which results in the different coordinate transformations. At last, the availability of the proposed approach is illustrated by a mass-spring-damper system.

Fuzzy-Based Charging–Discharging Controller for Lithium-Ion Battery in Microgrid Applications

This article presents the fuzzy-based charging-discharging control technique of lithium-ion battery storage in microgrid application. Considering available power, load demand, and battery state-of-charge (SOC), the proposed fuzzy-based scheme enables the storage to charge or discharge within the safe operating region. Various controlling techniques have been implemented to evaluate and control the battery performance, which has the limitation of controlling overcharging or overdischarging, complexity in control, and longer charging time. Besides, a fuzzy controller is less complex and faster, as it obviates the extra sensing components, requires no additional deep discharging and overcharging protection, and easy to implement due to the absence of mathematical calculation. The numerical simulations with the load demand and the generations demonstrate the effectiveness of the proposed charging-discharging controller strategy. The efficacy of the proposed controller is tested under certain load variations for real-time application. The obtained experimental result shows that the developed model can control the battery charging-discharging efficiently. Moreover, it is also seen from the output that the battery SOC does not go beyond the limit of the respective safe battery operating region (20%-80%). Thus, the main contribution of this research is to develop an improved fuzzy model and, thus, implement the system for real-time application to control the charging-discharging of the battery.

Self-learning PSO based optimal EVs charging power control strategy for frequency stabilization considering frequency deviation and impact on EV owner

Nowadays, electric vehicles (EVs) raise numerous challenges and opportunities in microgrids. The EV deployment is increasingly gaining in importance for the purpose of providing ancillary services, especially grid frequency support. This paper presents a multi-objective optimal EVs charging power control strategy providing frequency stabilization in microgrids. The proposed strategy employs a dynamic droop controller improved by a Takagi–Sugeno (TS) fuzzy logic system, yielding good dynamic response and robustness. In this paper, the proposed strategy particularly allows an EV aggregator to temporarily reduce or pause the EVs charging power under the permissible range. Moreover, a self-learning particle swarm optimization (SLPSO) is applied to optimally tune the membership functions in the fuzzy logic system based on the multi-objective optimization problem, minimizing frequency deviation and impact on EV owner in terms of charging power deviation. The proposed strategy is investigated using AC microgrids performed by DIgSILENT Powerfactory simulation software. The simulation study is carried out to verify the performance and effectiveness of the proposed strategy in comparison with some existing control strategies. The simulation results reveal that the proposed strategy offers optimal solutions for frequency stabilization in terms of satisfying the grid utility and EV owner perspectives simultaneously. • A multi-objective optimal EVs charging power control strategy providing frequency stabilization is proposed. • A fuzzy-based droop controller is optimized by a self-learning particle swarm optimization (SLPSO) algorithm. • Minimizing frequency deviation to satisfy the grid utility perspective. • Minimizing EVs charging power deviation to satisfy the EV owner’s perspective.

Fuzzy logic based automatic slip control system for agricultural tractors

In this study, a fuzzy-based automatic slip control system was developed for agricultural tractors. The developed system continuously measures the amount of slip that occurs during the tillage activities and automatically changes the operation depth of tillage equipment according to the amount of increase in the slip value. The amount of slip occurring on the driving wheels was applied as a separate input to the designed fuzzy control system (FCS) and at the fuzzy rule base, it was decided how much the depth of tillage would be reduced. The system was mounted on an agricultural tractor and trials were carried out in actual field conditions. The results of the tillage trials performed with the developed FCS were compared separately with the results of the tillage trials performed with an operator control (OC) without using any automatic control system. As a result of the trials, compared to the operator control, it was determined that in the tillage activities carried out with FCS, there were 42% decreases in slip values, 30% decreases in draft force values, 44% decreases in fuel consumption values and 5% increases in field work performance values. It was also observed that there were 10% changes in tillage depth.

Guidance and control methodologies for marine vehicles: A survey

Offshore mechatronics systems engineering has recently received high attentions in various sectors such as energy, transportation, etc. Specifically, offshore robotic vehicles have become challenging topics in terms of design, guidance, control and maintenance aspects. Based on these reasons, in this article, some recent developments on the guidance and control methodologies for marine robotic vehicles are surveyed. The application-oriented methodologies under consideration mainly include fuzzy-based control design approach, neural network-based control design scheme, dynamic surface control strategy, feedback control technique as well as sliding model control method, for instance. With the help of these methodologies, the developments on various guidance and robust control issues are reviewed in great detail. In practical engineering, guidance and control design problems are mainly addressed for the maneuvering, path following, trajectory tracking, formation control and consensus. In particular, the state of art of the solution to guidance and control issues for marine robotic vehicles under different control methodologies is addressed respectively. Finally, some latest research results in filtering and control design developments are introduced, some conclusions are drawn and several possible future research directions based on the latest results are pointed out.

Hierarchical distributed model predictive control based on fuzzy negotiation

This work presents a hierarchical distributed model predictive control approach for multiple agents with cooperative negotiations based on fuzzy inference. Specifically, a fuzzy-based two-layer control architecture is proposed. In the lower control layer, there are pairwise negotiations between agents according to the couplings and the communication network. The resulting pairwise control sequences are sent to a coordinator in the upper control layer, which merges them to compute the final ones. Furthermore, conditions to guarantee feasibility and stability in the closed-loop system are provided. The proposed control algorithm has been tested on an eight-coupled tank plant via simulation.

Fuzzy Adaptive Control of Nonlinear Systems with Odd System Powers, Complex Dynamics, and Uncertain Control Coefficients

The adaptive stabilization issue for complicated nonlinear systems is investigated in this work. The discussed problem is comparatively challenging since the system owns non-triangular structure and involves unknown coefficients, unknown parameters, and complex dynamics. By proposing an improved fuzzy-based adaptive control approach, a new stable control law is designed to make semi-globally uniformly ultimately bounded (SUUB). The presented method is validated with a simulation example.

Thermal Control Performance of a Network-based Learning Controller in a Very Hot and Humid Area

Purpose: In order to improve the energy strategies for a building space, various control methods of supply air have been examined in aspects of its energy efficiency and users’ thermal satisfaction. This simulation study examines the network-based learning model’s capability to maintain the thermal comfort without compromising on its energy efficiency especially in a very hot and humid area. Method: A network-based learning controller is proposed to optimize the supply air conditions to meet desired values of set-point temperature and comfort levels by regulating the amount of air and its temperature elaborately. Result: In a comparison with two other types of controllers, the proposed model improves the thermal comfort by about 29.3% than the conventional thermostat controller, and increases the energy efficiency by about 17.2% than the fuzzy-based controller. It is concluded that the network-based learning algorithm properly works to manage its supply air condition to improve spatial thermal quality in a very hot and humid area.

Stabilization of permanent magnet synchronous generator-based wind turbine system via fuzzy-based sampled-data control approach

This study concerns the stabilization analysis for nonlinear permanent magnet synchronous generator (PMSG)-based wind turbine system under fuzzy-based memory sampled-data (FBMSD) control scheme. In this regard, the Takagi-Sugeno (T-S) fuzzy theory is utilized in the conversion of the proposed nonlinear model into linear-sub models and the corresponding FBMSD controller is designed. The paper introduces a suitable Lyapunov–Krasovskii functional that contains the information about the length of the sampling interval and a constant transmission delay. In order to stabilize the proposed system, sufficient conditions are derived in the form of linear matrix inequalities. As a test benchmark, the derived T-S fuzzy PMSG model is evaluated with a particular data set values and then validated with derived conditions. Besides that, the dynamical nature of system variables with respect to a blade pitch angle of the wind turbine system also discusses along with d-q axes inductance via numerical simulations. Finally, a comparison of derived conditions with the existing works is discussed to prove the less conservatism of the proposed method.

Multi-Agent Fuzzy-Based Transit Signal Priority Control for Traffic Network Considering Conflicting Priority Requests

The performance of transit signal priority (TSP) with conflicting priority requests highly depends on the serving sequence of multiple TSP requests. A series of existing methods have been developed to determine the priority level of requests. However, most of these methods focused on isolated intersections or a small number of intersections, which are not applicable to complex, dynamic and nonlinear urban traffic networks. In this regard, we propose a multi-agent TSP control method at the network level considering conflicting priority requests. Fuzzy inference is used to manage signal control. We further develop a specific control algorithm. The performance of the proposed method is verified by a case study with a sizeable traffic network with 20 intersections and 49 links. Simulation results demonstrate that the proposed method outperforms other three benchmarking methods under different traffic demands and bus departure frequencies. It is worth-noting that the improvement becomes more notable with the increase of traffic demands and the reduction of bus departure frequencies.

Hybrid Fuzzy Fractional-Order PID-Based Speed Control for Brushless DC Motor

This article presents a novel hybrid control scheme for speed control of Brushless DC (BLDC) motor by simultaneously controlling BLDC motor reference current and inverter DC bus voltage. A fractional-order PID (FOPID) controller is employed to control BLDC motor reference current while a fuzzy logic controller manipulates the inverter DC bus voltage. A modified harmony search (HS) metaheuristic technique is developed for FOPID controller parameters tuning. Three different operating conditions are applied to test the motor, including no-load operation, varying load operation, and varying speed operation to verify the proposed controller’s effectiveness. Furthermore, the proposed hybrid control strategy has been compared to Fuzzy-based and FOPID-based speed control schemes. The obtained results confirm that the proposed control scheme provides better and accurate speed control over a wide range of speeds. Also, the proposed controller decreases the torque ripples under different operating conditions.

Optimized Fuzzy-Based Wavelet Neural Network Controller for a Non-Linear Process Control System

One of the common non-linear process control problems is the continuous stirred tank reactor problem and the reactor is applied widely in chemical process industries. It is of high importance to develop a suitable controller scheme for the concentration and temperature control of the considered non-linear continuous stirred tank reactor (CSTR) model. In this paper, a novel wavelet neural network (WNN) controller model is developed to carry out the control action and to meet the performance requirements of the system model. The developed wavelet neural network model is tuned for its weights employing the proposed deterministic grey wolf optimizer algorithm and its input is fed from the Mamdani fuzzy model. The optimal number of rules to be formulated for the fuzzy rule base model is computed using the deterministic grey wolf optimizer (DGWO) which is the variant of the classic grey wolf optimizer (GWO). WNN controller devise, based on the inputs from the fuzzy model and weights from DGWO, is designed to perform control action on the non-linear CSTR model. The concentration and temperature control is carried out by the proposed controller and the responses are obtained. Also, the respective time response specifications are evaluated for the developed WNN controller. Simulation experiments prove the effectiveness of the proposed controller for the considered non-linear CSTR model in comparison with that of the existing controllers from the literature.

Design of Interval Type-2 Fuzzy-Based Sampled-Data Controller for Nonlinear Systems Using Novel Fuzzy Lyapunov Functional and its Application to PMSM

This paper presents interval type-2 fuzzy-based sampled-data controller for nonlinear systems using novel fuzzy Lyapunov functional. To do this, a type-2 fuzzy model is adapted for the nonlinear systems and a type-2 fuzzy-based sampled-data controller is designed. Based on the novel time-dependent Lyapunov–Krasovskii functional with membership functions, the sufficient conditions are derived to stabilize the proposed closed-loop system. The stabilization conditions are formulated in terms of linear matrix inequalities. Finally, in the numerical section, the proposed method is validated with nonlinear permanent magnet synchronous motors model which is formulated for reflecting the characteristics of the wind turbine. Besides that, we demonstrate the superiority of the derived sufficient conditions through the comparison with existing methods.

Coordination of Generators and Energy Storage to Smooth Power Fluctuations for Multi-Area Microgrid Clusters: A Robust Decentralized Approach

This paper addresses frequency deviation and tie-line power fluctuation in the islanded multi-area microgrid clusters (MMGCs). In the islanded MMGCs, the uncertain and varying nature of energy provided by renewable energy resources makes the issues of frequency and tie-line power more difficulty and complexity than the traditional power system. In response to these issues, a decentralized robust smoothing control strategy with the coordination of generators and energy storage system (ESS) is designed. First, taken into account the tie-line power change between sub-microgrids, the storing/releasing energy of energy storage system and the dynamic complexity of renewable energy, the detailed state space dynamic model is presented. Then, the architecture of the decentralized robust smoothing control strategy in MMGCs, which contains generator control loop and energy storage control loop, is proposed. Based on the architecture of robust smoothing control strategy, the microgrid smoothing control (MSC) in generator control loop and energy storage smoothing control (ESSC) in energy storage control loop are designed. In the MSC, by using linear matrix inequality LMI) technique, the rolling optimization mixed $H_{2}/H_{\infty }$ control with regional pole placement is proposed for regulating the generator power. Third, considering the overcharging and discharging of supercapacitors, the fuzzy-based self-adaptive energy storage smoothing controller (ESSC) of the supercapacitor is proposed to improve the low inertia of islanded MMGCs while smooth tie-line power fluctuation and frequency deviation. Finally, the robustness and performance of decentralized robust smoothing control is validated in the presence of various disturbances. The results are compared with other controller. Simulation results show the effectiveness and robustness of the proposed controller.

A Fuzzy-Based Congestion Control Scheme for Vehicular Adhoc Network Communication

Vehicular ad hoc network (VANET) is a self-organized, multi-purpose, service-oriented communication network that enables communication between vehicles and between vehicles and roadside infrastructures for the purpose of exchanging messages. In a dense traffic scenario, the message traffic may generate a load higher than the available capacity of the transmission medium leading to channel congestion problem. This situation leads to a rise in packet loss rates and transmission delay. Some existing congestion control schemes adapt the transmission power, transmission rate, and contention window parameters by making comparison with neighboring values through classical logic. However, the approach does not consider points between two close parameter values. This work uses fuzzy logic to improve the adaptation process of the network contention window parameter. The proposed scheme achieved a 15% higher in-packet delivery ratio and 10ms faster transmission compared with related work in terms end-to-end delay.

Fuzzy-Based Distributed Behavioral Control With Wall-Following Strategy for Swarm Navigation in Arbitrary-Shaped Environments

This paper addresses novel fuzzy-based distributed behavioral control with the wall-following strategy for robot swarm navigation in arbitrary-shaped environments. Instead of avoiding large-size obstacles during the swarm navigation, the proposed distributed control enables the robot swarm to follow obstacle boundaries by transforming from aggregation configuration to one-chain configuration. The wall-following strategy and one-chain configuration empower the swarm navigation to avoid local minima caused by obstacles and connectivity maintenance without dealing with the alignment control of swarm behaviours. The fuzzy-logic control is applied to calculate the parameters of the distributed control strategies. The proposed method is examined and evaluated in both simulation and real experiments.

Development of Control Techniques Using Modified Fuzzy Based SAPF for Power Quality Enhancement

Low power distribution systems have severe power quality issues due to the non-linearity of several residential and industrial loads. The main power quality issue is the harmonics leading to the overheating of the transformers in the distribution systems. By employing passive filters, active filters, and custom power devices, the harmonics in the source current can be reduced. To overcome the drawbacks of conventional tuned filters and active power filters the modified shunt active power filter was introduced with the fuzzy logic controller. In this paper, an effective way of reducing the total harmonic distortion using three-phase three-wire shunt active filter is carried out and this has been investigated through three control methods namely synchronous reference frame theory, real and reactive power theory, and indirect reference current theory. The recognized control methods are implemented with the fuzzy controller to improve the performance of the induction motor drive. The hardware setup was implemented for the proposed fuzzy-based control technique to achieve better performance in terms of reduced total harmonic distortion and DC link voltage and improved speed performance of induction motor drive when compared to other control methods. Further power factor correction and better reactive power compensation are achieved by implementing hardware.

An Integrated Fuzzy-based Admission Control System (IFACS) for 5G Wireless Networks: Its implementation and performance evaluation

The Fifth Generation (5G) network is expected to be flexible to satisfy user requirements and the Software-Defined Network (SDN) with Network Slicing is a good approach for admission control. There are different challenges of 5G networks such as: improved spectrum efficiency, reduced latency, low consumption, high data rate, capacity and throughput improvement. To deal with these challenges, we propose an Integrated Fuzzy-based Admission Control System (IFACS) for 5G Wireless Networks. We consider 4 input parameters: Quality of Service (QoS), Slice Priority (SP), User Request Delay Time (URDT) and Slice Overloading Cost (SOC). From simulation results, we conclude that the considered parameters have different effects on the acceptance decision. When QoS, SP, URDT parameters are increased, the AD value is increased. But, when the SOC parameter is increased, the AD value is decreased. When the QoS increases 20%, the AD is increased 20% when SP value is 0.5, SOC value is 0.3 and the URDT value is 0.5. But, when the SOC value changes 40%, the AD is decreased by 20% when the URDT value is 0.5.

Hedge algebras-based admittance controller for safe natural human–robot interaction

ABSTRACT In physical human–robot interaction (pHRI), the identification of inertia and damping matrices in the dynamic admittance model is still an open problem. Besides, the natural interaction is rarely considered in previous studies while it is crucial to obtain the effective cooperation. To this end, a fuzzy-based admittance controller is presented, in which the end-effector's velocity is adaptively adjusted via the external wrench and transmitted power without the identification of inertia and damping matrices. Besides, this fuzzy-based admittance controller also guarantees the natural cooperation between human and robot. Unfortunately, there is no formulated linkage of the fuzzy sets with the natural linguistic term semantics. As a consequence, human experts must utilize the order relationships between the terms of interest when formulating the fuzzy rule-based knowledge of the fuzzy controller. This paper presents an alternative admittance controller for pHRI based on an algebraic approach to linguistic hedges in fuzzy logic to overcome the existing shortcomings of previous admittance controllers. In addition, this paper also considers end-effector's full degree of freedom to guarantee the natural human–robot interaction. The proposed admittance controller is experimentally evaluated on a teaching task set-up using 6-DOF manipulator.