Mamdani Controller Research Articles

Optimized fuzzy type 1 Mamdani controller to enhance transient stability of AC-power system

Optimized fuzzy type 1 Mamdani controller to enhance transient stability of AC-power system

Synchronization and Optimal Operation of a 140 kVA Inverter in On-Grid Mode Using Mamdani Controllers in Cascade

This paper addresses the synchronization and operation of a 140 kVA inverter system connected to the main grid as part of a decentralized microgeneration system. The considerations for the supply of electrical energy stored in battery banks, mostly of photovoltaic origin, involve a study of the details of a rigid nonlinear system, which parallels the generation and distribution standards typical of hydroelectric and thermoelectric plants. Considering aspects related to power electronics operation, this paper presents both the modeling and the controlling aspects necessary to synchronize and ensure a stable operation of the microgeneration systems when connected to the main grid. Statistical processing was developed to guarantee synchronization between the systems without presenting electric shocks by simulating the magnetic link in asynchronous generators to meet this aim. The proposed model simulates the increase in power by a phase shift by maintaining a constant frequency based on a Chirp wave generator. The proposed process considers a generation power baseband operation. A Mamdani-type fuzzy proportional-integral controller is used to determine the power setpoint, which sets the Chirp generator phase shift setpoint, which includes a Mamdani fuzzy proportional-type controller. Both controllers are connected in a cascade. The applied correlational technique to achieve the synthesis of the sinusoid and the synchronization presented optimal performance when using 17 samples per signal period. The design of the transformer primarily, guaranteed a phase shift of −4.3018°, allowed for a THD below 2.75%.

Optimizing the wash time of the washing machine using several types of fuzzy numbers

In this paper we used various fuzzy numbers to calculate wash time for washing machines and provided various decisions after comparison among them. Again, this paper shows the importance of fuzzy logic control-based washing machine to get an appropriate wash time for the degree of dirt and the quantity of grease present on the clothes. This method is predicated on a fuzzy inference system. A fuzzy inference system using the Mamdani controller type is illustrated in this paper. From the utilization of fuzzy logic control, the machine can respond in several conditions. The simulation was done by MATLAB programming language.
 J. Bangladesh Acad. Sci. 45(1); 105-116: June 2021

Measuring Current in a Power Converter Using Fuzzy Automatic Gain Control

The accuracy of current measurements can be increased by appropriate amplification of the signal to within the measurement range. Accurate current measurement is important for energy monitoring and in power converter control systems. Resistance and inductive current transducers are used to measure the major current in AC/DC power converters. The output value of the current transducer depends on the load motor, and changes across the whole measurement range. Modern current measurement circuits are equipped with operational amplifiers with constant or programmable gain. These circuits are not able to measure small input currents with high resolution. This article proposes a precise loop gain system that can be implemented with various algorithms. Computer analysis of various automatic gain control (AGC) systems proved the effectiveness of the Mamdani controller, which was implemented in an MCU (microprocessor). The proposed fuzzy controller continuously determines the value of the conversion factor. The system also enables high resolution measurements of the current emitted from small electric loads (≥1 A) when the electric motor is stationary.

Diver tracking in open waters: A low‐cost approach based on visual and acoustic sensor fusion

AbstractThe design of a robust perception method is a substantial component towards achieving underwater human–robot collaboration. However, in complex environments such as the oceans, perception is still a challenging issue. Data‐fusion of different sensing modalities can improve perception in dynamic and unstructured ocean environments. This study addresses the control of a highly maneuverable autonomous underwater vehicle for diver tracking based on visual and acoustic signals data fusion measured by low‐cost sensors. The underwater vehicle U‐CAT tracks a diver using a 3‐degree‐of‐freedom fuzzy logic Mamdani controller. The proposed tracking approach was validated through open water real‐time experiments. Combining acoustic and visual signals for underwater target tracking provides several advantages compared to previously done related research. The obtained results suggest that the proposed solution ensures effective detection and tracking in poor visibility operating conditions.

Effects of Increasing the Footprints of Uncertainty on Analytical Structure of the Classes of Interval Type-2 Mamdani and TS Fuzzy Controllers

The fundamental difference between an interval type-2 (IT2) fuzzy controller and a type-1 fuzzy controller is the footprint of uncertainty (FOU) of an IT2 fuzzy set. In this paper, we study how FOUs affect the analytical structure (i.e., the input–output mathematical relationship) of a broad class of IT2 Mamdani and takagi-sugeno (TS) controllers. The controllers employ arbitrary fuzzy rules, the Karnik–Mendel (KM) or Enhanced KM type-reducer, the minimum AND operator, and the centroid defuzzifier. The controllers utilize commonly used IT2 fuzzy sets for their input variables and any kind of type-2 fuzzy sets for their output variable. We prove that, with increase of FOUs of the input fuzzy sets, the Mamdani controllers approach constant controllers, and the TS controllers approach piecewise linear controllers. The resemblance to the constant or pricewise linear controllers increases as the FOUs increase. When all the FOUs are at their maximum (to reflect the highest level of uncertainties), the Mamdani and TS controllers become the constant controllers and piecewise linear controllers, respectively. We investigate how change in the resemblance takes place progressively as FOUs increase. We also show that an increase in the resemblance narrows control gain variations for part of the IT2 controllers, which can worsen control performance. These findings implicit controller design—too large FOUs are generally undesirable for the input fuzzy sets because they can make an IT2 controller behave like a constant or piecewise linear controller. Real-time control experiment results are provided to illustrate the theoretical analysis.

Fuzzy Rule-Based and Particle Swarm Optimisation MPPT Techniques for a Fuel Cell Stack

The negative environmental impact and the rapidly declining reserve of fossil fuel-based energy sources for electricity generation is a big challenge to finding sustainable alternatives. This scenario is complicated by the ever-increasing world population growth demanding a higher standard of living. A fuel cell system is able to generate electricity and water with higher energy efficiency while producing near-zero emissions. A common fuel cell stack displays a nonlinear power characteristic as a result of internal limitations and operating parameters such as temperature, hydrogen and oxygen partial pressures and humidity levels, leading to a reduced overall system performance. It is therefore important to extract as much power as possible from the stack, thus hindering excessive fuel use. This study considers and compares two Maximum Power Point Tracking (MPPT) approaches; one based on the Mamdani Fuzzy Inference System and the other on the Particle Swarm Optimisation (PSO) algorithm to maintain the output power of a fuel cell stack extremely close to its maximum. To ensure that, the power converter interfaced to the fuel cell unit must be able to continuously self-modify its parameters, hence changing its voltage and current depending upon the Maximum Power Point position. While various methods exist for Maximum Power Point tracker design, this paper analyses the response characteristics of a Mamdani Fuzzy Inference Engine and the Particle Swarm Optimisation technique. The investigation was conducted on a 53 kW Proton Exchange Membrane Fuel Cell interfaced to a DC-to-DC boost converter supplying 1.2 kV from a 625 V input DC voltage. The modelling was accomplished using a Matlab/Simulink environment. The results showed that the MPPT controller based on the PSO algorithm presented better tracking efficiency as compared to the Mamdani controller. Furthermore, the rise time of the PSO controller was slightly shorter than the Mamdani controller and the overshoot of the PSO controller was 2% lower than that of the Mamdani controller.

Fuzzy adaptive control system of a non-stationary plant with closed-loop passive identifier

Typically chemical processes have significant nonlinear dynamics, but despite this, industry is conventionally still using PID-based regulatory control systems. Moreover, process units are interconnected, in terms of inlet and outlet material/energy flows, to other neighboring units, thus their dynamic behavior is strongly influenced by these connections and, as a consequence, conventional control systems performance often proves to be poor. This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller, also exploiting the results coming from an identification procedure that is carried on when an unmeasured step disturbance of any shape affects the process behavior. In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective.

FUZZY CONTROL OF DIFFERENTIAL DRIVE MOBILE ROBOT FOR MOVING TARGET TRACKING

Tracking of moving objects, including humans has important role in mobile robotics. In this paper, the hierarchical control structure for target/human tracking consisted of high and low level control was presented. The low level subsystem deals with the control of the linear and angular velocities using multivariable PD controller whose parameters are obtained by Particle swarm optimization. The position control of the mobile robot represents the high level control, where we use two fuzzy logic Mamdani controllers for distance and angle control. In order to test the effectiveness of the proposed control scheme a simulation was performed. Two cases, when the mobile robot pursues a target moving along a circular path and when the mobile robot pursues a target moving along a rectangle path, were simulated.

ARITHMETIC-BASED FUZZY CONTROL

Fuzzy control is one of the most important parts of fuzzy theory for which several approaches exist. Mamdani uses $alpha$-cuts and builds the union of the membership functions which is called the aggregated consequence function. The resulting function is the starting point of the defuzzification process. In this article, we define a more natural way to calculate the aggregated consequence function via arithmetical operators. Defuzzification is the optimum value of the resultant membership function. The left and right hand sides of the membership function will be handled separately. Here, we present a new ABFC (Arithmetic Based Fuzzy Control) algorithm based on arithmetic operations which use a new defuzzification approach. The solution is much smoother, more accurate, and much faster than the classical Mamdani controller.

СИСТЕМА ПОИСКА И АНАЛИЗА ДОСТОВЕРНОЙ ИНФОРМАЦИИ В СЕТИ ИНТЕРНЕТ

Now in the world a problem of reliable information retrieval in the Internet network is especially critical. It is defined by increased data capacity and by the absence of control over data placement. It is offered to determine information reliability by methods of fuzzy logic at the correct formation of retrieval request. For the estimate of completeness, truth and integrity of data obtained it is offered to rely upon the analysis of information from other pages of a site found. In accordance with this the notions of topical closeness of site pages and reliability of information presented in the remaining sections. To organize an output are compiled the rules for the integral reliability output of a site page. For example, if information on a page does not coincide with analyzed one and is reliable then one can suppose that related information is reliable. Further, with the use of Mam-dani controller is carried out a defuzzification. A technology for a retrieval request organization is offered to increase an information content retrieval. The technologies offered for retrieval and analysis of information allow increasing effectiveness and a retrieval rate of reliable, integrated and true information in the Internet network.

Passive Identification is Non Stationary Objects With Closed Loop Control

Typically chemical processes have significant nonlinear dynamics, but despite this, industry is conventionally still using PID-based regulatory control systems. Moreover, process units are interconnected, in terms of inlet and outlet material/energy flows, to other neighbouring units, thus their dynamic behaviour is strongly influenced by these connections and, as a consequence, conventional control systems performance often proves to be poor. However, there a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller, also exploiting the results coming from an identification procedure that is carried on when an unmeasured step disturbance of any shape affects the process behaviour. This paper presents procedure for identifying technological object control in a closed loop, i. e. that operates the automated control system. The variation in the controlled variable, caused by the change of the nonmeasurable disturbance, is considered the initial signal for the identification procedure. The parameters of the control object are found by optimization method Levenberg-Marquardt.

Fuzzy Adaptive Control System of a Non-Stationary Plant

This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller. In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective.

Análisis de estabilidad de controladores borrosos tipo Mamdani mediante el cálculo del exponente de Lyapunov

In order to design and implement any type of controller, their stability analysis is pivotal. At this regard, Lyapunov's analytical method consists in finding a candidate function as a sufficient but not necessary condition to validate the stability of the controller. In the case of fuzzy controllers such a candidate function is not always found, leading to an uncertainty about their stability. To overcome this problem, we propose to employ the Lyapunov Exponent in order to determine whether fuzzy controllers are stable. The Lyapunov exponent is calculated through a numerical method on the time series obtained experimentally by having the fuzzy controller in closed loop with the plant dynamics. In this paper, the plant is the inverted pendulum, which is a benchmark plant to test complex control laws. Sixteen experiments were carried by modifying the rule base structure of Mamdani fuzzy controllers, which were also tested under normal and disturbed conditions. In all the cases, the Lyapunov Exponent is negative, indicating that the analyzed Mamdani controllers are indeed dissipative systems. Future applications on adaptive control are presented because the Lyapunov serves as a quantitative metric to determine controllers’ performance.

Fuzzy Control Technique Applied to Modified Mathematical Model for Malaria Control

In this paper, fuzzy control technique is applied to the modified mathematical model for malaria control presented by the authors in an earlier study. Five Mamdani fuzzy controllers are constructed to control the input (some epidemiological parameters) to the malaria model simulated by 9 fully nonlinear ordinary differential equations (ODE) simulator of the Modified Ngwa and Shu’s Model. Each of the controllers uses two different and unique variable parameters to calculate commanded control inputs to the simulator. The input domains are divided into 3 membership functions resulting in 7 fuzzy rules for each rule base and the output domains are partitioned with 5 membership functions. The Mamdani controllers use a standard max-min inference process and a fast centre of area method to calculate the crisp control signals. Numerical results obtained using Matlab 2008a software guarantee that malaria infection can be reduced to a tolerable limit. http://dx.doi.org/10.4314/njt.v34i4.21

Fuzzy adaptive control system of a non-stationary plant with closed-loop passive identifier

Abstract Typically chemical processes have significant nonlinear dynamics, but despite this, industry is conventionally still using PID-based regulatory control systems. Moreover, process units are interconnected, in terms of inlet and outlet material/energy flows, to other neighboring units, thus their dynamic behavior is strongly influenced by these connections and, as a consequence, conventional control systems performance often proves to be poor. This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller, also exploiting the results coming from an identification procedure that is carried on when an unmeasured step disturbance of any shape affects the process behavior. In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective.

Artificial hydrocarbon networks fuzzy inference systems for CNC machines position controller

This paper proposes a novel position controller for computer numerical control (CNC) machines based on a hybrid fuzzy inference system that uses artificial hydrocarbon networks in its defuzzification step, so-called fuzzy-molecular inference system. The fuzzy-molecular-based position controller is characterized to improve the accuracy in position and the time machining. In order to prove these characteristics, a case study was run over a reconfigurable micromachine tool (RmMT) assembly in lathe configuration. In addition, a workpiece machining in the RmMT assembly serves to realize a comparative analysis between the proposed controller and three other controllers: a classical PID controller manually tuned, a PID controller auto-tuned, and a fuzzy Mamdani controller. Experimental results validate the performance and the implementability of the proposed fuzzy-molecular position controller against the others.

A novel fuzzy-based expert system for RET selection

The aim of this work is to demonstrate a novel fuzzy-based expert system for selecting renewable energy technologies RET. Fuzzy multi-rules and fuzzy multi-sets are used to evaluate the main operational characteristics of six types of RET fuelled by biogas from municipal solid waste MSW landfills. The construction of the fuzzy multi-rules and fuzzy multi-sets is based on the following method: Mamdani controller using the Max-Min inference process and Center of Gravity defuzzification process. Several criteria are used for the investigation: costs, efficiency, cogeneration, life-cycle and environmental impacts. The fuzzy-based expert system considers three different settings with two different constraints: costs and environmental impacts. One of the most relevant aspects presented by this work is about the previous criteria rank. It was created according to the different relevance observed among the attributes. The purpose of the proposed arrangement is to facilitate the understanding of the methodology and to increase the possibility of incorporating the decision makers' preferences on the decision-aid process. These aspects are essential to strengthen the final decision.

Fuzzy Control Design for a DC-DC Buck Converter based on Recursive Least Square Algorithm

In this paper a novel fuzzy control is proposed for a DC-DC Buck Converter based on recursive least square method. First, a conventional DC-DC Buck converter model by state space equations is simulated and waveforms of the converter are presented in its classical control condition. Then, a fuzzy MAMDANI controller is added to the system to improve the performance of the system. The fuzzy control parameters are adjusted by a recursive least square method based on the error between the voltage outputs and the desired values. Simulation results show that fuzzy controller acts much better than classical control. The proposed control scheme can normalize the output voltage and decrease its ripple. Furthermore, by this fuzzy controller, reaching to the desired output voltage is fulfilled.

Application of the Methods of Complexation of Analogs and Fuzzy Logic for Prediction of Some Operation Metrics

We suggest usage of the methods of complexation of analogs, the Mamdani controller for analysis and prediction of metrics for estimation of activity of staff of software maintenance department. We developed software, which realizes this technique as well as the method of exponential smoothing, which is used for comparison of productivity and quality of prediction. The conducted experiments showed high efficiency of the methods. Recommendations for methods usage for prediction of different samplings are adduced.