A Substractive Clustering Based Fuzzy Hybrid Reference Control Design for Transient Response Improvement of PID Controller

The well known PID controller has inherent limitations in fulfilling simultaneously the conflicting control design objectives. Parameters of the tuned PID controller should trade off the requirement of tracking set-point performances, disturbance rejection and stability robustness. Combination of hybrid reference control (HRC) with PID controller results in the transient response performances can be independently achieved without deteriorating the disturbance rejection properties and the stability robustness requirement. This paper proposes a fuzzy based HRC where the membership functions of the fuzzy logic system are obtained by using a substractive clustering technique. The proposed method guarantees the transient response performances satisfaction while preserving the stability robustness of the closed loop system controlled by the PID controller with effective and systematic procedures in designing the fuzzy hybrid reference control system

A Substractive Clustering Based Fuzzy Hybrid Reference Control Design for Transient Response Improvement of PID Controller

The  well  known  PID  controller  has  inherent  limitations  in  fulfilling simultaneously the conflicting control design objectives.  Parameters of the tuned PID  controller  should  trade  off  the  requirement  of  tracking  set-point performances,  disturbance  rejection  and  stability  robustness.  Combination  of hybrid  reference  control  (HRC)  with  PID  controller  results  in  the  transient response  performances can be independently achieved without deteriorating the disturbance  rejection  properties  and  the  stability  robustness  requirement.  This paper proposes a fuzzy based HRC where the membership functions of the fuzzy logic  system  are  obtained  by  using  a  substractive  clustering  technique.  The proposed  method  guarantees  the  transient  response  performances  satisfaction while preserving the stability robustness of the closed loop system controlled by the  PID  controller  with  effective  and  systematic  procedures  in  designing  the fuzzy hybrid reference control system

Fuzzy Logic Control System Stability Analysis Based on Lyapunov’s Direct Method

A stability analysis method for nonlinear processes controlled by Takagi- Sugeno (T-S) fuzzy logic controllers (FLCs) is proposed. The stability analysis of these fuzzy logic control systems is done in terms of Lyapunov’s direct method. The stability theorem presented here ensures sufficient conditions for the stability of the fuzzy logic control systems. The theorem enables the formulation of a new stability analysis algorithm that offers sufficient stability conditions for nonlinear processes controlled by a class of T-S FLCs. In addition, the paper includes an illustrative example that describes one application of this algorithm in the design of a stable fuzzy logic control system

Review on decomposed fuzzy PID structure for power inverters regulation

The aim of this paper is to critically review prominent decomposed Fuzzy PID control structures. Structural construction and output control laws of these controllers will be discussed. Their merits and drawbacks are highlighted. Based on the critical discussions, a new structure of Fuzzy PID controller is proposed. It is based on cascaded structure, which yields simpler design flow and parameters tuning. Other advantages of the proposed Fuzzy PID structure are the reduction of tuning parameters and rules of the Fuzzy controller. In addition, the proposed structure allows the usage of signed distance method. The application of the method reduces the computation burden significantly as the power inverter regulation needs very fast and precise computation

Performance comparison of optimal fractional order hybrid fuzzy PID controllers for handling oscillatory fractional order processes with dead time

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Fuzzy logic based PID controllers have been studied in this paper, considering several combinations of hybrid controllers by grouping the proportional, integral and derivative actions with fuzzy inferencing in different forms. Fractional order (FO) rate of error signal and FO integral of control signal have been used in the design of a family of decomposed hybrid FO fuzzy PID controllers. The input and output scaling factors (SF) along with the integro-differential operators are tuned with real coded genetic algorithm (GA) to produce optimum closed loop performance by simultaneous consideration of the control loop error index and the control signal. Three different classes of fractional order oscillatory processes with various levels of relative dominance between time constant and time delay have been used to test the comparative merits of the proposed family of hybrid fractional order fuzzy PID controllers. Performance comparison of the different FO fuzzy PID controller structures has been done in terms of optimal set-point tracking, load disturbance rejection and minimal variation of manipulated variable or smaller actuator requirement etc. In addition, multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) has been used to study the Pareto optimal trade-offs between the set point tracking and control signal, and the set point tracking and load disturbance performance for each of the controller structure to handle the three different types of processes

Detecting financial sustainability risk of the assets using MAMDANI fuzzy controller

The paper aims to develop a MAMDANI fuzzy controller for detecting the financial sustainability risk of the assets owned by the company. This type of risk indicates when an asset no longer produces economic benefits to the company, or the benefits are small enough to no longer justify the asset maintaining in working order. The proposed fuzzy controller has as input variables the asset operating expenses and the variation of this category of expenses from one analysis period to another. The controller's objective function is to keep operating costs at their initial state and thus reducing the financial sustainability risk. The controller's output variable is represented by the economic benefits variation, considered to be an essential component in the financial sustainability risk analysis. The obtained results were interpreted taking into account the objective function of the controller as well as the evolution of the input variables. Two simulations for fuzzy controllers were made, with the mention that the variation ranges for the input variables were delimited. In practice, fuzzy controllers can be generated according to company policies to keep under control the expense categories that accompany the asset exploitation

Rancang Bangun Sistem Kontrol Kecepatan Motor BLDC Menggunakan ANFIS Untuk Aplikasi Sepeda Motor Listrik

Sistem kontrol kecepatan motor BLDC menggunakan ANFIS telah didesain dan diimplementasikan. Algoritma ANFIS mampu mengkontrol kecepatan motor BLDC sesuai dengan nilai refrensi yang diinginkan. Rata-rata error steady state yang dicapai dengan menggunakan ANFIS adalah sebesar 0,1 % dengan rise time sebesar 2,7437 s untuk kecepatan referensi sebesar 4000 rpm. Proses pembelajaran ANFIS menggunakan metode hybrid PSO dan RLSE dengan supervisi dari Fuzzy-PID. PSO dan RLSE dapat mentraining data ANFIS multi output dengan sangat baik. Data training terbaik dicapai saat nilai λ=1 dengan error RMSE sebesar 0,05364. Waktu eksekusi algoritma ANFIS pada mikrokontroler adalah sebesar 96 us. ====================================================================================================== BLDC motor speed control system using ANFIS has been designed and implemented. ANFIS algorithm is able to control the speed of the BLDC motor according to the desired reference value. The average of steady state error achieved using ANFIS is 0.1% and the rise time is 2.7437 s when the reference speed is 4000 rpm. ANFIS learning process uses hybrid PSO and RLSE methods supervised by Fuzzy-PID. PSO and RLSE can train the ANFIS multi-output data very well. The best training data is achieved when the value of λ = 1 with RMSE error of 0.05364. The execution time of ANFIS algorithm on microcontroller is 96 us

Development and implementation of fuzzy controller in biodiesel production

Orientador: Flavio Vasconcelos da SilvaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuimicaResumo: A crescente utilização do biodiesel como combustível renovável e, principalmente, as situações de operação transiente, de característica complexa e não-linear, motivaram a implementação de um controlador digital avançado neste processo. O controlador avançado foi implementado em computador através de um sistema de comunicação digital, visando manter a temperatura da mistura reacional em 50 °C. A temperatura é uma variável importante deste processo devido à influência direta na taxa de conversão do óleo em biodiesel. Óleo de soja foi utilizado como fonte de ácidos graxos, além do uso do álcool etílico anidro e ácido sulfúrico como reagentes. A reação química foi acompanhada durante 1 hora de batelada visando assegurar uma boa conversão do óleo (acima de 90%). Neste trabalho, além do controlador avançado também foi implementado o controlador PID. Os parâmetros de sintonia do controlador PID foram obtidos em malha aberta e auxiliaram a sintonia do controlador fuzzy. Durante a etapa de sintonia fina do controlador fuzzy foram alteradas as funções de pertinência, o universo de discurso e a base de regras. A implementação do controlador fuzzy apresentou-se como uma ferramenta apropriada para o controle da temperatura reacional devido às complexidades advindas das variações dos parâmetros deste processo. A análise comparativa do desempenho dos controladores fuzzy e PID aplicados à produção de biodiesel comprovaram isso. O uso do controlador fuzzy reduziu o consumo de energia elétrica durante a batelada em 10% quando comparado ao parâmetro obtido para o controlador PID e também reduziu o tempo necessário para atingir a máxima conversão do óleo em biodiesel em 15 minAbstract: The increasing use of biodiesel as a renewable fuel, and mainly because of their nonlinearity and time varying properties this work aimed the implementation and the development of an automation system based in fuzzy logic. In this work, the fuzzy controller is applied in the maintenance of the temperature of bulk at 50?C, using digital system. The control of the temperature in this process is important to guarantee the final quality of biodiesel. Soybean oil was used like source of fatty acids, ethanol and sulfuric acid were used like reagents. This reaction occured during an hour to can achieved high conversion (above 90%). The PID controller tuning parameters were obtained via open-loop experiments. The tuning the fuzzy controller can be achieved by modifying the rules, the discurse universe and the pertinence functions. Due the complexity and the nonlinearity of this reaction, the results of this study showed the effectiveness of fuzzy controller. The fuzzy controller reduced the energy consumption (10% smaller) and the batch time (15 min smaller) when compared to a PID controllerDoutoradoSistemas de Processos Quimicos e InformaticaDoutor em Engenharia Químic