Control Strategy for Anaesthetic Drug Dosage with Interaction Among Human Physiological Organs Using Optimal Fractional Order PID Controller

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.In this paper, an efficient control strategy for physiological interaction based anaesthetic drug infusion model is explored using the fractional order (FO) proportional integral derivative (PID) controllers. The dynamic model is composed of several human organs by considering the brain response to the anaesthetic drug as output and the drug infusion rate as the control input. Particle Swarm Optimisation (PSO) is employed to obtain the optimal set of parameters for PID/FOPID controller structures. With the proposed FOPID control scheme much less amount of drug-infusion system can be designed to attain a specific anaesthetic target and also shows high robustness for +/-50% parametric uncertainty in the patient's brain model

Fractional Order Fuzzy Control of Hybrid Power System with Renewable Generation Using Chaotic PSO

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants

Kriging based Surrogate Modeling for Fractional Order Control of Microgrids

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.This paper investigates the use of fractional order (FO) controllers for a microgrid. The microgrid employs various autonomous generation systems like wind turbine generator (WTG), solar photovoltaic (PV), diesel energy generator (DEG) and fuel-cells (FC). Other storage devices like the battery energy storage system (BESS) and the flywheel energy storage system (FESS) are also present in the power network. An FO control strategy is employed and the FO-PID controller parameters are tuned with a global optimization algorithm to meet system performance specifications. A kriging based surrogate modeling technique is employed to alleviate the issue of expensive objective function evaluation for the optimization based controller tuning. Numerical simulations are reported to prove the validity of the proposed methods. The results for both the FO and the integer order (IO) controllers are compared with standard evolutionary optimization techniques and the relative merits and demerits of the kriging based surrogate modeling are discussed. This kind of optimization technique is not only limited to this specific case of microgrid control but also can be ported to other computationally expensive power system optimization problems

Fractional Order AGC for Distributed Energy Resources Using Robust Optimization

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.The applicability of fractional order (FO) automatic generation control (AGC) for power system frequency oscillation damping is investigated in this paper, employing distributed energy generation. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell and aqua electrolyzer along with other energy storage devices like the battery and flywheel. The controller is placed in a remote location while receiving and sending signals over an unreliable communication network with stochastic delay. The controller parameters are tuned using robust optimization techniques employing different variants of Particle Swarm Optimization (PSO) and are compared with the corresponding optimal solutions. An archival based strategy is used for reducing the number of function evaluations for the robust optimization methods. The solutions obtained through the robust optimization are able to handle higher variation in the controller gains and orders without significant decrease in the system performance. This is desirable from the FO controller implementation point of view, as the design is able to accommodate variations in the system parameter which may result due to the approximation of FO operators, using different realization methods and order of accuracy. Also a comparison is made between the FO and the integer order (IO) controllers to highlight the merits and demerits of each scheme

Fractional Order State Feedback Control for Improved Lateral Stability of Semi-Autonomous Commercial Heavy Vehicles

With the growing development of autonomous and semi-autonomous large commercial heavy vehicles, the lateral stability control of articulated vehicles have caught the attention of researchers recently. Active vehicle front steering (AFS) can enhance the handling performance and stability of articulated vehicles for an emergency highway maneuver scenario. However, with large vehicles such tractor-trailers, the system becomes more complex to control and there is an increased occurrence of instabilities. This research investigates a new control scheme based on fractional calculus as a technique that ensures lateral stability of articulated large heavy vehicles during evasive highway maneuvering scenarios. The control method is first implemented to a passenger vehicle model with 2-axles based on the well-known “bicycle model”. The model is then extended and applied onto larger three-axle commercial heavy vehicles in platooning operations. To validate the proposed new control algorithm, the system is linearized and a fractional order PI state feedback control is developed based on the linearized model. Then using Matlab/Simulink, the developed fractional-order linear controller is implemented onto the non-linear tractor-trailer dynamic model. The tractor-trailer system is modeled based on the conventional integer-order techniques and then a non-integer linear controller is developed to control the system. Overall, results confirm that the proposed controller improves the lateral stability of a tractor-trailer response time by 20% as compared to a professional truck driver during an evasive highway maneuvering scenario. In addition, the effects of variable truck cargo loading and longitudinal speed are evaluated to confirm the robustness of the new control method under a variety of potential operating conditions

Metodologia baseada em algoritmos evolutivos para otimização de controladores de ordem fracionária

Orientador: Gustavo Henrique da Costa OliveiraCoorientador: Gideon Villar LeandroTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 06/12/2022Inclui referências: p. 159-171Área de concentração: Engenharia ElétricaResumo: Nos últimos anos, o cálculo de ordem fracionária ganhou muita atenção, especialmente no campo da teoria de sistemas dinâmicos e do projeto de sistemas de controle. Algoritmos de controle com ordem fracionária permitem expandir a quantidade de parâmetros de projeto visando melhorar o desempenho do sistema em malha fechada. No entanto, os graus de liberdade são acompanhados com uma complexidade na síntese. Dentro dessa perspectiva, encontra-se o controlador PID de ordem fracionária (FOPID), que possui as ordens integral e diferencial ajustáveis, criando a possibilidade de fornecer melhor desempenho de controle, desde que corretamente sintonizado. Da mesma forma, a sintonia do controlador CRONE e suas gerações também é um desafio, onde a escolha incorreta dos parâmetros pode comprometer o desempenho do controlador. Em vista disso, este trabalho apresenta três objetivos principais, sendo o primeiro uma nova estratégia híbrida de controle, chamada AFOPID. Nesta estratégia, os cinco parâmetros do FOPID são sintonizados online de forma que, na ocorrência de alguma perturbação, a Lógica Fuzzy atualiza os coeficientes kp, ki e kd do FOPID para adaptar a malha fechada à nova condição de operação. Em seguida, os coeficientes fracionários (lambda) e µ, que são as ordens integral e diferencial do controlador, são atualizados usando um algoritmo de Evolução Diferencial (DE). Para fins de validação da metodologia proposta, uma planta de uma usina hidrelétrica baseada em um sistema real é utilizada. Através dos resultados, percebeu-se que o sistema híbrido melhorou a solução geral, fornecendo melhor desempenho em malha fechada do que soluções semelhantes, o que pôde ser comprovado através da análise dos índices de desempenho ISE, ITAE e ITSE. O segundo objetivo desta tese consiste na proposta de um algoritmo de otimização multiobjetivo para os controladores CRONE gerações 1 e 2. Para tanto, utiliza-se o algoritmo NSGA-II Multiobjetivo baseado em dois objetivos principais: (i) Minimizar o sinal de controle; (ii) Reduzir o erro em regime permanente. Os resultados mostraram que além de facilitar o processo de escolha dos parâmetros, não dependendo tanto do conhecimento do projetista, o controlador otimizado conseguiu fornecer bons níveis de desempenho, ou seja, minimizou o sinal de controle e reduziu o erro em regime permanente. Por fim, como terceiro objetivo deste trabalho, tem-se o desenvolvimento de uma plataforma computacional chamada UFPR-FracControl. A plataforma contém os controladores CRONE 1 e 2, convencional e otimizado, FOPID, PID convencional e, visa a utilização desses controladores por usuários não especialistas. Os resultados demonstraram que esta nova plataforma facilitará o uso de sistemas de controle de ordem fracionária pelo fato de ser leve, não depender de instalação, não depender de licenças e pelo fato de ser de fácil implementação. Por fim, conclui-se que os três objetivos aqui propostos obtiveram sucesso em melhorar o desempenho e facilitar o uso dos controladores de ordem fracionária.Abstract: In recent years, fractional order calculus has gained a lot of attention, especially in the field of dynamical system theory and control system design. Control algorithms with fractional order allow expanding the number of design parameters to improve the performance of the closed-loop system. However, the degrees of freedom are accompanied by complexity in the synthesis. Within this perspective, there is the fractional order PID controller (FOPID), which has adjustable integral and differential orders, creating the possibility of providing better control performance, as long as it is correctly tuned. Likewise, the tuning of the CRONE controller and its generations is also a challenge, where the incorrect choice of parameters can compromise the performance of the controller. Given this, this work presents three main objectives, the first being a new hybrid control strategy, called AFOPID. In this strategy, the five parameters of the FOPID are tuned online so that, in the event of any disturbance, the Logic Fuzzy updates the coefficients kp, ki and kd of the FOPID to adapt the closed loop to the new operating condition. Next, the fractional coefficients (lambda) and µ, which are the integral and differential orders of the controller, are updated using a Differential Evolution (DE) algorithm. To validate the proposed methodology, a plant of a hydroelectric plant based on a real system is used. Through the results, it was noticed that the hybrid system improved the overall solution, providing better closed-loop performance than similar solutions, which could be proven through the analysis of the ISE, ITAE, and ITSE performance indexes. The second objective of this thesis consists in proposing a multiobjective optimization algorithm for CRONE controllers generations 1 and 2. For this purpose, the NSGA-II Multiobjective algorithm is used based on two main objectives: (i) Minimize the control signal; (ii) Reduce the steady-state error. The results showed that in addition to facilitating the process of choosing the parameters, not depending so much on the designer's knowledge, the optimized controller was able to provide good levels of performance, that is, it minimized the control signal and reduced the steady-state error. Finally, as the third objective of this work, there is the development of a computational platform called UFPR-FracControl. The platform contains CRONE 1 and 2 controllers, conventional and optimized, FOPID, and conventional PID, and aims to use these controllers by non-specialist users. The results showed that this new platform will facilitate the use of fractional order control systems because it is lightweight, does not depend on installation, does not depend on licenses, and because it is easy to implement. Finally, it is concluded that the three objectives proposed here were successful in improving performance and facilitating the use of fractional order controllers