Omar Hanif

In this paper, a Complex Fractional Order Proportional Integral Derivative (CFOPID) controller is designed for a stable first-order system and complex order system. The increased complexity in tuning of the CFOPID controller with seven parameters needs to be addressed compared to PID with three parameters and Fractional Order PID (FOPID) controller with five parameters. The CFOPID tuning becomes tedious with conventional tuning methods. Hence, Genetic Algorithm (GA) is used for tuning the CFOPID controller with cost function as integral absolute error (IAE). The closed-loop response of the chosen systems with the CFOPID controller is compared against PID and FOPID controllers tuned using GA method. The indicators used to assess the performance are settling time and IAE for servo and regulation. A Bode plot is provided to check the stability.

In this paper, a Novel Complex Fractional Order Proportional Integral Derivative (CFOPID) controller structure is developed of the form PIx+iyDa+ib for fractional order systems. This novel type of controller has more (seven) parameters to tune than the conventional PID and fractional order PID controllers, resulting in a higher degree of control freedom. However, the complexity of CFOPID is due to many tuning parameters, making design and tuning cumbersome. Therefore, this work uses a Genetic algorithm optimization technique with cost function as Integral Absolute Error (IAE). The performance of the proposed CFOPID controller is compared to that of PID and fractional order PID controllers, and then validated on an identified real heat exchanger system. In terms of performance measures such as rise time, settling time, and IAE, the results reveal that the CFOPID controller outperforms integer and fractional order PID controllers for set point and disturbance rejection. Also, the stability is investigated through frequency response using Bode plot.

The interest on five phase induction motor drives is gradually increasing due to its wide variety of applications including electric vehicles, high compressor systems, ship propulsion etc. The designing of a proper controller to make 5-phase induction motor based electric drive is playing a vital role for many usages among many controllers, a direct torque controller (DTC) is a famous technique to achieve motor torque with fewer ripples. Moreover, Takagi Suguno - Fuzzy (TS-Fuzzy) controllers can provide fast response under rapid changes of inputs. Therefore, TS-Fuzzy based DTC of 5-phase induction motor drive is proposed in this paper. Further, the space vector modulation technique is implemented for obtaining proper pulse sequence for 5-phase inverter to drive the motor. In order to achieve smooth control of both torque and flux, two fuzzy logic controllers are deployed on the DTC of the motor. For better illustration, responses of the proposed TS-Fuzzy based DTC of 5-phase induction motor are compared with 3-phase motor. The proposed method is simulated and presented in this paper to analyze the performance of the proposed method under various conditions.

This paper takes up the challenge of modelling a complex nonlinear distillation column type-A and designs four optimal controllers for the same. The nonlinear plant is first linearized into a linear higher-order model. Thereafter, it is reduced into a lower-order model. Subsequently, the controllers are designed for the lower-order linear model. The controllers are designed by reducing the error cost functions, namely Integral Square Error (ISE), Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE). The process of optimization is done heuristically using the Genetic Algorithm method of tuning. The four controllers designed are Proportional Integral Derivative, Fractional-order Proportional Integral Derivative, Tilt Integral Derivative and Fractional-order Internal Model Control. A thorough comparison is made between the three designed controllers, first on the Single Input Single Output (SISO), then to the reduced-order linear model and finally to the main plant. The FO-IMC controller is shown as an exhibition controller designed using GA to demonstrate the novelty of its kind. It has also been compared, but the results show poor performances as it has not been tuned with the same parameters as the rest.

Heat exchangers are utilized in various industrial applications such as power generation, petroleum refinery, coal plants, etc. It is essential to model and control these applications to meet the desired outputs. This paper takes the experimental values of the heat exchanger, uses system identification techniques to model it, and then develops control schemes to control the same. Initially, conventional feedback control is developed. A feedforward algorithm is then added to regulate the external disturbances. Subsequently, the advanced control algorithms such as Internal Model Control and Linear Quadratic Gaussian are implemented. The control starts from developing a simple Feedback control, then adding a feedforward controller to regulate the external disturbances and then advancing to Internal Model Control and finally a Linear Quadratic Gaussian control. The responses, namely: servo responses (reference tracking), regulatory responses (input and output rejections), noise rejection responses, robust responses, and sudden step test responses, are compared for all the mentioned control schemes. Finally, the paper compares the four algorithms and gives an insight into each of their characteristics.

This work introduces a novel variant of controller having basic structure of Proportional Integral Derivative (PID) controller as PI x+iy D a+ib . The controller is termed as Complex Fractional-order Proportional Integral Derivative (CFOPID) controller, since it has orders of fractional and complex form. This controller has more parameters to tune than the other variants of PID controller known as Fractional-order PID (FOPID). The paper employs Genetic Algorithm based tuning method for determining the parameters of PID, FOPID and CFOPID controllers by minimizing the cost function in the form of weighted sum of error specifications (due to complexity of the structures of the latter two controllers, GA proves to be handy tool). The paper, further, simulates and compares the results of the three controllers based on servo, regulatory and stability performances on a standard second order plus time delay system. Henceforth, practical results of controllers are analyzed from a case study on DC servomotor. This research is based on tuning the three PID variants through the said technique and comparing them on their controlling performances.

Nonlinear control study has evolved owing to the complexity of systems in multi-faceted disciplines. The most effective method of dealing with nonlinear systems is through linearization. Another revolution in the field of control is identifying the system with the help of fractional-order differential equations. Later, the fractional-order transfer function is calculated and controlled with the help of fractional-order controllers. This paper is a comprehensive work on a nonlinear system by taking a spherical tank case study. The work models the latter into multi-model integer order transfer functions (IOTF) then converts them into fractional-order transfer functions (FOTFs) via the frequency-domain method. It uses the Kalman filter algorithm to estimate the outputs of the various models of the multi-model bank. It then designs controllers, namely Proportional-Integral-Derivative (PID), Fractional-Order Proportional Integral Derivative (FOPID), and Multi-term Fractional-Order PID (MFOPIDs), using genetic algorithm. Subsequently, the paper thoroughly compares servo, regulatory, and robust responses of the PID controller and its variants.

The trajectory tracking for a non-holonomic mobile robot is addressed in this paper. Cascade control is implemented with a Sliding Mode Controller for the outer loop and a Proportional-Integral-Derivative controller governing the inner loop. Linear Quadratic Regulator is further applied to the outer loop to optimize the controller's effort. The model is further analyzed based on various output performances such as trajectory tracking, reference tracking of linear and angular velocities, sliding surfaces of the controller, and its robustness. A comparative analysis with and without an optimal approach has been illustrated. Simulations are carried out on a virtual robotic simulator in LabVIEW.

Modeling of a DC-DC converters is usually carried via small-signal approximations with small disturbances taken in account at steady state. Although due to presence of harmonic content or DC offset, the latter's modeling is restricted to certain range of operation. However, this is limited to a certain range of operation. This paper is a work based on modeling of Dual Active Bridge (DAB) type converters using Generalized Average Modeling. This type of procedure takes into account the harmonic as well as DC effect of the alternating current. After modeling the DAB, it's open loop transfer function is derived. Thereafter for controlling, parameters of fractional order (FO) controllers namely Fractional order PID and Tilt Integral Derivative (TID) are synthesized using heuristic optimization technique also known as Genetic Algorithm. The use of FO controllers enables more controllability of the plant than classical integer order controllers. The paper then compares the result obtained after simulation of plant controlled by FOPID and TID controllers respectively. It discusses various Power as well as Control results and then concludes the topic by summarizing the procedure and highlighting the importance of FO controllers.

DC-DC converters are generally modeled using small-signal approximations considering small perturbations around a steady-state point. However, this is limited to a certain range of operation which in the presence of harmonic content in addition to dc offset might not provide an accurate result. Generalized Average modeling takes into account the harmonic contents along with the dc values and this can be used for alternating components such as inductor current in a Dual Active Bridge converter. The transfer function is derived which is an open-loop stable system, and in order to establish closed-loop stability, fractional order PI controller is used in this paper. Further, the optimization technique is applied in order to optimize the control variables as the degree of freedom has increased due to the incorporation of fractional powers. These optimized values are used to establish the closed-loop stable operation of a DAB converter and the technique is validated through MATLAB simulations. The entire results are shown in the figures with specifications tabulated and confirm the accurate setpoint tracking with the ripples in range.

In this paper, the author has focussed on the detailed modeling and analysis of a variant of cuk converter i.e. Interleaved Cuk converter (ICC), which along with having the benefits of conventional cuk converter also provide additional advantages of ripple cancellation, increased current and voltage rating thereby high rating applications can be successfully taken advantage of. Feedback control is a part of any stable system and to gain desired response, a progressive optimization algorithm which is the Genetic Algorithm (GA) is used here to tune the control variables; PID control gains and additional freedom of control are present where fractional order PID (FOPID) controller variables λ and μ can be utilized with the traditional PID controller for the desired response. The simulation results are presented for servo and regulatory response, parameters current and voltage results along with the results when variations are subjected to the input voltage as well as the reference voltage. The results confirm the desired results are obtained with the ripples confined to 1% and settling of the output voltage at the setpoint value.

The increasing integration of the solar based system, EV's(Electrical vehicles) with the existing conventional generation has led to required dc-dc converters for interfacing. For improve the system performance the DC-DC converters have to meet many requirements, such as high energy density, less ripple in the system, low EMI(Electromagnetic interference), compact size. Hence, a DC-DC interleaved CUK converter (ICC) is most suitable choice for meeting the requirements. This paper is based on the novel idea of modeling and controlling an interleaved CUK converter. Modeling is carried by linearizing the nonlinear model of the converter via average and small signal modelling and tuning of controller parameters is based on heuristic search technique known as Genetic Algorithm. The results show a better response time (rise time, settling time) and error specification in case of GA tuned controller than without controller. Hence, it can be concluded that this element (converter) coupled controller can give the desired results as demanded by the energy device.

Proportional Integral Derivative (PID) controller has been used in feedback mechanism since time immemorial. This work discusses about another hybrid of PID controller of the form PI x+i yD a+ib also termed as Complex Fractional order PID controller. This variant of PID controller has excess of tuning parameters, more than any of the PID and its other fractional order variant (also known as Fractional order PID controller). Henceforth, this research realizes CFOPID controller and synthesizes all the variants of PID controller's parameter utilizing Genetic Algorithm (starting from primitive Proportional controller to advanced CFOPID controller) by minimizing the weighted sum of error specifications. It additionally relates the responses of each of the controllers and concludes the superior variant of PID controller.

The advancement of science is responsible for the implementation of new technologies from time to time. Fractional order controllers are thus, a result of advancement in the field of controllers characterized by fractional orders. This paper takes a DC-DC Cuk converter; identifies the differential equations governing the system.The system is found to be nonlinear in dynamics, hence it is linearized using average modelling and the small signal analysis is done. The study further designs classical Proportional Integral Derivative (PID) controller and its variants: fractional order controllers i.e. Tilt Integral Derivative (TID) and Fractional Order Proportional Integral Derivative (FOPID) controllers through the evolutionary method known as Genetic Algorithm by minimizing the cost function based on the error specifications. The work further simulates and compares the results so obtained when closed loop system is controlled by the pre-said controllers. A detailed analysis of the plant parameters and output response subjected to constant and time varying input; servo response; regulatory response; and relative stability is carried out.

This paper uses a non-isolated DC-DC Cuk converter, models it into a fourth order differential equation. It further concentrates on identifying the transfer function of the DC-DC Cuk converter. The non-isolated model generates a nonlinear dynamics in its characteristics which is linearized by using the average modelling and small signal analysis is done. Thereafter, the work focuses on designing a Fractional order Proportional Integral Derivative (FOPID) controller using the Genetic Algorithm (GA) minimizing the weighted sum of the error specifications i.e. Integral Time Absolute Error (ITAE), Integral Absolute Error (IAE) and Integral Square Error (ISE). The FOPID controller has more parameters giving the controller more degrees of freedom and accuracy than the classical Proportional Integral Derivative Controller (PID). Henceforth, the work implements the designed controller to the model and records various simulation results like step response, current across inductor, voltage across capacitor, response to a disturbance etc., along with setpoint tracking, disturbance rejection and relative stability analysis.

Proportional Integral Derivative order controller has been used in feedback mechanism since time immemorial. The paper introduces a novel form of proportional integral derivative (PID) controller termed as complex fractional order proportional integral derivative (CFOPID) controller of the form PI x+iy D a+ib . This variant of PID controller has more number of parameters than any of the PID and its derivatives. This work realizes a complex natured PID controller and exhibits the tuning of all the variants of PID controller utilizing Genetic Algorithm (starting from primitive Proportional controller to advanced CFOPID controller). It additionally relates the responses of each of the controllers and concludes the superior variant of PID controller.

DC motors have been employed in domestic and industrial works owing to their tendency of developing a constant torque over wide speed applications. A typical Dc motor has characterizing parameters like inertia of the rotor, friction damping coefficient, winding resistances and inductances. The objective of a controller is to maintain the same speed with a step change in the excitation. Conventionally, Proportional Integral Derivative (PID) controller is widely used in controlling the speed of DC motor. With the use of fractional order calculus in synthesizing fractional order PID (FOPID) controller the quality of the controller and the output is increased. This paper synthesizes a new form of FOPID controller namely complex fractional order PID (CFOPID) through Genetic Algorithm.

The paper introduces a new form of complex fractional order proportional integral derivative (CFOPID) controller of the form D. This latest derivative of has more parameters than any of the Fractional-order PID controller or conventional integer order controller. The paper demonstrates the tuning of this complex fractional order controller using Genetic Algorithm method of tuning. It further relates the results and responses got in the latter tuning method with interior point optimization tuning method (fmincon) of another research paper.