Multiobjective Optimization of Multiloop Fractional Order PID Controller Tuned Using Bat Algorithm for Two Interacting Conical Tank Process

This paper presents the identification, control and robustness analysis for a robotic system of 1 degree of freedom using fractional and integer PID controllers. Initially, an ADAMS-MATLAB co-simulation model is built to simulate the dynamic model of robotic system. The identification of the dynamic model parameters of the system employs the recursive least-squares method. The controller design uses a linearized model of the robotic system obtained by the method of input-output linearization, and then, the fractional PID and integer PID controllers are designed based on it. The robustness of the PID controllers is analyzed in the frequency and the time domain. The robustness analysis in the frequency domain uses The Gang of Four (sensibility, complementary sensibility, sensibility of the plant and sensibility of the controller effort). The time domain analysis uses the ADAMS-MATLAB co-simulation model, considering external disturbances, noise in the feedback loop, parametric uncertainty and set-point variation, using as performance criteria ISE, average value and standard deviation of the control action. The tests are performed for positioning and tracking tasks. The obtained results show that the fractional PID controller has a better behavior in presence of external disturbances and lower energy consumption.

This paper addresses a control frequency scheme of the microgrid system using a fractional order PID controller. The proposed Microgrid system is consisted of a Photovoltaic System, Wind Turbine Generator, Diesel Engine Generator, Fuel Cell, and different storage systems like Battery Energy Storage Systems, and Flywheel Energy Storage Systems. The principal objective of the present paper is to limit the frequency and power deviations by the application of the proposed controller which has five parameters to be determined through optimization techniques. Krill Herd algorithm is used for determining the optimum fractional order PID controller parameters using the Integral of Squared Error. A comparison between the Genetic Algorithm and Krill Herd is done, and the obtained simulation results presents that the investigated controller-based Krill Herd outperforms the Genetic Algorithm in terms of fewer fluctuations in power and frequency deviation.

This paper proposes a novel fractional order PID control scheme for Multi Input and Multi Output (MIMO) power systems. This control scheme utilizes Cuckoo Search algorithm to tune the fractional PID controller to guarantee better closed loop performance in the transmission or distribution networks. Cuckoo Search optimization algorithm is proposed to optimize gain values of the fractional PID controller. The effectiveness of the proposed control strategy has been assessed by simulations in MATLAB/Simulink platform. The robustness of the proposed controller has been validated by varying the plant parameters.

This paper proposes a novel method to graphically compute all feasible gain and phase margin specifications-oriented robust and optimal fractional PID controllers to stabilize time delay processes (TD) of any order. The gain-phase margin tester method is utilized to guarantee the concerned systems certain robust safety margins and test the constant gain margin boundary, constant phase margin boundary, and stability boundary in the parameter plane. The overlapping region of these plotted boundaries, which represents all feasible robust stabilizing PID controllers and allows the considered TD processes to retain pre-specified safety margins, is graphically determined as the gain and phase margin specifications-oriented region (GPMSOR). The variations of the GPMSOR and the stability region with respect to the variations of the noninteger orders of the integral and derivative terms have been investigated. According this, it is possible to slightly enlarge the GPMSOR by altering the noninterger order of the integral and derivative terms. This modifications in GPMSOR through altering the noninteger orders of the integral and derivative terms allows us to find the robust and optimal PID controller gain set for the considered TD systems. In other words, the selected fractional order PID controllers from the GPMSOR not only retain the prescribed GM and PM specifications but minimize the integral of the absolute error (IAE) or the integral of the squared error (ISE) performance criterion. Finally, an illustrative example cited from the literature with computer simulations is provided to demonstrate the effectiveness and confirm the validity of the proposed methodology.

The cooperative control of both electrode remelting rate and position was applied in order to obtain optimal electro-slag remelting metallurgy effect and improve melting production efficiency. The nonlinear dynamic model of electro-slag remelting process was linearized. And the lineariztion model was analyzed including its stability, coupling property and variable matching strategy. The multi-loop PID controllers with cascade PID controllers were applied to the linearization model based on matching strategy. The dynamic performance was analyzed to electro-slag remelting control system. Study results show that linearizition model of electro-slag remelting process have characteristic of stability and weakly-coupled property. Multi-loop PID control system can realize stable control both electrode melting rate and electrode position. The control system has faster dynamic response and better anti-interference capability.

This paper emblems the application of fractional order PID (FOPID) controller into a single phase islanded microgrid having a single power source to control the fluctuations in its output voltage. The proffered controller has adaptability in devise as the controller provides more parameters than integer Order PID controller (IOPID)to tune it. The proffered controller is devised using Nelder-Mead optimization technique. Use of the optimization technique gives increased performance of the system. The controller is applied into the system under uncertainties and different load settings. After assessment of performance, it is observed that the application of the proffered controller can cut down the voltage fluctuations of the system and afford fast response with robust performance.

The rapid advancement of electric consumer demand enforces challenging situations to manage high penetration of Solar PV and Wind Power into the hydrothermal system resulting in load frequency control (LFC) problem. This article introduces an advanced strategy to manage the LFC issue in an integrated power system (PS) with the impact of hydrogen aqua equalizer (HAE)-based Fuel cells (FC). A transformative fractional-order fuzzy-based PID (FOFPID) controller regulated by the Sunflower optimizing (SFO) technique has been proposed for frequency regulation of the proposed integrated PS. The preeminence of the FOFPID controller has been validated over PID, Fuzzy-PID (FPID), and fractional order PID (FOPID) controller. The effectiveness of the SFO technique has been established over teaching learning-based optimization (TLBO) and Genetic Algorithm (GA). The supremacy of the SFO approach authenticated by some recent frequency regulation research outcomes. The influence of HAE-FC for frequency regulation in an integrated PS has been studied with the proposed SFO-tuned FOFPID controller. From the observation, it is perceived that the values of reduction in ITAE with suggested SFO-tuned FOFPID controller as compared to GA & TLBO are 84.61% & 56.04% and further 35.16% is reduced with the occurrence of HAE-FC element. It is also perceived that the improvement in settling time for ∆f1, ∆f2, ∆Ptie12 with SFO approach related to GA are 78.07%, 76%, and 83.87% and compared to TLBO are 9.97%, 38.25%, and 68.23%. Furthermore, 26.45%, 18.64%, and 31.21% are reduced with the presence of HAE-FC unit. Lastly, the sturdiness of the anticipated strategy was authenticated by a sensitivity test and a real-time validation test using OPAL-RT.

Using the Caputo-Fabrizio operator, the influence of substitution in the two-mass system linear model an integer derivatives on fractional derivatives is shown. Parameters changing of fractional PID controller in comparison with classical PID controller is analyzed. The influence on the system transient characteristics with the fractional order PID controller using is demonstrated in this paper.

Level control is one of the most used processes in industries. However, it can present nonlinearities, which can make difficult its project. The PID controller is still a commonly used topology due to the non-necessity to know the full system dynamics, only the modelling that well describes the system behavior. The objective of this work is to identify, control and audit a level tank system from a SMAR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> didactic plant. Firstly, system identification techniques (Smith, Bröida, Vitečková and Artificial Neural Network) were used to perform the controllers tuning later, approaching it to a First Order plus Dead Time transfer function (FODT). To tuning PI/PID controllers, optimization methods were used, such as Bat Algorithm, Bacterial Foraging Optimization, Genetic Algorithm, Bee Swarm, Bat Algorithm, Ant Colony Optimization, and Shuffled Frog-Leaping. Beyond optimization methods, analytical/classical PI/PID controllers tuning techniques (Cohen-Coon, Hallman, Internal Model Control (IMC), Chien-Hrones-Reswick (CHR), and Integral of Absolute Error (ITAE)) were also introduced to do this parameterization. In order to compare the simulated and experimental results, non-intrusive performance indexes based on integral errors (IAE, ISE, ITAE and ITSE) were introduced to evaluate and choose the best performance. The results were interesting, showing that the classical identification technique Bröida had the best response. For PID controllers tuning, optimization algorithms overcame analytical/classical tuning techniques performance, proving a better execution, performed by Shuffled Frog-Leaping technique.

In this paper, a self-tuning hierarchical controller in which a Fuzzy logic controller supervises the control actions of a conventional PID has been proposed, implemented and presented. The controller has been applied to a control study of Fluid Catalytic Cracking (FCC) unit riser temperature, and regenerator temperature respectively. Performance comparison of the proposed Fuzzy-PID controller and the conventional PID was made in simulation studies of regulatory and servo performances of the two controller types. Six performance measures: Percent overshoot (OS), settling time (ST), integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE) were employed as the tools for performance comparison between the conventional PID and the Fuzzy-PID controller. For the tracking of riser temperature with a set point at 524oC, the performance indicators under PID control gave the following results overshoot (14.5%); settling time (40 seconds) Integral absolute error (8.246), integral square error (3.3); integral time absolute error(1762);integral time square error (43.77) while for the same indicators under Fuzzy-PID control the following values: overshoot (3.3%); settling time (40 seconds) ;Integral absolute error (8.811); integral square error (14.5); integral time absolute error(280),;integral time square error (31.11) .The results allude to the superiority of the fuzzy-PID scheme over the PID scheme in tracking the optimal set point of riser temperature. More so, for tracking the regenerator set point temperature of 746oC, comparative study of step response under the two schemes gave the following results in six performance indicators: overshoot (PID(12.6%)/Fuzzy-PID(6%)); settling time (PID(80 seconds)/Fuzzy-PID(20seconds));Integral absolute error(PID(14.29)/Fuzzy-PID(8.63)); integral square error(PID(6.713).Fuzzy-PID(4.506)); integral time absolute error(PID(2858)/Fuzzy-PID(305.9)), integral time square error (PID(77.55)/Fuzzy-PID(33.05)).Moreover, the fuzzy-PID controller also showed superior performance over the conventional PID controller in terms disturbance rejection (regulatory response) of both riser and regenerator temperature. The results from this study suggest that the application of fuzzy-PID scheme to temperature control offers good promise of improved fluid catalytic cracking unit (FCCU) operations.

Design of a Multi-loop Self-Tuning PID Controller based on the augmented output and its application.

The air feeding system is one of the most important systems in the proton exchange membrane fuel cell (PEMFC) stack, which has a great impact on the stack performance. The main control objective is to design an optimal controller for the air feeding system to regulate oxygen excess at the required level to prevent oxygen starvation and obtain the maximum net power output from the PEMFC stack at different disturbance conditions. This paper proposes a fractional order fuzzy PID controller as an efficient controller for the PEMFC air feed system. The proposed controller was then employed to achieve maximum power point tracking for the PEMFC stack. The proposed controller was optimized using the neural network algorithm (NNA), which is a new metaheuristic optimization algorithm inspired by the structure and operations of the artificial neural networks (ANNs). This paper is the first application of the fractional order fuzzy PID controller to the PEMFC air feed system. The NNA algorithm was also applied for the first time for the optimization of the controllers tested in this paper. Simulation results showed the effectiveness of the proposed controller by improving the transient response providing a better set point tracking and disturbance rejection with better time domain performance indices. Sensitivity analyses were carried-out to test the robustness of the proposed controller under different uncertainty conditions. Simulation results showed that the proposed controller had good robustness against parameter uncertainty in the system.

An analytical approach for tuning fractional PID controllers based on the frequency response of the system is addressed in this paper. Fractional PID controller inherits all advantages of conventional PID controllers and also has additional degrees of freedom. Based on their frequency response a new method of tuning is proposed in this paper, which is inspired from the previous works associated with this topic. Then the same method is validated by applying for attitude control of Reusable launch vehicle. The robustness study of the whole system is also carried out.

The problem of restricted-structure benchmarking for multi-input, multi-output (MIMO) systems controlled by multi-loop PID controllers is formulated in state space. The particular criterion shown in this paper is that for a linear quadratic generalised predictive controller, which combines the properties of linear quadratic Gaussian with those of generalized predictive control. (5 pages)

By adding an electronic throttle and a torque sensor to an engine, it is potentially possible to improve emissions and fuel economy while preserving the torque response of a conventional engine. To do so effectively, however, requires the use of proper control strategy for electronic throttle. In this paper, a kind of fractional order fuzzy PID controller is proposed and it is applied to an electronic throttle. An efficient way to tune fractional order fuzzy PID controller parameters is proposed using a fruit fly optimization algorithm (FOA), which treats the controller parameters tuning as an optimization problem with a proper fitness function. The obtained simulation results show the effective performance of the proposed method.