Intelligent PI Research Articles

Intelligent control based on single-input interval type-2 fuzzy iPI and super twisting nonlinear sliding mode compensator for regulating PEMFC output voltage

The control of Proton Exchange Membrane Fuel Cell (PEMFC) is exceptionally challenging due to its complex nonlinearity, slow dynamics and load disturbance. In this paper, an Adaptive Model Free Control (AMFC) approach is introduced for regulating the PEMFC output voltage under load perturbation and measurement noise conditions. The proposed method consists of four sub-elements. Firstly, an Extended State Observer (ESO) based on a Single-Input Interval Type-2 Fuzzy intelligent PI (SI-IT-2-FLC-iPI) scheme is designed to enhance the performance of PEMFC. Secondly, a Nonlinear Sliding Mode Control-based Super Twisting Algorithm (NSMC-STA) is used as an auxiliary control approach to reduce the estimation error and ameliorate the system robustness. Thirdly, a Forgotten Recursive Least Square Error (FRLSE) is applied for adjusting the physical parameter of MFC and further ameliorating the control response. Finally, all these elements are merged together within what it is called Ultra Local Model (ULM).Through extensive simulation, the efficacy of the proposed regulator in accurately tracking the output signal is showcased, outperforming conventional controllers with remarkable improvements. The system achieves 0.001 steady-state error, 0% overshoot and settling time of approximately 8.9 s, which is good enough in the context of standalone fuel cell applications.

Analysis of Distribution Static Compensator Control Strategies for Mitigating Voltage Dip Impact on Distribution Network

Voltage dip, over-voltage, load unbalanced, and current and voltage harmonics distortions are the key characteristics of poor power quality (PQ) issues on the power distribution network with a significant negative impact. The performance of a custom power device, distribution static compensator (D-STATCOM), in reducing current total harmonic distortion (THD) during the mitigation process of voltage dip with fault is investigated in this study. To control the load side voltage, the D-STATCOM utilizes a three-phase voltage source converter and is coupled at the point of common coupling (PCC). To mitigate voltage dip effects, this study implements and compares the effectiveness of the conventional PI controller with an intelligent optimization-based PI controller using the dynamic gravitational search algorithm (DGSA). The performance of these controllers is validated by the MATLAB/Simulink simulation results obtained. Analysis of the results demonstrates that D-STATCOM operates flawlessly with an intelligently optimized PI control strategy reducing the current THD from 11.68% to 3.74%.

An Improved Sensorless Control of Induction Motor Using ADALINE: Theory and Experiment

This article develops a new observer to achieve highly speed estimation for induction motor control. Its principle consists of estimating flux and speed separately, by using linear Luenberger as flux observer and intelligent PI controller as rotor speed estimator. Considering that is generally challenging to established proper PI parameters, the ADALINE algorithm is integrated as a trainable module to automatically calculate the KP and the KI gains in each iteration during all operation induction motor. This algorithm helps to search on the one hand the best regulation of the controller in real-time, taking into account the objectives to achieve (an accurate estimation) and the respect constraints (heavy calculation). On the other hand, it makes it possible to improve the adaptive Luenberger estimation problem in the unobservable zone (low speed). The experimental comparison of the observers demonstrates the visible improvement of the enhanced observer. It can converge to real speed in a short time with less static and transient error.

Design and implementation of a Model-Free Fractional Order Intelligent PI Fractional Order Sliding Mode Controller for water level tank system

This paper presents a combination of three nonlinear control methods, namely Model free Controller (MFC), Fractional-order Proportional Integral Controller (FO-PIC) and Fractional-order Sliding Mode Controller (FO-SMC), which gives rise to the new MFC algorithm with the term MF-FOiPI-FOSMC called Model Free-Fractional Order Intelligent Proportional Integral-Fractional Order Sliding Mode Controller. The stability analysis of the closed loop system (CLS) and the attractiveness of the proposed method are established by Lyapunov theorem analysis. The validation of the MF-FOiPI-FOSMC is first presented by simulation results and then performed by experimental results on the water level tank system. In addition, the effectiveness and performance of the new proposed FOiPI-FOSMC strategy is proved by comparing it to other strategies such as the classical PI controller and intelligent proportional–integral controller (i-PI).

SOFC-Supported Hybrid PSOGSA-Optimized Dynamic Voltage Restorer for Power Quality Enhancement

A novel optimal method of a Dynamic Voltage Restorer (DVR) supported by solid oxide fuel cell (SOFC) and its simplified topological structure are proposed. DVR is a power-electronic converter-based device, and the objective of the DVR control system is to minimize supply voltage variations at the load terminals. This is attained by generating a compensating voltage at the series injection transformer. Conventional controllers are mathematical model-based; also, the particular system varies widely, and nonlinear factors make the PI controller tuning more challenging to some extent. As a result, an intelligent PI optimization control method is essential. This paper proposes Hybrid PSOGSA to search for optimal values of two PI control parameters for the [Formula: see text]–[Formula: see text]-axis components by considering a novel bi-objective function. The performance of the test system is analyzed for five test scenarios using the proposed PI controller with SPSO-optimized and Ziegler–Nichols tuning methods. DVR system provides an excellent control performance in the transient and steady states for compensating the sensitive load voltages with almost zero steady-state errors. Simulation results show that the proposed approach can provide improved performance than PSO-optimized and classical PI controllers for the power quality indices measured.

An Intelligent PI Controller-Based Hybrid Series Active Power Filter for Power Quality Improvement

The quality of power that is degrading day by day is an important issue for all the consumers. The important factor for this is harmonics in the voltage and current waveforms which can be resolved by the use of hybrid series active power filter. The combination consists of a series active power filter and a shunt passive filter connected in parallel to the load. The method used in this paper is for the purpose of achieving good harmonic compensation and reduced total harmonic distortion for various types of nonlinear loads as per the standards of IEEE 519. The proposed HSAPF technique uses the synchronous reference frame method for generating the compensating signal with an intelligent PI controller that uses particle swarm optimization (PSO) technique to obtain the required gain values needed to improve the steady state response of the system. The concept of vigorous HSAPF has been authenticated through MATLAB simulation analysis, and the results obtained validate the accuracy of the method for the different load conditions.

Movement of a Solar Electric Vehicle Controlled by ANN-based DTC in Hot Climate Regions

Vehicle autonomy presents the most complex problem for modern commercialized solar electric vehicle (SEV) propulsion systems. The power supplied by electric vehicles’ batteries is limited by the state of charge, the type of battery, and its level of technological development. This study’s aim was to resolve the problem of energy variation at several velocities and under different road topology conditions. Several works related to the use of fuzzy logic confirm that classical regulators have such advantages over fuzzy regulators as short processing times and mathematical precision. Therefore, the hybrid power source is presented as the best solution for energy management, and it is composed of a solar panel (PV) and a nickel metal hydride battery. The PV system is connected to the SEV via a boost converter that is controlled using the maximum power point tracking technique. In this paper, we used an intelligent PI regulator for direct torque control, which introduced a certain degree of intelligence into the regulation strategy. Indeed, this approach of associating the PI regulator with the fuzzy rules-composed supervisor allowed us to take advantage of both the PI’s mathematical precision and the adaptability, flexibility, and simplicity of fuzzy linguistic formalism. Because of its dynamic capabilities, an adaptive PI regulator was substituted to achieve high speeds and a satisfactorily vigorous performance while quickly compensating for the disturbances that were expected to possibly take place on the regulation chain. The present study’s results confirm that the proposed control approach increased the utility of SEV autonomy under several speed variations. Moreover, the industry’s future offerings must take the option of hybrid power management into consideration during this type of vehicle’s manufacturing phase

An intelligent fuzzy PI controller based 33 level switched capacitor multilevel inverter for PMSM drives

This paper presents an intelligent Fuzzy PI controller based switched capacitor 33 level multilevel inverter for PMSM Drives and the advantages of proposed SCMLI is proved. Compared to this suggested topology, the conventional MLI topology is compared with an indifferent viewpoint, such as the number of switches needed, output voltage levels and power supply. In order to further improve torque ripple, anti-interference capability and speed response, a new fuzzy PI vector speed control system with an adaptable proportion factor familiar by the RBF neural network must be created. The proposed design is implemented in MATLAB/ Simulink and the performance is improved for proposed circuit is achieved with experimental findings.

Design of MIMO QFT fractional control based on intelligent fractional PI<SUP align=right>α</SUP>D<SUP align=right>μ</SUP> controller combined with decentralised and centralised FBLFD pre-filter: application to SCARA robot

The aim of this paper is to propose new robust controls for multivariable parametric uncertain systems and to validate their efficiency in robots path tracking. An automated fractional multivariable Quantitative Feedback Theory is developed. The principle is to obtain desired performances on the basis of controllers and pre filters without using loop shaping process. The proposed approach has benefited from the robustness of fractional control. Indeed, the method is based on a combination of intelligent fractional PIαDμ controller with both diagonal and non-diagonal Frequency Band Limited Fractional Differentiator. A bi-objective optimisation based on genetic algorithm is utilised to find the controller parameters. The developed methodologies are applied to a SCARA robot model and the findings highlight the robustness of the designed controller and the success of the diagonal pre-filter to eliminate loop interactions.

Design of MIMO QFT fractional control based on intelligent fractional PI<SUP align=right>α</SUP>D<SUP align=right>μ</SUP> controller combined with decentralised and centralised FBLFD pre-filter: application to SCARA robot

Design of MIMO QFT fractional control based on intelligent fractional PI<SUP align=right>α</SUP>D<SUP align=right>μ</SUP> controller combined with decentralised and centralised FBLFD pre-filter: application to SCARA robot

Artificial Intelligent Tuning PI Controller on Wind Turbine System with Three-Phase Grid Connected System

In order to generate the electricity from the wind turbine system with a permanent magnet synchronous generator (PMSG) to the three-phase grid connected system, it is very challenging because the electricity generated from wind energy resource is inconstancy. In this paper, the electrical power between the two systems are regulated by PI controller. For tuning the gains of PI controller, artificial intelligent methods, particle swarm optimization (PSO), gravitational search algorithm (GSA), artificial neural networks (ANN), are applied. The experiments of the control systems are testing at 200 w and 400 w. The experimental results show that the rise time of an electrical power of control system at 200 w tuning gains of the PI controller by ANN combined with PSO is better than the traditional tuning methods, pole placement. Therefore, the proposed intelligent system can not only reduce the overshoot of the active power but also improve the response power.

Artificial Intelligent Tuning PI Controller on Wind Turbine System with Three-Phase Grid Connected System

In order to generate the electricity from the wind turbine system with a permanent magnet synchronous generator (PMSG) to the three-phase grid connected system, it is very challenging because the electricity generated from wind energy resource is inconstancy. In this paper, the electrical power between the two systems are regulated by PI controller. For tuning the gains of PI controller, artificial intelligent methods, particle swarm optimization (PSO), gravitational search algorithm (GSA), artificial neural networks (ANN), are applied. The experiments of the control systems are testing at 200 w and 400 w. The experimental results show that the rise time of an electrical power of control system at 200 w tuning gains of the PI controller by ANN combined with PSO is better than the traditional tuning methods, pole placement. Therefore, the proposed intelligent system can not only reduce the overshoot of the active power but also improve the response power.

Design and development of a Stewart platform assisted and navigated transsphenoidal surgery

In this study, technical details of a Stewart platform (SP) based robotic system as an endoscope positioner and holder for endoscopic transsphenoidal surgery are presented. Inverse and forward kinematics, full dynamics, and the Jacobian matrix of the robotic system are derived and simulated in MATLAB/Simulink. The required control structure for the trajectory and position control of the SP is developed and verified by several experiments. The robotic system can be navigated using a six degrees of freedom (DOF) joystick and a haptic device with force feedback. Position and trajectory control of the SP in the joint space is achieved using a new model-free intelligent PI (iPI) controller and it is compared with the classical PID (proportional-integral-derivative) controller. Trajectory tracking experimental results showed that the tracking performance of iPI is better than that of PID and the total RMSE of the trajectory tracking is decreased by 17.64% using the iPI controller. The validity of the robotic system is proven in the endoscopic transsphenoidal surgery performed on a realistic head model in the laboratory and on a cadaver in the Institute of Forensic Medicine. The key feature of the system developed here is to operate the endoscope via the joystick or haptic device with force feedback under iPI control. Usage of this system helps surgeons in long, fatiguing, and complex operations. This system can generate new possibilities for transsphenoidal surgery such as fully automated robotic surgery systems.

Model-free based adaptive nonsingular fast terminal sliding mode control with time-delay estimation for a 12 DOF multi-functional lower limb exoskeleton

The design of a 12 DOF lower limb multi-functional exoskeleton is realized in SolidWorks with the analysis of kinematics. For controller design, this paper presents the model-free based adaptive nonsingular fast terminal sliding mode control which comprises three parts: the intelligent PI controller, time-delay estimation, and adaptive sliding mode compensator. The proposed control strategy drives tracking errors to a nonsingular fast terminal sliding surface and realizes fast convergence in finite time. The designed adaptive law approximates the upper bound of estimation error in time-delay estimation and thus it reduces the fundamental chattering on the switching manifold. The stability of controller is verified by using Lyapunov theory. To validate the proposed method, the virtual prototype is transferred to MATLAB/SimMechanics as a platform for co-simulation. The proposed method shows improved tracking performance compared with the existing model free controller in co-simulations.

Model‐free control of a seeded batch crystallizer

AbstractAs the use of a batch crystallization process in several industrial applications is extensive, finding an effective control strategy is important to improve the product quality, which is typically characterized by a unimodal and narrow crystal size distribution (CSD) with a large mean crystal size. To achieve this requirement, an accurate mathematical model is needed to predict the process comportment and to design an efficient and robust controller. However, due to the highly nonlinear comportment, the difficulties of characterizing several phenomenological effects, the kinetic parameters, uncertainty, and the unknown disturbances, the used model may not describe the real process behaviour, resulting in a poor control strategy. In this work, a model‐free control and its corresponding intelligent PI (iPI), the recently introduced approach, has been proposed to ensure that the desired unimodal CSD with a desired mean size could be reached facing these problems with an easy control structure, choosing the seeded batch crystallizer of adipic acid as a case study. The proposed iPI is compared with the classic PI controller. The simulation results demonstrate the effectiveness and disturbance rejection capability of the iPI controller against the classic PI.

Intelligent robust PI adaptive control strategy for speed control of EV(s)

The intensive non-linear system and plants of modern industry highly motivating researcher to extend and evolve non-linear control systems. In this study, in order to control a class of non-linear uncertain power systems in the presence of large and fast disturbances, a new simple indirect adaptive proportional-integral is proposed. For handling dynamic uncertainties, the proposed controller utilises the advantages of least squares support vectors regression (LS-SVR) to approximate unknown non-linear actions and noisy data. The LS-SVR is used to approximate the non-linear uncertainties which must be bounded, whereas no requirement needs for bounds to be known. The globally asymptotic stability of the closed-loop system is mathematically proved by using Lyapunov synthesis. To show the merits of the proposed approach, a non-linear electric vehicle (EV) system is considered as a case study. The goal is to force the speed of EV to track a desired reference in the present of structured and unstructured uncertainties. The experimental data, new European driving cycle, is used in order to examine the performance of proposed controller. The simulation studies on a second-order EV with the presence of fast against slow and large against small disturbances demonstrate the effectiveness of the proposed control scheme.

Simulation based neuro-fuzzy hybrid intelligent PI control approach in four-area load frequency control of interconnected power system

This paper presents a novel control approach of hybrid neuro-fuzzy (HNF) for load frequency control (LFC) of four-area power system. The advantage of this controller is that it can handle the non-linearities, and at the same time it is faster than other existing controllers. The effectiveness of proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in four area interconnected power system. Area-1 and area-2 consist of thermal reheat power plant whereas area-3 and area-4 consist of hydro power plant. Performance evaluation is carried out by using fuzzy, ANN, ANFIS and conventional PI and PID control approaches. The performances of the controllers are simulated using MATLAB/Simulink package. The result shows that intelligent HNF controller is having improved dynamic response and at the same time faster than ANN, fuzzy and conventional PI and PID controllers.

Designing an Emotional Intelligent Controller for IPFC to Improve the Transient Stability Based on Energy Function

The controllability and stability of power systems can be increased by Flexible AC Transmission Devices (FACTs). One of the FACTs devices is Interline Power-Flow Controller (IPFC) by which the voltage stability, dynamic stability and transient stability of power systems can be improved. In the present paper, the convenient operation and control of IPFC for transient stability improvement are considered. Considering that the system's Lyapunov energy function is a relevant tool to study the stability affair. IPFC energy function optimization has been used in order to access the maximum of transient stability margin. In order to control IPFC, a Brain Emotional Learning Based Intelligent Controller (BELBIC) and PI controller have been used. The utilization of the new controller is based on the emotion-processing mechanism in the brain and is essentially an action selection, which is based on sensory inputs and emotional cues. This intelligent control is based on the limbic system of the mammalian brain. Simulation confirms the ability of BELBIC controller compared with conventional PI controller. The designing results have been studied by the simulation of a single-machine system with infinite bus (SMIB) and another standard 9-buses system (Anderson and Fouad, 1977).

IAPI: An intelligent adaptive PI active queue management scheme

Internet traffic is bursty and unpredictable. It is therefore important to control congestion to protect users from traffic surges in a way that is efficient, stable and robust. Research has shown that active queue management (AQM) has the potential to control congestion such that these requirements are met. However, despite much work and many published proposals of AQM algorithms, a satisfactory solution is still unavailable. In this paper, an intelligent adaptive PI controller (IAPI) is proposed as a new AQM scheme, and its design details are presented. Setting of parameter values for IAPI is provided based on previous studies on PI and additional empirical studies. The performance of IAPI is evaluated by simulations and the results demonstrate that it is stable and robust under various scenarios including cases involving different types of background traffic (UDP and HTTP) and a case involving a multiple bottlenecks. Its superiority over other AQMs is also demonstrated.

The Multiple AC Servomotors’ Synchronous Operation Control System Based on DSP and FPGA

The multi AC servomotor synchronous operation control system based on DSP+FPGA and a new intelligent PI controller have been designed. The intelligent PI controller can change the structure of controller and parameters dynamically according to the running status of the system, which improves the dynamic performance of the speed control system. In terms of the condition without loading, the experiments about four AC servomotors in different revolving speed have been implemented and The results of experiments show that the system can achieve the high precision control of the operation of multiple AC servomotors synchronously.