Traditional Proportional Integral Controller Research Articles

Advanced Direct Vector Control Method for Optimizing the Operation of a Double-Powered Induction Generator-Based Dual-Rotor Wind Turbine System

The main goal of this paper is to increase the active/reactive power extracted from variable-speed dual-rotor wind power (DRWP) based on doubly-fed induction generators (DFIG) by optimizing its operation using advanced direct vector control. First, the dynamic modeling of different parts of the system is introduced. The DFIG is modeled in the Park reference system. After that, the control techniques are introduced in detail. Direct vector command (DVC) with four-level fuzzy pulse width modulation (FPWM) is used to control the rotor current, thereby controlling the reactive power and active power of the generator. Then, use the neural network design to replace the traditional proportional-integral (PI) controller. Finally, the Matlab/Simulink software is used for simulation to prove the effectiveness of the command strategy using 1.5 MW DRWP. The results show good performance in terms of response time, stability, and precision in following the reference under variable wind speed conditions. In addition, the total harmonic distortion (THD) value of stator current is about 0.13%, being a bit less than other THD values reported in the literature.

Finite control set model predictive torque control using sliding model control for induction motors

Finite control set model predictive torque control (FCS-MPTC) has become increasingly prevalent for induction motors (IM) owing to its simple concept, easy incorporation of constraints and strong flexibility. In traditional FCS-MPTC speed controller design, a classical proportional integral (PI) controller is typically chosen to generate the torque reference. However, the PI controller is dependent on system parameters and sensitive to the load torque variation, which seriously affects control performance. In this paper, a model predictive torque control using sliding mode control (MPTC + SMC) for IM is proposed to enhance the robust performance of the drive system. First, the influence of the parameter mismatches for FCS-MPTC is analyzed. Second, the shortcomings of traditional PI controller are derived. Then, the proposed MPTC + SMC method is designed, and the MPTC + PI and MPTC + SMC are compared theoretically. Finally, experimental results demonstrate the correctness and effectiveness of the proposed MPTC + SMC. In comparison with MPTC + PI, MPTC + SMC has the better dynamic performance and stronger robust performance against parameter variations and load disturbance.

Control of arc suppression devices in compensated power distribution systems using an integral sliding mode controller for mitigating powerline bushfires

An integral sliding model controller (I-SMC) is proposed in the work for arc suppression devices (ASDs) in resonant grounded power distribution systems which generally experience single line-to-ground faults and arcs from such faults are responsible for power line bushfires in remote areas.The proposed I-SMC is designed for the residual current compensation (RCC) inverter that is used with ADSs and the current is injected in a way that the risk of powerline bushfires are completely eliminated by reducing the fault current. In this work, a T-type inverter is employed and the control law is derived by considering the model uncertainties while the reference value of the current injected by the RCC inverter is calculated based on the fundamental analysis of ASDs. The sliding surface for the switching control input is derived in such a way that the system becomes stable. Finally, simulation studies are carried out to demonstrate the performance of the proposed scheme in terms of the desired tracking of the fault current and phase to neutral voltage with different fault impedances. Furthermore, the performance is compared with traditional proportional-integral (PI) controller in the time-domain to demonstrate its high performance and robustness.

Objective holographic feedbacks linearization control for boost converter with constant power load

As a typical load, the constant power load (CPL) is different from the resistive load and has destabilizing negative impedance characteristics, influencing the system stability. The boost converter with the output voltage as the feedback control variable is a non-minimum phase nonlinear system because its linear approximation has a right-half-plane (RHP) pole. The non– minimum phase system limits the application of control techniques such as input–output linearization (IOL). This paper presents an objective holographic feedbacks linearization control strategy (OHFLC) for DC power systems with constant power loads, bypassing non-minimum phase systems to stabilize the system. The analysis of the proposed method’s pole-zero points indicates that it is based on placing the pole-zero points of the system on their corresponding desired positions through feedback so as to achieve satisfactory dynamic and static performance of the control system. Finally, simulation and experimental verification demonstrate that the presented control approach provides superior dynamic response and robustness than the traditional proportional-integral (PI) control.

Controller Design of an Active Front-End Converter Keeping in Consideration Grid Dynamic Interaction

This paper presents a systematic control design procedure for an active front-end (AFE) converter connected to a power electronics embedded grid. A parametric model of the grid is firstly identified using the AFE itself to excite the system. This model is then integrated with AFE one in order to obtain a global system representation. Subsequently, a globally optimized local controller is synthesised for the AFE keeping in consideration interaction between the converter and the grid. The proposed method is finally tested experimentally and its performance is compared with a traditional Proportional-Integral (PI) controller in a dq synchronous reference frame.

Adjustable Model Predictive Control for IPMSM Drives Based on Online Stator Inductance Identification

To suppress the disturbance caused by the parameter mismatch, this article proposes an adjustable model predictive control (AMPC) strategy for an interior permanent-magnet synchronous motor (IPMSM) drives. The key of the proposed method is that an improved model reference adaptive system (IMRAS) is applied to identify online parameters. The traditional proportional-integral (PI) controller in the adaptive law of IMRAS is replaced by a fuzzy logic controller, it can improve the identification accuracy and enhance the robustness of sudden variations in speed reference and load torque. Then, the identified parameters can be used in the AMPC to replace constant parameters. Compared with the traditional model predictive control, the AMPC strategy can effectively decrease motor system current distortion and torque ripple when parameter mismatch occurs. Moreover, the maximum torque per ampere control (MTPA) control strategy based on the formula solution is adopted. And the optimal MTPA operation points can be found automatically by online parameter identification to achieve the minimum amplitudes of the phase current. Finally, the validity and correctness of the proposed method are verified by experiments.

Improved PR Control Strategy for an LCL Three-Phase Grid-Connected Inverter Based on Active Damping

Aiming at the problem of power coupling and complicated decoupling in the d-q coordinate system of a three-phase grid-connected inverter, a current closed-loop control strategy based on an improved QPIR (quasi-proportional integral resonant) controller in the α-β two-phase static coordinate system is proposed. Firstly, the mathematical model of an LCL three-phase grid-connected inverter is established, and its instantaneous power calculation equation is deduced. Secondly, the frequency method is applied to compare and analyze the proportional resonant, quasi-proportional resonant, and improved current controller, and the appropriate improved controller parameters are obtained according to the traditional proportional integral controller parameter design method and the weight coefficient. Finally, the improved controller is compared with the traditional controller in the simulation model of the LCL three-phase grid-connected inverter based on active damping. The results show that the proposed improved current control strategy has good dynamic response characteristics, can realize the non-static error control of grid-connected current, and realizes the decoupling control of active power and reactive power when the load jumps. At the same time, the results also prove the superiority of the proposed control strategy and verify its effectiveness.

Development of Double Closed-loop Vector Control Using Model Predictive Control for Permanent Magnet Synchronous Motor

Vector control has been widely used to manipulate the permanent magnet synchronous motor (PMSM), in which the traditional double proportional integral (PI) controllers have commonly been utilized. However, it is difficult to set the suitable parameters of PI and maintain the PMSM performance under the different operating regimes. In order to solve these problems, a double closed-loop model predictive control (MPC) strategy based on the vector control has been presented for PMSM in this paper. Firstly, a simplified discrete model of PMSM is established in the d-q coordinate system without considering the interference. Then, the MPC is developed to substitute the PI controller in the speed-loop and the q-axis current-loop, respectively, and a double closed-loop MPC control system is formed. Finally, some case studies are carried out. The simulation and experimental results demonstrate that the presented MPC strategy improves the dynamic and steady-state performance of PMSM.

Controlling of Induction Motor Using Grey Wolf Optimization Algorithm

This paper introduces the improved Direct Torque Control (DTC) of the Induction Motor (IM) to enhance its performance. The DTC method is generally used to control electrical machinery. The traditional Proportional Integral (PI) speed controller parameters are manually adjusted to match the speed with the given reference. However, traditional PI has a drawback such that it does not fit all error limits. Thus, the intelligent DTC has been discussed with the proposed Grey Wolf (GWO). The grey wolf algorithm is used to adjust PI speed controller parameters as well the results are compared with those of genetic algorithm. Simulation results show that the GWO algorithm is exceptionally successful for improving induction motor with a fast-tracking response.

Performance analysis of boost converter with cascaded inner loop fuzzy SMC

PurposeThis paper intent to design, develop, and fabricate a robust cascaded controller based on the dual loop concept i.e. Fuzzy Sliding Mode concept in the inner loop and traditional Proportional Integral controller in the outer loop to reduce the unknown dynamics and disturbances that occur in the DC-DC Converter.Design/methodology/approachThe proposed Fuzzy sliding mode approach combines the merits of both SMC and Fuzzy logic control. FSMC approach reduces the chattering phenomena that commonly occurs in the sliding mode control and speed up the response of the controller.FindingsIn most of the research work, the inner current loop of cascaded controller was designed by sliding mode control. In this paper FSMC is proposed and its efficacy is confirmed with SMC -PI. In most uncertainties, FSMC-PI produces null maximum peak overshoot and a very less settling time of 0.0005 sec.Originality/valueThe presence of Fuzzy SMC in the inner loop ensure satisfactory response against all uncertainties such as steady state, circuit parameter variations and sudden line and load disturbances.

Reactive power and harmonic compensation in a grid-connected photovoltaic system using fuzzy logic controller

Abstract This work investigates the performance of a single-phase grid-connected photovoltaic (PV) system under non-ideal source voltage conditions using a fuzzy logic controller. The single-phase instantaneous reactive power theory has been modified to explore the harmonic and reactive power compensation with distorted grid voltage excitation while transferring active and reactive power to the grid and the non-linear load. Instead of a traditional Proportional-Integral (PI) controller, the proposed method implements a fuzzy logic controller for the extraction of the reference compensating current. This method is compared with the traditional single-phase instantaneous reactive power theory. The advantage associated with a fuzzy logic controller is that it does not need accurate mathematical modeling of the system, instead, it is based upon the linguistic characterization of the input and output variables. Besides, care has been taken to provide uninterrupted compensation throughout the variation of the solar irradiation level. The proposed method has also been verified through a laboratory-developed prototype using a DS1103 processor.

Design of Inverter Side of Modular Grid Simulator Based on Proportional integral-Quasi-proportional resonance Control

The tracking of a given voltage in the traditional double closed-loop proportional integral control in the current power grid simulator has problems such as static difference, delay and oscillation. It is proposed that the voltage outer loop and current inner loop of the inverter side of the power grid simulator adopt proportional integral and quasi-proportional resonance control respectively, and the topology of the inverter adopts a cascaded modular design to establish a single-phase inverter model. Compared with the traditional double closed-loop proportional-integral control, it is verified that the proportional-integral-quasi-proportional resonant controller can effectively improve the system’s ability to track the command voltage and the stability of the output voltage.

A Novel Hybrid Control Strategy for the Energy Storage Modular Multilevel Converters

In this paper, a novel hybrid control strategy has been proposed for the energy storage modular multilevel converters (ESMMC) to realize a nice current performance and ensure the state of change (SOC) of batteries to be balanced. Firstly, traditional proportional integral (PI) control was used to get the circulating current reference; then an improved model predictive control (MPC) method with the weighting factors being online tuned has been designed to obtain the accessing numbers of the submodules in each phase. Lastly, the conventional sorting method for the SOC balancing was given. Experimental comparisons indicate that this proposed novel control strategy can not only ensure a nice current control performance, but also effectively suppress the circulating current component, and it can save over 39.42% execution time in contrast with the traditional PI method, which is feasible for the digital implementation.

The extended model predictive-sliding mode control of three-level AC/DC power converters with output voltage and load resistance variations

In the proposed approach, an Extended Model Predictive Sliding Mode Controller (EMPSMC) is designed to control Three-Level Ac/Dc Power Converters to achieve improved dynamic performance and better. Steady-state stability. The traditional proportional-integral (PI) controller is used in the Model Predictive PI controller (MPPIC) technique to produce active power reference. However, this technique results in a significant overshoot/undershoot and steady-state error. Instead of PI, sliding mode control (SMC) is used to overcome these shortcomings. The performances of EMPSMC and MPPIC are compared and analyzed with and without disturbance. The results show that the system’s settling time is minimized by introducing SMC, and the overshoot is also reduced. Moreover, it also provided better steady-state stability. Similarly, dynamic improvements are achieved with EMPSMC for tracking the desired dc reference voltage demand. The simulation results validate the performance of the designed model.

Critical Quality Predictive Control of Fed-Batch Mammalian Cell Bioreactors

Abstract A model predictive control (MPC) formulation for a mammalian cell fed-batch bioreactor processes is developed. A nonlinear fundamental model for the bioreactor is used to generate a database of historical runs comprising of the measurement variables and the manipulated input feed flow rate to the bioreactor. The database is used with subspace identification methods to develop a state-space model of the process. The identified model is used to design various MPC formulations with different objective criteria, including the conventional trajectory-tracking objective function and a novel terminal objective for maximizing the product yield at completion of a run. Case studies involving the simulated bioreactor process demonstrate the efficacy of the MPC algorithms subject to unknown disturbances, random variations in the inlet feed glucose and glutamine concentrations, and measurement noise. Compared to the traditional proportional-integral control algorithm, the trajectory-tracking predictive control algorithm is able to better track the reference glucose concentration set-point with an improvement of 5.1% in the tracking error. The critical quality attribute predictive control algorithm designed to maximize the product yield results in a 3.9% increase in the product concentration at the completion of the run.

Current harmonic suppression for permanent magnet synchronous motor based on phase compensation resonant controller

Permanent magnet synchronous motor always suffers from air gap field distortion and inverter nonlinearity, which lead to the harmonic components in motor currents. A resonant controller is a remarkable control method to eliminate periodic disturbance, whereas the conventional resonant controller is limited by narrow bandwidth and phase lag. This article presents a novel resonant controller with a precise phase compensation method for a permanent magnet synchronous motor to suppress the current harmonics. Based on the analysis of the current harmonic characteristics, the proposed resonant controller for rejecting a set of selected current harmonic components is plugged in the current loop, and it is parallel to the traditional proportional–integral controller. Furthermore, the stability analysis of the proposed resonant controller is investigated, and the parameters are tuned to get a satisfactory performance. Compared with the conventional resonant controller, the proposed resonant controller can achieve good steady-state performance, dynamic performance, and frequency adaptivity performance, simultaneously. Finally, the experimental results demonstrate the effectiveness of the proposed suppression scheme.

Energy-Saving of Battery Electric Vehicle Powertrain and Efficiency Improvement during Different Standard Driving Cycles

This article focuses on the energy-saving of each driving distance for battery electric vehicle (BEV) applications, by developing a more effective energy management strategy (EMS), under different driving cycles. Fuzzy logic control (FLC) is suggested to control the power management unit (PMU) for the battery management system (BMS) for BEV applications. The adaptive neural fuzzy inference system (ANFIS) is a modeling technique that is mainly based on data. Membership functions and FLC rules can be improved by simply training the ANFIS with real driving cycle data gathered from the MATLAB/SIMULINK program. Then, FLC console blocks are rewritten by enhanced membership functions by ANFIS traineeship. Two different driving cycles are chosen to check the improvement in the efficiency of this proposed system. The suggested control system is validated by simulation and comparison with the traditional proportional-integral (PI) control. The optimized FLC shows better energy-saving.

Natural Genetics Adapted Control for an Autonomous Wind-Battery Based Microgrid

Autonomous microgrids supply power to large remote areas where access to the grid is infeasible. The generation of these microgrids is highly dominated by renewable energy resources (RESs) equipped with a storage battery. Due to the uncertainty associated with RESs, the sustainability and reliability of supply become the prime areas of focus. The battery reduces the uncertainty during demand rise, power deficiency, and overloading with their faster response than diesel generators and microturbines. In this article, the power generation of the isolated microgrid is considered from the wind energy source along with a battery. For the favorable speed regulation of the wind turbine driven generator, the traditional proportional integral (PI) controller is replaced by natural genetics [genetic algorithm (GA)] and evolution inspired PI controller. The issues identified by the use of a fixed gain PI controller such as unsuitable regulation due to incorrect tuning and discontinuous tuning are addressed by the use of a GA tuned speed controller. The performance of the PI controller degrades while dealing with the ac quantities, so in terms of dealing with sinusoidal signals, here an adaptive proportional resonant controller with a combined generalized integrator based phase locked loop is utilized. It tracks the reference ac voltage and provides frequency adaptation in the presence of harmonics pollution with zero steady state error. The batteries deployed through a bidirectional converter, act as the residential storage for backing up the power firming renewable production. The maximum power point (MPP) extraction of the wind generator is carried by a perturb and observe based MPP technique. The performance improvement obtained from the laboratory developed test setup verifies the microgrid effectiveness under renewable generation variability and load alterations.

Research on brushless DC motor control system based on fuzzy parameter adaptive PI algorithm

As a complex system with multiple variables, nonlinearity, and strong coupling, the BLDCM (Brushless Direct Current Motor) has many problems, such as bad parameter tuning, poor adaptability, low control accuracy, and weak anti-interference ability by using the double closed loop traditional PI (Proportional Integral) control algorithm. In order to obtain good control performance, a fuzzy parameter adaptive PI algorithm based on speed loop was designed by combining fuzzy control with traditional PI control. This paper analyzes the mathematical model and operating characteristics of the BLDCM and designs a fuzzy system that takes the deviation e and deviation change rate ec of the reference speed and feedback speed as input and takes the corresponding PI adjustment parameters as output. The step response of the BLDCM at different reference speeds is analyzed. The variable speed response with the initial speed of 4000 r/min under different control algorithms and the changes in the three-phase current, back electromotive force, and electromagnetic torque in this state are compared. The results show that the designed fuzzy parameter adaptive PI algorithm based on the speed loop can make the motor have a faster response time, a smaller overshoot, and a steady-state error when the motor achieves the stable operation. The proposed algorithm also has better control effect, robustness, and stable operation under variable speed conditions.

A Generalized Interpretation of Three Types of Disturbance-Based Controllers for Perturbed Integral Systems in Frequency Domain

It is well recognized that two-degree-of-freedom controllers are more proficient than one-degree-of-freedom controllers in overcoming uncertainty and large parameter variation. Establishing the equivalence analysis criterion among different classes of two-degree-of-freedom controllers to evaluate or compare the performance of controllers can stimulate more advanced two-degree-of-freedom controllers. We establish the equivalence analysis criterion by exploring the equivalence among proportional (P) with full-order extended state observer (FESO), P with reduced-order extended state observer (RESO) and proportional-integral (PI) with reduced-order proportional-integral observer (RPIO) in the context of a class of second-order, cascade integral systems. The equivalence analysis results are also suitable for high-order cascade integral systems. Moreover, P with FESO, P with RESO and PI with RPIO can be interpreted as a class of the generalized PI controllers, which can provide parameter tuning guidelines for the traditional PI controllers. Furthermore, the parameters of PI with RPIO cascade control can be entirely applied to tune the parameters of P with FESO cascade control and P with RESO cascade control and vice versa. Lastly, simulations in high speed permanent magnetic synchronous machine starter are provided to illustrate the theoretical results in this brief.