Finite Control Research Articles

Augmented Finite Control Set MPC Design Technique for Wide Speed Range Control of Permanent Magnet Synchronous Motor Drives

The effective motion control task is highly influencing the final drive performance in wide-range motor drives applications such as electric vehicles (EVs), washing machines, compressors, elevators and lathe machines. Recently, permanent magnet synchronous motor (PMSM) drive has been widely employed in these applications to achieve desired performances such as high efficiency, high torque requirements with minimum size “occupancy”. The PMSM drive features combining with these modern drive applications are highly nonlinear and the desired operation requirements demand high-performance control schemes with fast system response and robustness features. Linear control schemes are usually implemented to drive the PMSM that entail trade-offs between drive performance and simplicity in control implementation. Therefore, this paper presents the nonlinear control technique based on augmented finite control set (AFCS) model predictive control (MPC) scheme (AFCS-MPC). The proposed AFCS-MPC scheme enhances the standard MPC design by augmenting the nonlinear characteristics of the PMSM drive to achieve wide speed range drive performance under both steady-state and dynamic operating conditions. Comparative performance studies have been conducted and presented to confirm the efficacy of the proposed scheme.

The lexical semantics of finite control: A view from Japanese

In this paper, we propose a semantic analysis of control verbs in Japanese that take finite clauses marked by the nominalizer koto. We argue for an analysis in which the invisible subject of the embedded clause is a run-of-the-mill zero pronoun and where the obligatory coreference relation between the controller and the embedded subject is mediated by a primarily semantic factor. At the heart of our analysis lies the idea that there is a common underlying meaning shared across apparently heterogeneous classes of koto-taking control verbs, which consists of a causal relation between a volitional action inherent in the meaning of the verb and a de se attitude denoted by the embedded clause. The semantic analysis we offer not only explains language-internal properties of Japanese control verbs with respect to tense morpheme distribution that have been attributed to syntactic factors in the previous literature, but it also suggests a hitherto unnoticed possible cross-linguistic generalization about finite control and embedded tense interpretation which we dub ‘hypothesis of relative tense in finite control.’

Finite time fault estimation and fault‐tolerant control for a class of uncertain nonlinear systems based on cascaded observers

AbstractIn this paper, a finite time fault estimation and fault‐tolerant control method is proposed for a class of uncertain nonlinear systems that have both actuator faults and sensor faults. First, the outputs of the original system pass through the low‐pass filters to obtain the augmented states, and then an augmented system is composed of the states of the low‐pass filters and the original system, so as to realize the conversion of the sensor faults of the original system into partial actuator faults of the augmented system. Next, design state observers to reconstruct the unknown states of the augmented system, and develop extended state observers to estimate the actuator faults and disturbance of the augmented system. Again, the fault‐tolerant controllers are completed based on the backstepping control and the finite time control, and the finite time convergences of the designed observers and controllers are proved by theoretical analyses. Finally, based on a permanent magnet synchronous motor (PMSM) speed system, the simulation research and dSPACE simulated experiment research are conducted to verify the effectiveness of the proposed control method and its feasibility in the actual application.

An Optimized Model Predictive Control Method With Fixed Switching Frequency for Eliminating Common-Mode Voltage of Five-Level Converters

In this paper, an optimized model predictive control (MPC) method with fixed switching frequency (FSF) is proposed to eliminate the common-mode voltage (CMV) and balance the capacitor voltage. Firstly, the zero CMV vector diagram is divided by finite control set MPC (FCS-MPC) to obtain the candidate vectors effectively. Then, in order to control the capacitor voltage and avoid the great computational burden originated from the redundant switching combinations, a piecewise control scheme is proposed to determine the required ones directly. The sequence generated by the selected vectors is further optimized for the purpose of saving switching transitions according to the internal and external parts of the vector diagram. Finally, the duty ratios are evaluated based on the results of the FCS-MPC in dividing sectors so as to avoid any extra complex calculation. The experimental evaluation has been carried out to validate the feasibility and effectiveness of the proposed method.

Event-triggered prescribed time adaptive fuzzy output-feedback control for nonlinear systems subject to bounded disturbances via command filtering

The issue of prescribed time adaptive fuzzy output-feedback event-triggered control strategy subject to bounded disturbances is considered in this paper. For the unmeasured states, an adaptive fuzzy observer is designed to estimate them. Then, a novel event-triggering condition involving a decreasing function containing the error between the system states is designed by synthesising the backstepping design framework and command filtered technique. The developed prescribed finite time control approach ensures that all signals of the closed-loop system are bounded and the tracking error remains within the predefined bound in the prescribed time. Meanwhile, Zeno phenomenon can be avoided. Finally, a mass-spring-damper system is taken as an example for simulation.

Hexagonal zoning deadbeat model predictive control of induction motor

AbstractDeadbeat model predictive control is known as a precise control method in motor control applications. Recently, finite set deadbeat control is applied to power electronics applications. A voltage‐based cost function is used to select the closest voltage vector to the reference vector by examining all possible states in the cost function. This method needs a high amount of computation when it is applied to multilevel inverters or virtual voltage vectors due to a considerable increase in the number of candidates. Some studies have been done to reduce the number of candidate voltage vectors and computation burden. The methodology in these methods is based on finding the adjacent area and applying the vertex vectors to the cost function. So, these strategies are multi‐step and complex. The hexagonal zoning deadbeat model predictive control is proposed and introduced. The basic idea of the proposed method is that each zone should contain only one vector. In this way, the area identification is equal to finding the optimum voltage selection. Hexagonal zoning is used, which simplifies the process of identifying the area. A simple area identification method is developed based on the reference voltage phase angle. The proposed method can be applied to different inverters with different spatial vector diagrams. Furthermore, the synthesis of the virtual vector is manipulated to reduce the torque and flux ripple. The proposed method is experimentally evaluated by applications to the virtual voltage switching of a two‐level inverter for the induction motor drive in this research.

Adaptive Virtual Impedance Control with MPC’s Cost Function for DG Inverters in a Microgrid with Mismatched Feeder Impedances for Future Energy Communities

More and more distributed generations (DGs), such as wind, PV or battery bank sources, are connected to electric systems or customer loads. However, the locations of these DGs are based on the highest energy that can be potentially harvested for electric power generation. Therefore, these locations create different line impedances based on the distance from the DGs to the loads or the point of common coupling (PCC). This paper presents an adaptive virtual impedance (AVI) in the predictive control scheme in order to ensure power sharing accuracy and voltage stability at the PCC in a microgrid network. The reference voltage from mismatched feeder impedances was modified by utilizing the suggested AVI-based predictive control for creating equal power sharing between the DGs in order to avoid overburdening any individual DG with low-rated power. The AVI strategy used droop control as the input control for generating equal power sharing, while the AVI output was used as the reference voltage for the finite control set–model predictive control (FCS-MPC) for creating a minimum voltage error deviation for the cost function (CF) for the inverter’s vector switching pattern in order to improve voltage stability at the PCC. The proposed AVI-based controller was tested using two DG inverter circuits in a decentralized control mode with different values of line impedance and rated power. The performance of the suggested controller was compared via MATLAB/Simulink with that of a controller based on static virtual impedance (SVI) in terms of efficiency of power sharing and voltage stability at the PCC. From the results, it was found that (1) the voltage transient magnitude for the AVI-based controller was reduced within less than 0.02 s, and the voltage at the PCC was maintained with about 0.9% error which is the least as compared with those for the SVI-based controller and (2) equal power sharing between the DGs increased during the change in the load demand when using the AVI-based controller as compared with using the SVI-based controller. The proposed controller was capable of giving more accurate power sharing between the DGs, as well as maintaining the voltage at the PCC, which makes it suitable for the power generation of consumer loads based on DG locations for future energy communities.

Predictive Control for PV- Water Pumping System based of Interconnected High Gain Observer

This article presents a comprehensive evaluation of an integrated photovoltaic (PV) and water pumping system employing both Finite Set Model Predictive Control (FS-MPC) and Deat Beat Predictive Control (DB-MPC) under varying insolation levels (1000 W/m² to 700 W/m²). The system, initially tested at 1000 W/m², rapidly achieves Maximum Power Point Tracking (MPPT), stabilizing PV parameters at the MPP within 0.2 seconds. Notably, DB-MPC demonstrates superior dynamic response and faster settling times compared to conventional methods. The Interconnected High-Gain Observer accurately estimates motor speed, facilitating the attainment of the rated rotor speed in line with the desired reference speed. Transitioning from 1000 W/m² to 700 W/m² insolation, the system exhibits stability in Vpv and rapid Ipv adjustment at MPP. The InC algorithm extends PV array operation, showcasing a decrease in power output to 2204 W at 60% flow rate. These results affirm the efficacy and adaptability of the proposed control strategies in optimizing the performance of the PV-driven water pumping system, offering promising advancements in sustainable energy applications.

Reduced order in domain control of distributed parameter port-Hamiltonian systems via energy shaping

An in-domain finite dimensional controller for a class of distributed parameter systems on a one-dimensional spatial domain formulated under the port-Hamiltonian framework is presented. Based on (Trenchant et al. 2017) where positive feedback and a late lumping approach is used, we extend the Control by Interconnection method and propose a new energy shaping methodology with an early lumping approach on the distributed spatial domain of the system. Our two main control objectives are to stabilize the closed-loop system, as well as to improve the closed-loop dynamic performances. With the early lumping approach, we investigate two cases of the controller design, the ideal case where each distributed controller acts independently on the spatial domain (fully-actuated), and the more realistic case where the control action is piecewise constant over certain intervals (under-actuated). We then analyze the asymptotic stability of the closed-loop system when the infinite dimensional plant system is connected with the finite dimensional controller. Furthermore we provide simulation results comparing the performance of the fully-actuated case and the under-actuated case with an example of an elastic vibrating string.

Current ripple reduction for finite control set model predictive control strategy of grid-tied inverter with reference current compensation

Current ripple reduction for finite control set model predictive control strategy of grid-tied inverter with reference current compensation

Cost evaluation of finite dimensional and approximate null-controllability for the one dimensional half-heat equation

In this article we evaluate the cost of the finite dimensional and the approximate controllability of a one dimensional anomalous diffusion equation which is not null-controllable. The finite dimensional controllability property consists in driving to zero a finite number of frequencies of the corresponding solutions. To evaluate its cost, we construct an explicit biorthogonal sequence to a finite part of a family of real exponential functions. Since the exponents of these functions satisfy the so-called Müntz density condition, there is no biorthogonal sequence to the entire family which shows that the equation is not even spectrally controllable. For the approximate controllability problem, we combine the idea of finite dimensional controllability studied before and the intrinsic dissipative nature of the system. These arguments allow to show that the controlled solution can be made arbitrarily small after sufficiently large time and provide estimates for the corresponding approximate controls.

Full state-constrained finite time control of non-strict feedback interconnected systems with dead zones based on backstepping

Full state-constrained finite time control of non-strict feedback interconnected systems with dead zones based on backstepping

Integration of predictive and computational intelligent techniques: A hybrid optimization mechanism for PMSM dynamics reinforcement

<abstract> <p>This paper presents an integrated approach combining a sequential neural network (SNN) with model predictive control (MPC) to enhance the performance of a permanent magnet synchronous motor (PMSM). We address the challenges of traditional control methods that struggle with the dynamics and nonlinear nature of PMSMs, offering a solution that leverages the predictive capabilities of MPC and the adaptive learning potential of neural networks. Our SNN-MPC model is contrasted with state-of-the-art genetic algorithm (GA) and ant colony optimization (ACO) methods through a comprehensive simulation analysis. This analysis critically examines the dynamic responses, including current, torque, and speed profiles, of the PMSM under proposed hybrid control strategies. The heart of the work deals with the optimal switching states and subsequent voltage injection to the inverter fed PMSM drive by a predefined minimization principle of a current modulated objective function, where MPC constitutes an integral finite control set (IFCS) mechanism for voltage vector selection and thereby selects the optimized integral gains <italic>K</italic><sub>d</sub> and <italic>K</italic><sub>q</sub> for direct and quadrature axes, respectively, with the FCS gain <italic>K</italic><sub>fcs </sub> obtained from implemented intelligent techniques. Based on the control criteria, the SNN-MPC scheme was established as the preferred benchmark with optimized tuning values of <italic>K</italic><sub>d</sub> = 0.01, <italic>K</italic><sub>q</sub> = 0.006, and <italic>K</italic><sub>fcs</sub> = 0.13, as compared to the gain values tuned from GA and ACO. The experimental setup utilized MATLAB and a Python environment for robust and flexible simulation, ensuring an equitable basis for comparison across all models.</p> </abstract>

Comparative Analysis of Direct Torque Control with Space Vector Modulation (DTC-SVM) and Finite Control Set-Model Predictive Control (FCS-MPC) of Five-phase Induction Motors

Comparative Analysis of Direct Torque Control with Space Vector Modulation (DTC-SVM) and Finite Control Set-Model Predictive Control (FCS-MPC) of Five-phase Induction Motors

Computationally Efficient Sphere Decoding Algorithm Based on Artificial Neural Networks for Long-Horizon FCS-MPC

Successful application of finite control set model predictive control (FCS-MPC) strategies with long prediction horizon depends on the careful design of the optimization algorithm. The conventional method involves transforming the problem to an equivalent box-constrained integer least-squares (BILS) formulation that can be solved with branch-and-bound techniques such as the sphere decoding algorithm (SDA). In this work, it is proposed to define an artificial neural network (ANN) to replace the SDA, avoiding its inherent computational variability. Similarly to practical applications of the SDA, the ANN finds an approximate solution of the underlying optimization problem. In contrast, the main benefit of the proposed approach is that it can be implemented in a low-cost microprocessing platform, greatly improving the performance in terms of resources in comparison with other advanced techniques proposed in the literature.

Study and Evaluation a New Predictive Control Method for Speed and Stator Current Control of Induction Motor

It is clear that modern industries rely heavily on electric motors, especially induction motors. These motors convert electrical energy into mechanical energy. The distinction in the performance of the induction motor lies in that it is powerful when exposed to various operational and environmental variables, and it is also inexpensive. However, there are many traditional disadvantages that appear during the operation of the induction motor in its non-linear mechanical properties in addition to the difficulty in regulating the speed of the motor. In this paper , we present a new control method for controlling the stator current and speed of induction motor based on current control with speed control technique. The present model is based on conventional predictive controller development with a structure which is similar to rotor control and the direct torque control .It has double loops and both loops will use the prediction power. The inner loop controls the stator current based on Finite Control Set - Model Predictive Control (FCS-MPC) and the outer loop controls the speed to maximize the dynamic response of the loop. The MATLAB software has been used to implement the controller circuit. The obtained simulation results indicates that the presented control method has comparable performance to conventional controllers with some reduction in the overshoot and fast response interval.

An Impact of Switching Frequency and Model Accuracy on Model Predictive Current Control Performance for Reluctance Synchronous Motors

Abstract The present paper investigates the feasibility of utilizing the simplified prediction model for finite control set model predictive current control (FCS-MPCC) applied to reluctance synchronous motors (RSMs). The FCS-MPCC exhibits torque and current ripples, and a crucial consideration is the reduction of these ripples by increasing the switching frequency. The algorithm’s computational complexity is tied to the accuracy of the adopted model. Two approaches with varying levels of accuracy in predicting current dependencies concerning changes in the inductance of the RSM are investigated. The findings highlight the potential of employing simplified fixed inductance values for efficient control in drive systems, particularly those amenable to high switching frequencies.

Finite Control Set Model Predictive Control for a Wheatstone Bridge Active Magnetic Bearing

Finite Control Set Model Predictive Control for a Wheatstone Bridge Active Magnetic Bearing

Cell-by-Cell-Based Finite Control Set Model Predictive Control for Paralleled Three-Phase Three-Level Inverters

Cell-by-Cell-Based Finite Control Set Model Predictive Control for Paralleled Three-Phase Three-Level Inverters

Finite Control Set Model Predictive Current Control (FCS-MPCC) of Three-Port Converter for Fuel Cell Hybrid Electric Vehicles

Finite Control Set Model Predictive Current Control (FCS-MPCC) of Three-Port Converter for Fuel Cell Hybrid Electric Vehicles