Classical Predictive Control Research Articles

Impact of Sequential Model Predictive Control on Induction Motor Performance: Comparison of Converter Topologies

Finite Set Model Predictive Control (FS-MPC) is a widely used technique in power electronic converter applications. One challenge in FS-MPC implementation is selecting appropriate weighting factors, as there is currently no established methodology for finding the best values. An alternative approach is to consider cost functions without weighting factors, as used by the Sequential Model Predictive Control (SMPC). In this paper, the performance of SMPC applied to induction motors is analyzed. The SMPC strategy involves sequentially evaluating simple cost functions by considering a limited number of available switching states for the power electronic converter. This number is the control parameter of the SMPC. The parameter’s domains and a selection criteria based on THD were established in this investigation. The power converter topologies studied include the Voltage Source Inverter (VSI) and the Neutral Point Clamped three-level (3L-NPC). Simulations performed in PLECS software and Hardware-in-the-Loop (HIL) tests using an RT Box for valid parameters satisfy the characteristics of the classical predictive control, such as good control variables tracking and high dynamic response. For a VSI converter, increasing the control parameter results in reduced harmonic distortion, while for an NPC converter, optimal results are achieved with control parameter values within a specific range.

A Complete Framework for a Behavioral Planner with Automated Vehicles: A Car-Sharing Fleet Relocation Approach.

Currently, research on automated vehicles is strongly related to technological advances to achieve a safe, more comfortable driving process in different circumstances. The main achievements are focused mainly on highway and interurban scenarios. The urban environment remains a complex scenario due to the number of decisions to be made in a restrictive context. In this context, one of the main challenges is the automation of the relocation process of car-sharing in urban areas, where the management of the platooning and automatic parking and de-parking maneuvers needs a solution from the decision point of view. In this work, a novel behavioral planner framework based on a Finite State Machine (FSM) is proposed for car-sharing applications in urban environments. The approach considers four basic maneuvers: platoon following, parking, de-parking, and platoon joining. In addition, a basic V2V communication protocol is proposed to manage the platoon. Maneuver execution is achieved by implementing both classical (i.e., PID) and Model-based Predictive Control (i.e., MPC) for the longitudinal and lateral control problems. The proposed behavioral planner was implemented in an urban scenario with several vehicles using the Carla Simulator, demonstrating that the proposed planner can be helpful to solve the car-sharing fleet relocation problem in cities.

Controller Coordination Strategy for DC Microgrid Using Distributed Predictive Control Improving Voltage Stability

The paper presents the design and control strategy of an isolated DC microgrid, which is based on classical control techniques, predictive control and iterative algorithms. The design control parameters are maximum overshoot, settling time and voltage ripple. The strategy is designed to operate in two different modes, end-users minimum and maximum demand scenarios, and this is achieved through the incorporation of network dynamic loads. The control methodology developed allows to obtain a fast response of the design set points, and an efficient control for disturbance rejection. The simulation results obtained satisfy the proposed design guidelines by obtaining a maximum overshoot of 4.8%, settling time of 0.012 seconds and a voltage ripple of 0.1 percentage. The implemented system simulation was developed in Matlab-Simulink software.

Performance of predictive control for a continuous horizontal fluidized bed dryer

Advanced process control can benefit to the operation efficiency and quality of tablets produced by wet granulation lines. Following the development of a new fully continuous fluidized bed reactor as a potential substitute for the current batch dryer design, this paper examines the energy and product quality performances of two predictive control strategies using an experimentally validated simulator. The first one proposes an economic model predictive controller explicitly minimizing the dryer energy consumption. The second one follows the same objective, but uses a simpler classical predictive control framework implementing targets on manipulated variables to drive them toward low energy consumption operating points. Both controllers lead to similar performances, and consume about 20% less energy than a reference feedback scheme without manipulated variable setpoints. Results also show that tracking the product quality attribute significantly benefits from a feedforward action because of the long residence time. This article addresses a subject little explored by the literature, the control of continuous fluidized bed dryers, and clearly demonstrates that a standard linear strategy can reduce the operating costs with an appropriate design.

Effect of Winding Design on the Performance of Predictive Current Control of Six-Phase Induction Machine-Based Propulsion Systems

Electric propulsion based on six-phase machines in marine propulsion and automotive traction applications are undergoing rapid development due to its high capacity, fault tolerance, reduced size, and reduced torque pulsations. Among different control techniques of six-phase machines, direct controllers, such as finite control set model predictive control (FCS-MPC), have extensively been studied in recent literature. One of the main problems of this controller type is the relatively poor current quality due to circulating <i>xy</i> current components. This problem has been tackled in literature through enhanced control techniques and/or winding design. Following both approaches, this paper extends a previous study to deeply investigate the effect of winding design on the performance of the model predictive control applied to a six-phase induction machine (SPIM). Three six-phase winding layouts have been compared, namely, dual-three phase (D3P), asymmetrical (A6P) and symmetrical (S6P) configurations. The main objective of this study is to investigate the effect of winding chording when standard three-phase stator frames are employed on the current quality of different winding configurations under classical predictive current control (PCC). A 1.5Hp prototype system has been used for this experimental comparative study.

Predictive current control based pseudo six-phase induction motor drive

Asymmetrical six-phase induction machines are widely used in high-power safety-critical applications. The so-called pseudo six-phase winding layout has recently been proposed as a promising contender for high power medium voltage applications in terms of torque density, phase current quality, simple single-layer-based stator winding design, and fault-tolerant capability. This winding layout employs quadruple three-phase stator winding sets connected to provide only six terminals, while being fed from two three-phase inverters. Therefore, the same conventional six-phase-based control structure can still be employed. Among the different control techniques, model predictive current control is considered as one of the cutting-edge technologies for multiphase drive systems, thanks to its simplicity and flexibility in defining new control objectives. This paper investigates how the available 64 voltage vectors of a six-phase inverter are classified among different subspaces when employing a pseudo six-phase stator winding. Moreover, the required modifications to classical predictive current control (PCC) are introduced in order to properly control the machine in a manner similar to conventional asymmetrical six-phase machines while ensuring minimum circulating xy current components. The concept of virtual voltage vectors (VVVs) has also been employed to further enhance the stator current quality. The proposed controller is validated using a 2Hp prototype pseudo six-phase machine under different operating conditions. An experimental comparative study with an asymmetrical six-phase machines is also carried out to verify the claimed equivalence.

Enhanced control technique for a sensor-less wind driven doubly fed induction generator for energy conversion purpose

The current paper is concerned with introducing a predictive polar flux control (PPFC) scheme for a variable speed wind driven doubly fed induction generator (DFIG) without speed sensor. The operation of the designed PPFC is based on the power angle regulation. The adaptation process is performed through studying the relation between the generator’s torque and the power angle between the stator and rotor flux vectors. A robust observer is designed based on the back-stepping theory to estimate the rotor speed, stator currents, rotor flux and stator and rotor resistances. Furthermore, an effective maximum power point tracking (MPPT) scheme is designed to achieve the optimal wind power exploitation. To recognize the operation of the schemed PPFC, a discursive performance evaluation is performed for the modeled control scheme and the classic predictive torque control (PTC) scheme. The achieved results report that the DFIG’s performance is effectively enhanced with the proposed PPFC in comparison with the PTC technique. The improved dynamics are observed through the ripples reduction and reduced switching frequency. In addition, the designed observer has successfully managed in estimating the specified variables with high precision.

Enhancement of Induction Motor Dynamics Using a Novel Sensorless Predictive Control Algorithm

The paper introduces a novel predictive voltage control (PVC) procedure for a sensorless induction motor (IM) drive. In the constructed PVC scheme, the direct and quadrature (d-q) components of applied voltages are primarily managed instead of controlling the torque and flux as in the classic predictive torque control (PTC) technique. The theoretical basis of the designed PVC is presented and explained in detail, starting from the used cost-function with its relevant components. A comprehensive performance comparison is established between the two controllers, from which the superiorities of the designed PVC over the PTC approach can be easily investigated through the reduced ripples, reduced computation time, and faster dynamics. To sustain the system’s reliability, a combined Luenberger–sliding mode observer (L-SMO) is designed and verified for different operating speeds for the two controllers. The Luenberger component is concerned with estimating the stator current, rotor flux, and rotor speed. Meanwhile, the sliding mode term is used to ensure the system’s robustness against any disturbance. The verification of PVC’s validity is outlined through performing a performance analysis using the Matlab/Simulink software. The results illustrate that the IM dynamic is significantly improved when considering the constructed PVC compared with the IM dynamics under the PTC. In addition, the designed L-SMO observer has effectively proved its ability to achieve definite parameters and variable estimation.

Fixed switching frequency scheme for current predictive control of DFIG

This paper suggests a new current predictive control algorithm for the wind-driven doubly-fed induction generator (DFIG) based on space vector modulation (SVM) with fixed switching frequency. Combining the current predictive controller and SVM has the benefits of a predictive controller, and fixing the switching frequency improves the output waveform quality. However, if classical predictive control is used, the computational burden will still be high. The proposed algorithm uses SVM to fix the switching frequency of the rotor side converter. Additionally, by using an incremental algorithm, the proposed technique prevents examining all inputs over the prediction horizon. As a result, in the proposed current predictive algorithm the computational time is significantly reduced. The detailed state-space model of DFIG and rotor side converter are used to execute the predictive algorithm. The proposed algorithm uses the rotor current to predict the next behavior of the DFIG, and then, by using SVM, the duty cycles of 2-level voltage source inverter are obtained. Finally, the proposed controller's responses are compared to those of the SVM-based field-oriented control strategy. Simulation results of the proposed controller on a two-level voltage source inverter under a balanced three-phase power system illustrate the satisfactory active and reactive power tracking and improved quality of the inverter outputs.

A Critical Review on the Control Strategies Applied to PCM-Enhanced Buildings

The incorporation of phase change materials (PCM) in buildings has the potential to enhance the thermal efficiency of buildings, reduce energy cost, shift peak load, and eventually reduce air pollution and mitigate global warming. However, the initial capital cost of PCM is still high, and thus the establishment of a control strategy has become essential to optimize its use in buildings in an effort to lower investment costs. In this paper, an extensive review has been made with regard to various control strategies applied to PCM-enhanced buildings, such as ON/OFF control, conventional control methods (classical control, optimal, adaptive, and predictive control) and intelligent controls. The advantages and disadvantages of each control strategy are evaluated. The paper further discusses the opportunities and challenges associated with the design of PCM-enhanced buildings in combination with control strategies.

Control of an Asynchronous Electric Drive Based on Forecasting Its State

Variants of constructing control systems with a lagging argument for the positioning problem of an asyn-chronous electric motor (IM) and the problem of energy-saving AM control are considered. Variants of control of an asynchronous electric drive with IM on the basis of predicting its state are considered. The analytical de-sign of the predictor (ADP) is an asynchronous motor control algorithm based on the mathematical theory of optimal control (L.S. Pontryagin's maximum principle). The control algorithm (ADP) ensures the achievement of the minimum value of the target functional, which (functional), in contrast to the structure of the classical pre-dictive control system (Model Predictive Control - MPC), is clearly not part of the ADP. Calculations of the movements of an electric drive with an IM in the control modes of its state, taking into account delays along the channels for assessing its state and control, as well as using predictors, are given. The effectiveness of the in-troduction of predictors to improve the stability and quality of control of an electric drive with an IM has been established.

A Novel Modulated Model Predictive Control Applied to Six-Phase Induction Motor Drives

Model-based predictive control techniques, with finite set control, are considered an interesting option to control multiphase drives due to their control flexibility and fast dynamic response. However, those techniques have some drawbacks such as a high computational cost, poor ( $x-y$ ) currents reduction, and steady-state error, especially at high speeds. To improve some of these drawbacks, modulation stages have been presented as an alternative. However, some of those drawbacks have not been improved. This article proposes a novel approach to the classic predictive current control (PCC) applied to an asymmetrical six-phase induction machine, where a space vector modulation with specific vectors is used in order to improve the ( $x-y$ ) currents, the steady-state error and total harmonic distortion (THD) at high operation speeds. Experimental results are presented to demonstrate the characteristics of the proposed control technique in terms of current tracking, ( $x-y$ ) currents reduction and THD of stator currents compared to the classic PCC.

Predictive-Fixed Switching Current Control Strategy Applied to Six-Phase Induction Machine

In applications such as multiphase motor drives, classical predictive control strategies are characterized by a variable switching frequency which adds high harmonic content and ripple in the stator currents. This paper proposes a model predictive current control adding a modulation stage based on a switching pattern with the aim of generating a fixed switching frequency. Hence, the proposed controller takes into account the prediction of the two adjacent active vectors and null vector in the ( α - β ) frame defined by space vector modulation in order to reduce the (x-y) currents according to a defined cost function at each sampling period. Both simulation and experimental tests for a six-phase induction motor drive are provided and compared to the classical predictive control to validate the feasibility of the proposed control strategy.

Finite-State Model Predictive Control With Integral Action Applied to a Single-Phase Z-Source Inverter

Finite control set model predictive control can be applied to a power converter if there is an accurate existing model of the converter. The best results will be achieved if and only if the parameters and variables that make up the system are properly estimated. If this is not the case, the predictions made using these strategies may be erroneous and cause problems, such as steady-state error with respect to the assigned desired references. This paper presents a predictive control strategy with integral action that compensates for the differences between the estimated model and the inverter with the objective of achieving a zero steady-state error without requiring external loops or state observers. The proposed strategy is tested on a single-phase Z-source inverter so as to evaluate the error in both the ac and dc controlled variables with respect to their references to their cosigns. The experimental results confirm that the proposed strategy achieves a zero error in the steady state while maintaining the fast dynamic response of the classic predictive control.

Dynamic Correction Algorithms in Multi-axis Systems Based on Predictive and Invariant Control Methods

This article considers the issue of controlling a mobile robotic technological complex based on the use of predictive and invariant control methods for compensation of external indeterminate perturbations. Authors carry out the analysis of the perturbations arising in the control system. We propose to introduce three correction contours into the control system structure ensuring parrying of all the perturbation components. A modification of the classical predictive control scheme based on the application of complementary correction algorithms is presented. The algorithms of the complementary correction are based on the statements of the fourth modified form of invariance. We consider the application of cross-correction in the automatic control system of a robot while controlling the motion along specified technological trajectories.

Effective Predictive Flux Control for an Induction Motor Drive with an Online Estimation Procedure for Stator Transient Inductance

This paper focuses on the presentation of an effective predictive flux control (PFC) approach for an induction motor (IM) drive. The finite control set (FCS) principle is used in order to select the inverter voltages and thus there is no need to use the pulse width modulation (PWM) strategy. The proposed control approach uses a cost function formulation in which there is no need for tuning the value of the weighting factor which is inherently present in the classic predictive torque control (PTC) approach, and thus the computational burden is effectively reduced. The proposed PFC approach is derived based on the rotor field orientation principle in which the stator flux components are utilized as control variables instead of utilizing the stator current components as adopted in the classic RFO technique. In order to get better-predicted signals with limited noise content, the stator flux is estimated using an effective flux estimator instead of using the machine voltage model. An effective online identification procedure for the stator transient inductance is proposed and analyzed in a systematic manner, which has led to an improvement in the rotor flux orientation, and thus the robustness of the controller is enhanced. The performance of the drive is tested during the normal speed operation and then during the field weakening operation. The obtained results confirm the validity of the proposed PFC control approach and the effectiveness of the identification procedure in achieving correct and precise rotor field orientation which consequently results in improving the overall dynamic performance of the drive.

A model‐based flight qualities evaluation approach for civil aircraft

SummaryEvaluation of flight qualities plays an important role in the design of a civil aircraft. An appropriate level of flight qualities will be the key to achieve the airworthiness certification. With the objective to provide an overall view of flight qualities evaluation for a civil aircraft, a model‐based approach is proposed. Some terms related to flight qualities are defined formally, and the model‐based flight qualities evaluation approach under different situations is formed and proposed. Two working conditions of actuators are considered in the evaluation of flight qualities. When no actuator fails, linearization is adopted to build a linear flight dynamics model for analyzing the main parameter of flight qualities. When some actuators fail, a new off‐line approach for handling qualities evaluation, which employs a controller that generates appropriate commands for healthy actuators, is proposed. The design of the controller is following the idea of classical predictive control technique, and a recurrent linear quadratic programming problem is formulated to find the maximum remaining maneuverability while not avoiding any constraints. Taking the rotational maneuver as a study example, a test‐bed for handling qualities evaluation is built. The numerical simulation results show the feasibility of proposed evaluation approach.

Evolution of Topologies, Modeling, Control Schemes, and Applications of Modular Multilevel Converters

Modular multilevel converter (MMC) is one of the most promising topologies for medium to high-voltage high-power applications. The main features of MMC are modularity, voltage and power scalability, fault tolerant and transformer-less operation, and high-quality output waveforms. Over the past few years, several research studies are conducted to address the technical challenges associated with the operation and control of the MMC. This paper presents the development of MMC circuit topologies and their mathematical models over the years. Also, the evolution and technical challenges of the classical and model predictive control methods are discussed. Finally, the MMC applications and their future trends are presented.

Optimal Predictive Torque Control of Two PMSM supplied in Parallel on a Single Inverter

This paper presents a new approach of Optimal Predictive Torque Control (OPTC) for two Permanent Magnet Synchronous Machines supplied in parallel on a single mutualized three-leg two-level inverter. In the field of multi synchronous machine control with a single inverter, the classic Predictive Torque Control (PTC) rests on the selection of a configuration of switches among a discrete set of possible solutions by the minimization of a single criterion of performance, sum of the criteria specific to each machine. It leads then to the definition of an applied voltage which results from a compromise between the machines. The method suggested here rests on the calculation of the optimal controls on a continuous set of solutions for each of the two machines separately, and the generation of the final applied voltage via the calculation of a mean value. In addition to the reduction of the highly-reduced computing time, this control law improves the Total Harmonic Distortion (THD) of the line currents.

Design and Stability Analysis of Uncertain Networked Predictive Control Systems with Multiple Forward Channels

This paper is concerned with the design and stability of networked predictive control for uncertain systems with multiple forward channels. The delays and packet dropouts are distributed such that the classic networked predictive control (NPC) needs modifications to be implemented. An improved control signal selection scheme with distributed prediction length is proposed to increase the prediction accuracy and hence achieve better control performance. Moreover, stability analysis results are obtained for both constant and random cases. Interestingly, it is shown that the stability of the closed-loop NPC system is not related to the distributed delays when they are constant and the system model is accurate. Finally, a two-axis milling machine example is given to illustrate the effectiveness of the proposed method.