Mariana Santos Matos Cavalca

Model-based predictive direct speed control applied to a Permanent Magnet Synchronous Motor with trapezoidal back-EMF

This paper presents an investigative work about the application of State Space Model-Based Predictive Control in a three-phase Permanent Magnet Synchronous Motor with trapezoidal back-electromotive force, for speed control. Such motor is utilized in the white goods appliances industry and also in automotive and medical applications, among others, especially due to its high efficiency and long life cycle. The predictive control methods present a differential in the driving performance for industrial applications, mainly by enabling the imposition of constraints. In this work, a linear prediction model identified with a Least Mean Squares algorithm is used with the State-Space predictive control approach. Such predictive method is interesting for industrial applications for being easy to tune and, in addition to the imposition of constraints, allowing ponderation between tracking performance and spent energy. The utilization of constraints is discussed for the predictive algorithm in this work. There are satisfactory simulated and experimental results that show advantages in using the mentioned control method to drive the Permanent Magnet Synchronous Motor.

Robust model predictive control using linear matrix inequalities for the treatment of asymmetric output constraints

One of the main advantages of predictive control approaches is the capability of dealing explicitly with constraints on the manipulated and output variables. However, if the predictive control formulation does not consider model uncertainties, then the constraint satisfaction may be compromised. A solution for this inconvenience is to use robust model predictive control (RMPC) strategies based on linear matrix inequalities (LMIs). However, LMI-based RMPC formulations typically consider only symmetric constraints. This paper proposes a method based on pseudoreferences to treat asymmetric output constraints in integrating SISO systems. Such technique guarantees robust constraint satisfaction and convergence of the state to the desired equilibrium point. A case study using numerical simulation indicates that satisfactory results can be achieved.

Model-Based Predictive Control Using Differential Evolution Applied to a Pressure System

Model-Based Predictive Control techniques have been applied industrially since the 1970s, presenting favorable characteristics. Among them, it can be mentioned the treatment of process constraints and optimization considering the output error with respect to the reference and the used energy. Usually, formulations based on linear models of the plant are used. However, the use of linear models for nonlinear plants can result in control loops with limited or even reduced performance. In this paper, the use of a Model-Based Predictive Control approach using a Differential Evolution algorithm for optimization applied in a pressure control system is presented. The results show the advantages found by using such nonlinear formulation. The methodology used, with comments about the obtained results and conclusions on this line of research are presented.

Analysis of predictive control for boost converter in power factor correction application

This work presents a boost converter design methodology for active power factor correction, using finite control set model-based predictive control (FCS-MPC) for current control. Moreover, an external proportional integrative (PI) voltage control loop design methodology is explained. This paper also compares the conventional PI current control with MPC current for this application, both in transient load disturbance condition and in steady state different load conditions. The results in rated operation are compared with IEC 61000-3-2 international standard, which establishes limits for current harmonics rms values. The design methodology shows itself effective, with results near to the expected. Both controllers presented advantages and disadvantages, many of them exhaustively discussed in this work.

Evaluation of constrained and unconstrained SESSMPC applied in five-phase PMSM

This work presents a comparative analysis of a successively evaluated state space model-based predictive control approach used to drive a five-phase permanent magnet synchronous motor. It is presented a five-phase motor modelling and control theoretical basis main concept, including the prediction model preparation, as well unconstrained and constrained formulations. Both formulations are used and compared to drive the motor, in the simulation results. The results satisfactorily demonstrate the advantages of the model-based predictive control to drive this motor, specially due to the constraints treatment.

Fault tolerant control for permanent magnet synchronous motor

This paper presents a fault tolerant control proposal for a single Open Phase Fault (OPF) in a three-phase Permanent Magnet Synchronous Motor (PMSM). The mathematical model of the motor under open single phase is presented and its disturb to current, flux and torque are evaluated. The presented method is based on the Finite Control Set Model Based Predictive Control (FCS-MPC), and it is compared to other fault tolerant Direct Torque Control (DTC) methods. All the tests are run with the same conditions and results show that FCS-MPC has a better performance than the methods presented in the literature.

Small wind turbine operating points and their effect on the DC-link control for frequency support on low power microgrids with high wind penetration

When the wind power accounts for a large portion of the islanded microgrid power, it may need to help the ac bus frequency regulation. The increasing penetration of variable speed wind turbines (WT) in microgrids leads to a lower inertia, as the rotational speed of the turbine and the grid are decoupled by power electronic converters. Lower system inertia results in a larger and faster frequency deviation after occurrence of abrupt variations on generation and load. It is possible to implement control loops in the WT converters to provide a virtual inertia and support frequency regulation in the microgrid. This paper investigates the variables related to the frequency and active power compensation capability of WT, such as kinetic energy, DC-link capacitance, turbine size, speed and operation region. The analysis is done in closed loop considering an optimal control technique to provide fair comparison among the variables and provide the optimum control trajectory, limiting the mechanical losses.

A three-phase model-based predictive control method for induction motors

The use of predictive controllers for induction motors actuation is growing with the improve of the computational capability. Consequently, new specific techniques have been developed. However, these techniques still often uses classical paradigms of machine control, only adapted to the predictive controllers. This paper proposes the use of a three-phase induction motor model for design of a predictive controller as an alternative to classical techniques such as vector control. The main point is to validate the use of such model to allow better utilizing of the benefits of predictive approaches, such as the treatment of constraints. The results show that the use of a three-phase model is viable with predictive control algorithms and can add some qualities to the techniques already developed, as use of constraints and support for unbalanced systems.

Evaluation of constrained SESSMPC to drive a three-phase PMSM applied in washing machines

This work proposes a study about using the successively evaluated state space model-based predictive control to drive a permanent magnet synchronous motor. The objective of this paper is to show as the control constraints and the anticipative reference characteristics influence in the drive. Still, it is done a brief analysis about the improvement of the drive efficiency with the proposed control scheme. The obtained results present the validity of the proposed approach.

Differential evolution optimization applied in multivariate nonlinear model-based predictive control

Model-based Predictive Controllers belong to a class of digital controllers which are used in many industrial applications. Such controllers have the main advantages of dealing with optimization subject to constraints and multiple-input, multiple-output systems. The optimization of the control system behavior is based on an explicit mathematical prediction model of the plant. Usually, linear approaches are used for the prediction model. However, for nonlinear plants, linear models may limit the control loop performance or even cause instability. In this work, a Nonlinear Model Predictive Controller, with optimization based on a nonlinear model performed with a Differential Evolution algorithm, was tested for position control of a robotic arm model. Simulation results show that the utilized control algorithm was able to deal with multivariable nonlinear optimization in the presence of process constraints.