1,720,968 research outputs found
On-line continuous control set MPC for PMSM drives current loops at high sampling rate using qpOASES
No full textSeveral Continuous Control Set Model Predictive Control algorithms have been successfully proposed for synchronous motor drive current control applications. The choice of the optimal input for the system results into the resolution of a constrained quadratic programming problem. However, most of the MPC algorithms found in literature do not consider any constraints in the identification of the optimal inputs, mainly because of computational burden limit. In the electric drives field this limitation is particularly strict, since industrial applications requires control frequency higher than 5 kHz. This paper deals with the analysis of an on-line continuous control set MPC which solves in real-time mode the quadratic problem, considering also the input constraints. The input feasible domain is mathematically derived in this paper and the information is properly given to a quadratic programming problem solver. The open-source solver qpOASES run real-time the MPC algorithm on a high performance RD hardware, at rate of 6 kHz. The proposed control scheme is applied as current regulator for an interior permanent magnet synchronous motor. The performances achieved with the proposed constrained MPC are compared to those of another MPC that computes only the unconstrained solution, coupled with an over-modulation strategy. Both steady state and transients experimental tests are reported, considering the operating conditions where the voltage constraints are violated
Sensorless control of Interior Permanent Magnet motor using a Moving Horizon Estimator based on a linearized motor model
No full textThis paper presents a sensorless control solution for Interior Permanent Magnet synchronous motor using a Moving Horizon Estimator. The active flux concept is used to describe the motor model. The position estimation problem is non linear for the considered type of synchronous machine, thus a linearization of the model is performed using a Taylor first order approximation. Thanks to the linearization, the problem assumes the form of an equality constrained Quadratic Programming that can be solved directly, without using any iterative method. The estimator is real-time implemented, coupled with a standard PI speed controller and a Model Predictive Control for the current loop, highlighting the feasibility of this innovative control architecture. Several steady state and dynamic tests are provided in this work to show the drive performances. In details, steady state operation, torque step, speed inversion and a speed ramp are included in the study
A model predictive control for synchronous motor drive with integral action
No full textThis paper deals with a novel robust current control scheme for Synchronous Motor (SM) drives, based on the Model Predictive Control (MPC) theory augmented with an Integral Action. The increased robustness is achieved through the integrator, nevertheless the same effective MPC quadratic formulation is hold. The integral action, within the prediction step of MPC, improves drastically the local accuracy of the nominal model prediction with further advantages in terms steady-state error for reference tracking problem. Holding all the model predictive control advantages, well known in literature, the effectiveness of the proposed control is verified by mean of simulations and test-bed experiments
A speed and current cascade Continuous Control Set Model Predictive Control architecture for synchronous motor drives
No full textModel predictive control represents a quite mature technology for current control of electric drives. Continuous control set MPC has been widely investigated for the current loop of permanent magnet synchronous motor. Standard PI controllers are often preferred for the speed loop. An innovative speed and current cascade model predictive control architecture for synchronous motor drives is proposed in this paper, to comply with the different requirements of the two loops. Parameter uncertainties represent a critical issue in the implementation of predictive controllers. Considering continuous control set MPC, these mismatches induce bias errors in the desired reference tracking. On one hand, a MPC with intrinsic integral action is adopted in the current loop. The integral action avoids the error induced by electric parameters uncertainties. On the other hand, a separate continuous control set MPC is adopted for the speed loop. A moving horizon estimator supports the latter MPC algorithm, compensating mismatches in the mechanical model of the system. This estimator is used, for instance, to compensate external loading torques. The proposed architecture is validated through experiments on an interior permanent magnet motor. Performances of the proposed solution are compared to the ones obtained using a cascade of PI controllers. Robustness of the architecture against mechanical and electric model uncertainties is investigated too
Data-Driven Continuous-Set Predictive Current Control for Synchronous Motor Drives
Full text linkOptimization-based control strategies are an affirmed research topic in the area of electric motors drives. These methods typically rely on an accurate parametric representation of the motor equations. In this paper, we present the transition from model-based towards data-driven optimal control strategies. We start from the model predictive control paradigm which uses the voltage balance model of the motor. Second, we discuss the prediction error method, where a state-space model is identified from data, without a parametrization. Moving toward data-driven controls, we present the Subspace Predictive Control, where a reduced model is constructed based on the singular value decomposition of raw data. The final step is represented by a complete data-driven approach, named data-enabled predictive control, in which raw data is not encoded into a model but directly used in the controller. The theory behind these techniques is reviewed and applied for the first time to the design of the current controller of synchronous permanent magnet motor drives. Design guidelines are provided to practitioners for the proposed application and a way to address offset-free tracking is discussed. Experimental results demonstrate the feasibility of the real-time implementation and provide comparisons between model-based and data-driven controls
Continuous Control Set Model Predictive Current Control of a Microgrid-Connected PWM Inverter
No full textThis paper deals with a novel control architecture for pulse width modulation inverters connected to the grid through resonant LCL filters. A continuous-control-set model predictive control scheme is derived for the grid current loop. A state observer, using a moving horizon estimator algorithm, is used for reducing the order of the mathematical model representing the grid. The proposed control architecture allows for reducing the number of required sensors, for eliminating measurement noise and for achieving a good stability against voltage grid disturbances and parameter uncertainties. High performance hardware is used in order to run a quadratic programming solver in real-time, with a control sampling rate of 10 kHz. The effectiveness of the proposed control architecture is validated by means of test-bed experiments demonstrating the feasibility of real-time operation and promising results
Data-driven predictive current control for synchronous motor drives
No full textData-driven control techniques have become increasingly popular in recent years due to the availability of massive amounts of data and several advances in data science. These control design methods bypass the system identification step and directly exploit collected data to construct the controller. In this paper, we investigate the application of data-driven methods to the control of electric motor drives, and specifically to the design of current controllers for three-phase synchronous permanent magnet motor drives. Two of the most promising data-driven algorithms are presented, namely the Subspace Predictive Control algorithm and the Data-Enabled Predictive Control algorithm. The theory behind these techniques is first reviewed in the optimization-based control framework. Standard algorithms are slightly modified to fulfill the requirements of the specific application, and then simulated in the MATLAB Simulink environment. Some key aspects of real-time implementation are studied, providing a proof-of-concept demonstration of the applicability of these algorithms. The data-driven design is proposed for three different topologies of synchronous motors, proving the flexibility of the approach
A novel formulation of continuous control set MPC for induction motor drives
No full textThe Induction Motor is the most widespread motor for industrial use, thanks to its simple construction, low cost and robustness, both at constant and variable-speed applications. Continuous Control Set Model Predictive Current Control strategy has not been deeply investigated for Induction Motor, due to its complicated state model. In fact, it can be described by a second order model, which requires both stator and rotor electric parameters. In this work, the Continuous Control Set solution is implemented. The internal motor model exploited by the current control is derived. Moreover, an Integral Action is included directly in the Model Predictive Control formulation, avoiding steady-state bias errors caused by parameter uncertainty. The proposed formulation is deeply described and compared to the standard one. Many experiments are designed to validate the control scheme. In details, comparisons with benchmark PI controller are proposed and discussed, highlighting advantages and drawbacks of the two solutions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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