41,713 research outputs found
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
Active-Flux-Based Motion-Sensorless Control of PMSM Using Moving Horizon Estimator
No full textThis paper deals with an 'active flux' model-based approach for state estimation of Permanent Magnet Synchronous Motors to build up sensorless drives. The active-flux vector is aligned to the rotor d-axis for all synchronous machines. In this way, the rotor position and speed observer seems more amenable to a wide speed range, with smaller dynamic errors. A Moving Horizon Estimation algorithm, an optimization-based scheme that yields good performance, is applied for the speed and rotor position estimation. Under assumptions, an optimal problem of Equality Constrained Quadratic Programming type has been solved each iteration. The algorithm has been efficiently implemented and tested for both Surface and Interior Permanent Magnet Synchronous Motors, demonstrating the real-time feasibility the proposed approach at 10 kHz sampling rate
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
Maximization of Sensorless Capabilities of Hybrid Excited Permanent Magnet Motors
No full textThis paper proposes an improved control strategy for hybrid excited permanent magnet (HEPM) motors in low-speed sensorless operating condition. In low speed region, the rotor position is retrieved by exploiting the rotor anisotropy via high frequency (HF) voltage signals. Non-zero cross-differential inductances induce an estimation error which could lead lower performance and instability issues. The proposed method exploits the excitation current of HEPM motor to reduce or even nullify the estimation error. The operating trajectory for stator and excitation currents is obtained via a two-step optimization procedure. The algorithm aim is the minimization of the position estimation error and the maximization of motor efficiency, by respecting the motor constraints. Simulation results validate the effectiveness in the position error reduction. Moreover, the efficiency of the drive under the proposed control strategy is compared to the benchmark maximum-torque-per-ampere policy
Motor Parameter-Free Predictive Current Control of Synchronous Motors by Recursive Least-Square Self-Commissioning Model
No full textThis article deals with a finite-set model predictive current control in synchronous motor drives. The peculiarity is that it does not require the knowledge of any motor parameter. The inherent advantage of this method is that the control is self-adapting to any synchronous motor, thus easing the matching between motor and inverter coming from different manufacturers. Overcoming the flaws of the existing lookup table based parameter-free techniques, the article elaborates the past current measurements by a recursive least-square algorithm to estimate the future behavior of the current in response to a finite set of voltage vectors. The article goes through the mathematical basis of the algorithm till a complete set of experiments that prove the feasibility and the advantages of the proposed technique
Data-Driven Continuous-Set Predictive Current Control for Synchronous Motor Drives
No full textOptimization-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
Sensorless Motor Parameter-Free Predictive Current Control of Synchronous Reluctance Motor Drives
No full textSynchronous reluctance motors represent an emerging technology in general purpose drives. However, a strongly nonlinear behavior characterizes the electrical dynamics of such machines, making the design of the control architecture challenging. Parameter-free model predictive control is becoming a relevant alternative to standard control techniques. Its key feature is the capability of self-adapting to any operating point. Sensorless operation is a feature commonly requested to general purpose drives. However, parameter-free sensorless control has not been investigated yet. This paper proposes a framework to implement a sensorless parameter-free scheme, inspired to the saliency tracking concept. Some relevant issues of state-of-the-art control architectures, mainly due to the magnetic cross saturation, are addressed. The performances of the proposed scheme and the effectiveness of the developed solution to address the magnetic cross saturation are validated by means of simulations on a synchronous reluctance motor
A Review about Flux-Weakening Operating Limits and Control Techniques for Synchronous Motor Drives
No full textThis paper deals with motor design aspects and control strategies for the flux-weakening (FW) operation of synchronous motors. The theory of FW is described by taking into account different control schemes. The advantages and drawbacks of each one are discussed, as well. Moreover, some motor design considerations for achieving an effective FW operation are illustrated for permanent magnet (PM), wound rotor (WR) and reluctance (REL) synchronous machines, using the per unit approach. The distinguishing characteristic of this review provides two-fold attention on both machine design and control strategies obtained by several collaborations with industrial and commercial companies
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
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