Dariusz Janiszewski

Extended Kalman Filter Based Speed Sensorless PMSM Control with Load Reconstruction

This paper describes a study and experimental verification of sensorless control of permanent magnet synchronous motor. This structure bases on the extended Kalman filter theory using only the measurement of the motor current for on-line estimation of speed, rotor position and load torque reconstruction. Control structure such as Kalman filtering, in real time requires a very fast signal processor in special way, adapted to perform complex mathematical calculations. The digital signal processors have become cheaper and their performance greater. Without using position and torque sensors, it has become possible to apply described control structure as a cost-effective solution

Load torque estimation in sensorless PMSM drive using Unscented Kalmana Filter

This paper describes a study and experimental verification of sensorless control of Permanent Magnet Synchronous Motor. There are proposed novel estimation strategy based on the Unscented Kalman Filter, using only the measurement of the motor current for on-line estimation of speed, rotor position and load torque. Information about the load is important for complex drive control systems like robot arm. It is rarely obtained by estimation especially in sensorless systems. Used Kalman filter is an optimal state estimator and is usually applied to a dynamic system that involves a random noise environment. Control structure with Kalman filtering algorithm, in real time requires a very efficient signal processor, adapted to perform complex matrix calculations. Experimental results have been carried out to verify the effectiveness and applicability of the novel proposed estimation technique.

Load torque estimation for sensorless PMSM drive with output filter fed by PWM converter

The paper describes an load torque observer for sensorless control of permanent magnet synchronous motor, when the output voltage of the PWM inverter is filtered by an sinusoidal filter. The dynamics of the output filter are taken into account in the design of Unscented Kalman Filter. The use of the output filter does not require additional motor currents or voltages measurements. The speed and position are based on the estimation and are used for speed sensorless control, load torque signal is treated arbitrary as master robot control input. In that case the focus was state estimation rather than at the control at all. Laboratory results performed on 1,3kW PMSM shows the functionality of the proposed estimation and control method with estimation load torque presence.

Real-time control of drive with elestic coupling based on motor position measured only

In the paper the problems of active damping of torsional vibrations in small power high dynamic servo drives are presented. Only solutions with position sensor called encoder and without other sensor are taken into consideration. Real time control system is taken as controller which consist all elements of signal conditioning, acquisition, signal processing and actuators like PWM converters. The total delay time, produced by whole elements of controller is important factor of ability active control dumping vibrations into speed control loop. The aim of work is test the ability of simple LQR and Kalman filter speed control using only motor position sensor. It appeared that there is not necessary differentiation of position to receive and speed control. The given structure of controller was tested on laboratory setup and results are presented.

Unscented Kalman Filter for sensorless PMSM drive with output filter fed by PWM converter

The paper describes an observer for a permanent magnet synchronous motor when the output voltage of the PWM inverter is filtered by an sinusoidal filter. The dynamics of the output filter are taken into account in the design of the observer. The use of the output filter does not require additional motor currents or voltages measurements. The speed and position are based on the estimation and are used for speed sensorless control. In that case the focus was state estimation rather than at the control. Laboratory results performed on 1,3kW PMSM shows the functionality of the proposed estimation and control method.

Sensorless control of PMSM drive with state and load torque estimation

Purpose – The purpose of this paper is to obtain a fully sensorless permanent magnet synchronous motor (PMSM) drive control algorithm used for robot arm drive with load recognition. The paper shows how to use an extended Kalman filter (EKF) instead of sensors of the mechanical quantities as well as how to adapt the model of the PMSM to the filter procedures and aims at the real time application in the field‐oriented control (FOC) structure of the high‐dynamic drive.Design/methodology/approach – The synthesis of the control system is based on the method of the FOC, theory of the EKF and object description in the form of state equation with suitable choice of state vector. The adequate connection of these three methodologies is a core of the approach to design. First, the control algorithm was tested by means of simulation method then the real laboratory plant was built and investigated.Findings – Owing to task‐oriented formulation of the PMSM model, adequate organization of the EKF procedures and suitable ...

Disturbance estimation for sensorless PMSM drive with Unscented Kalman Filter

This paper describes a study and experimental verification of sensorless control of Permanent Magnet Synchronous Motor in mechatronics application. There are proposed novel estimation strategy based on the Unscented Kalman Filter, using only the measurement of the motor current for on-line estimation of speed, rotor position and disturbance - load torque. Information about the load is important for complex drive control systems like robot arm. It is seldom obtained by estimation way especially in sensorless systems. Used Kalman filter is an optimal state estimator and is usually applied to a dynamic system that involves a random noise environment. Control structure with unscented algorithm, in real time requires a very efficient signal processor. Experimental results have been carried out to verify the effectiveness and applicability of the novel proposed estimation technique.

Gearless Pedaling Electric Driven Tricycle

In the paper the general conception and real construction of a three-wheel bicycle with auxiliary electric propulsion is presented. Each of the vehicle wheels is driven by an electric motor (BLDC) controlled by the central system of power distribution. The generator that charges a battery pack is driven by pedals. The vehicle speed is proportional with adjustable ratio to the pedaling velocity, whereas the power delivered to the motors is appropriately increased to the pedaling power with adjustable degree. Description and analysis of the selected modules of the vehicle are documented with prototype research results.

Control of multi-mass system by on-line trained neural network based on Kalman filter

The purpose of this paper is to obtain on-line trained Artificial Neural Network Controller for PMSM multi-mass high dynamic drive. Structure of the controller with training algorithm and idea of Kalman Filter as observer are shortly described. The Resilient Back Propagation algorithm (RPROP) was chosen for ANN training process. There is assumed rotor position can be sufficient to the possibility of torsional vibration damping. The Neural Network Controller has been proposed instead classical form of control loop with speed sensor. The problem of controller synthesis is discussed and solved. The main advantage of proposed system is Kalman Filter algorithm using to obtain all necessary signals for ANN controller. The measurement of motor position is enough for good control strategy. The proposed control scheme guarantee good properties in scope of mechanical parameter changing. The speed response is parameters nearly independent. The estimation scheme and controller was tested on a single drive setup under its mechanical elements changing. There are presented an original combination of Artificial Intelligence method with classical form of mathematical filters. We proved that newest control structures work best with known behaviour of classical observers theory.