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The electromagnetic torque and speed in Switched Reluctance Motor (SRM) greatly depend on the excitation parametersi.e. turn-on angle, turn-off angle, dwell angle and magnitude of the phase currents of its phases. At lower speeds, a change in the current contributes the torque requirement which can be achieved either by voltage control (pulse width modulation) or instantaneous current control techniques. At high speeds, due to high back EMF, the regulation of current is crucial and achieved with the control of switching angles of phases. This type of control is referred as average torque control, where the torque is averaged over one stroke (*2**π/N _{r}*). With constant dwell angle, advancing the phase angle influences the current into the phase winding at minimum inductance position. It has more time to get the current out of the phase winding before the rotor reaches the negative inductance slope. To maintain the speed of the motor at different load conditions, the turn-on and turn-off angles are adaptively varied. The change in dwell angle may be required where the turn-on and turn-off angle may not be sufficient to reach the required speed. In this paper, a new algorithm is proposed for self tuning of switching parameters of SRM. The proposed algorithm is simulated in MATLAB-Simulink and experimentally validated with Field Programmable Gated Array (FPGA) using MATLAB- system generator environment.

Switched Reluctance Motor (SRM) runs by reluctance torque. It has many advantages compared with other drives such as simple construction, low cost and highly fault tolerant. The torque-speed characteristics of the motor can be modified as per the requirement of the application starting from the design stage. High starting torque and extremely high speed are possible. DC motors are preferred for variable-speed applications whereas AC motors with constant frequency have been used for constant speed applications. In contrast, SRM has a wide range of constant torque and power regions.

The speed and average torque can be controlled by varying any one or indeed all of the switching parameters. The combinations of these depend on the performance requirements, permissible level of complexity, and cost. Attempts have been made to estimate these parameters [

A simple thumb rule is used to choose the turn-on angle. The position of rotor and turn-on angle was calculated with a phase lock loop system and a micro controller (Intel-8751) [

At certain scenario, the change in switching angle at run time degrades the transient response. To overcome this issue, off-line optimization of switching angles was developed [

At present the design of pumps and aerospace drives requires high speed and ultra high speed drives. Due to the complex structure of rotor, the stress on rotor at high speed and the demagnetization of magnetic materials under high temperature limits the application of permanent magnet motors to these applications. SRM has been the right candidate for the high speed application and can be made to run at ultra high speed. The feasibility of the ultra high speed for 1 kW, 6/2 SRM made by composite materials has been tested with the speed of 200,000 RPM in France [

In this paper a self tuning algorithm has been developed to vary the speed by varying the excitation parameters without high resolution position encoder. It has been validated in a 8/6 pole, 0.5 hp SRM and also tested in loaded condition. It uses the digital position sensors which changes its values for every 15˚ of rotor rotation. The proposed algorithm has been simulated in MATLAB and it is found to have good dynamic control of the motor. The same was experimented with Xilinx NEXYS-4 FPGA Board using MATLAB- system generator.

Following assumptions are made in order to facilitate qualitative development of algorithm.

1) The winding inductance is a linear function of rotor position.

2) Phase power switching devices are all ideal and switching is achieved instantaneously.

3) Power losses are ignored.

The rest of the paper is organized as follows: Section 2 presents description of the system and control principle. The proposed algorithm and techniques to achieve the desired control is discussed in the Section 3. The simulation of algorithm and the results are given in Section 4. The analysis and real time implementation of the algorithm with FPGA are presented in Section 5.

SRM is an electric motor in which torque is produced by its movable part to move to a position, where the inductance of the excited winding is maximum [

The phase windings of the motor are excited at the onset of increasing inductance, producing positive torque. Before the start of negative inductance profile, current must be commutated to avoid negative torque and hence torque ripples. The voltage equation of SRM can be written as [

The voltage Equation (1) consists of resistive voltage drop (

At high speeds in order to maintain the rated current, the power switches along with the phases are kept ON throughout the stroke. Now the chopping of voltage would reduce the average applied voltage and hence the current and torque. The peak flux linkage during the stroke is given as

If

Let

where

With the above relation, it is inferred that advancing the phase excitation (θ), will increase the current (

From (5), (6)

For constant

The turn-on and turn-off angles are the variables with respect to the speed of the motor and controller parameters. With increasing speed, fixed turn-on, turn-off angles may decrease electromagnetic torque and cause tail current, which leads to negative torque. To attain the maximum torque and efficiency, the switching parameters have to be adjusted proportional to speed. It is proposed that the calculation of turn ON angle of SRM as [

In [

where R_{a} and R_{u} are reciprocals of the aligned (L_{a}) and unaligned (L_{na}) inductances.

x is a constant (usually between

I is phase current (A);

The assumption of non-saturable inductance for the estimation of turn-off angle does not hold good for complete range of current [

The proposed algorithm is for 8/6 pole 4 phase SRM can be modified for any number of poles and phases. In this motor all the four phases has to be excited once in a stroke, with the stroke angle of

Consider the excitation of phase 3, as shown in

The maximum dwell angle for phase 3 is from

where

With a change in motor speed, the new value of K is calculated, by comparing the set speed with actual speed. If the difference is positive, the K value is decreased by 1%, else K increased by 1%. For the 8/6 pole SRM, 1% corresponds to 0.15˚. This procedure is repeated till the set speed is attained. It is expressed by Equation (9) as follows.

For higher value of K, phases will always be OFF, on the other hand for lesser value of K the phase will always be ON. Hence, the value of K is restricted within the range of 1/4th to 3/4th of K for better performance.

Flow ChartThe difference (e) between the set speed and actual speed is evaluated. The value of K is modified, the phase excitation signal (

The proposed algorithm for speed control is simulated with pre-determined excitation pattern as shown in

S0 | S1 | Phase to be excited |
---|---|---|

0 | 0 | PH1 (0˚ - 15˚) |

1 | 0 | PH2 (16˚ - 30˚) |

1 | 1 | PH3 (31˚ - 45˚) |

0 | 1 | PH4 (46˚ - 60˚) |

The practical sensor signals S_{0}, S_{1} has four different output states. It is modeled as per Expression (14)

With digital decoder, sensor outputs are mapped to the phase excitation as per Expression (15)

In the next stage, two adjacent phases have been ORed and passed through integrator. Output is compared with

From the actual speed the value of K is calculated and it is divided into 1/4K, 3/4K, to identify the safe region of operation. Finally the set speed is compared with actual speed and the error is generated. As per the algorithm the phase excitation signal is generated.

After building reasonable speed the switch has been closed to initiate the proposed speed control algorithm.

With change in set speed, the motor respond to the variations dynamically and it is shown in

The set speeds are 6000, 5000 and 5500. The generated PWM, and the comparison of K with integrated wave to increase and decrease the speed is shown in

The system responds to the variation of the set speed with respect to the higher and lower than actual speed.

Two primary platforms available for the implementation of the proposed speed control algorithm are FPGA and DSP. FPGAs are primary logic blocks connected by programmable wires to built unconventional processors.

Combination of these logic blocks can be designed to function as per the proposed algorithm. FPGA has the highest level of re-configurability which makes it suitable for this kind of application, than DSP.

MATLAB-System Generator Toolbox provides a way of Direct Real Time Simulation and Implementation (DRTSI) using a FPGA that provides lower level of abstraction and modular design.

The above block diagram shows the complete model of the real time implementation of the algorithm (

done as discussed in section III. The value of K within the

8/6 motor is used for testing the proposed speed control algorithm and the specifications given in appendix. The system has been tested for wide range of speed and supplying a load of 200 Newton meter/second. The experimental setup of the developed speed control algorithm is shown in

With the sensors signal (S0, S1) as ‘10’, phase 2 has been excited. The following scope waveform shows the same with the speed (rated) of 3000 RPM. When the speed is decreased to 1600 RPM, automatically the controller excites the phase at the appropriate time and it is shown in

Increasing the speed (up to 6000 RPM), the controller automatically advances the phase excitation and it is shown in

Real time implementation of developed speed control algorithm has been realized with Nexys-4 FPGA Board. The digital control issues are addressed and the solutions are as follows:

1) Signals from the position sensors are not of sufficient logic level. Hence to make it recognizable by Nexys-4 board, the signal is conditioned. For signal conditioning, Schmitt trigger with comparator circuit has been incorporated.

2) While calculating the speed, due to the high speed computation of FPGA (10 nano seconds per task), the unwanted glitches in the sensors output are misinterpreted as valid signal. The following

With the frequency of the glitch known, the appropriate filters (Low pass or High pass) can be incorporated in the conditioning circuit, which increases the complexity of the circuit. But the frequency of the glitches varies with speed.

With the known operating speed range of the motor, the sampling period has been varied, thereby the effect of the glitches are avoided. Here the full range of the speed has been taken as 1000 - 10,000 RPM.

In this paper, a speed control algorithm has been developed to change the switching parameters of SRM. A new technique has been presented for the phase advancement. The designed algorithm continuously monitors the speed and hence the change in load has also taken into consideration. The effectiveness of the proposed algorithm is validated with MATLAB-Simulink. At real time, the developed speed control algorithm has been implemented with Xilinx Nexys-4 FPGA board. The geometric data of the motor have not been used in the designed algorithm; hence it can be implemented with the motor of different ratings. Further the algorithm can be improved by introducing the torque sharing functions to minimize torque ripples.

P. Saravanan,R. Arumugam,M. Senthil Kumaran, (2016) FPGA Based Speed Control of SRM with Optimized Switching Angles by Self Tuning. Circuits and Systems,07,1530-1545. doi: 10.4236/cs.2016.78134