Robust sensorless load angle control for stepping motors

In industry, the bulk of the stepping motors is driven in open loop full-step mode with maximum current to avoid step loss. This results in noisy operation due to torque ripples and a poor energy-efficiency. To tackle these problems the current current level at which the stepping motor is driven can be reduced to an optimal level. In this paper, a sensorless load angle controller is proposed and implemented to optimise the drive current level. However, reducing the current level results in a diminished torque margin for load disturbances. In this paper, a countermeasure to enhance the robustness of the sensorless load angle controller against torque disturbances is proposed and assessed trough measurements

DSP-Based Field-Oriented Step Motor Control

The SMC3 motor drive has been built using an Analog Devices ADSP-2101 digital signal processor (DSP). The SMC3 is designed to work with two-phase step motors, which are permanent magnet motors with many (typically 100) poles. The firmware in the SMC3 DSP drives the step motor phase windings using field-oriented control rather than using single steps. This method derives the maximum theoretical performance from the motor. This paper describes field-oriented control, and how the SMC3 hardware and firmware implements it

DSP-Based Field-Oriented Step Motor Control

The SMC3 motor drive has been built using an Analog Devices ADSP-2101 digital signal processor (DSP). The SMC3 is designed to work with two-phase step motors, which are permanent magnet motors with many (typically 100) poles. The firmware in the SMC3 DSP drives the step motor phase windings using field-oriented control rather than using single steps. This method derives the maximum theoretical performance from the motor. This paper describes field-oriented control, and how the SMC3 hardware and firmware implements it

Investigation Of Different Rules Size FLSC Performance Applied To Induction Motor Drive

Fuzzy Logic Controller (FLC) has been widely used in speed controller due to its superior performance results. It is suitable when the system is difficult to model mathematically due to its nonlinearity and complexity. There are three common number of rules design which are commonly used in FLSC known as 49, 25 and 9 rules. However, the majority of the previous research report mainly focused on the dedicated rules size design either 49, 25 or 9 rules for the optimum performance. There is lack of performance comparison between 49, 25 and 9 rules size. Thus, it is difficult to understand how the rules size affects the motor performance. This research tries to fill up the gap by comparing the controller performance using the same platform. The fuzzy logic speed controllers (FLSC) with a different type of rules base are applied to the induction motor drive system. The FLSC with 49, 25 and 9 rules are investigated through MATLAB/SIMULINK and performance comparisons are made covering a wide speed range operations and load disturbance. The simulation results are evaluated based on the rise time

Investigation of Different Rules Size FLSC Performance Applied to Induction Motor Drive

Fuzzy Logic Controller (FLC) has been widely used in speed controller due to its superior performance results. It is suitable when the system is difficult to model mathematically due to its nonlinearity and complexity. There are three common number of rules design which are commonly used in FLSC known as 49, 25 and 9 rules. However, the majority of the previous research report mainly focused on the dedicated rules size design either 49, 25 or 9 rules for the optimum performance. There is lack of performance comparison between 49, 25 and 9 rules size. Thus, it is difficult to understand how the rules size affects the motor performance. This research tries to fill up the gap by comparing the controller performance using the same platform. The fuzzy logic speed controllers (FLSC) with a different type of rules base are applied to the induction motor drive system. The FLSC with 49, 25 and 9 rules are investigated through MATLAB/SIMULINK and performance comparisons are made covering a wide speed range operations and load disturbance. The simulation results are evaluated based on the rise time (Tr), overshoot (OS), settling time (Ts), Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE) for transient and steady state condition. It is shown that the smaller size of rules does not necessarily degrade the performance

Development of a Power Assist Lifting Device With a Fuzzy PID Speed Regulator

This paper introduces the development of a one-degree-of-freedom (1DOF) power assist device that helps to lift objects and facilitate the operator's job. The existing designs were examined for different control approaches and human-robot cooperation intuitiveness. The project involves the mechanical design of the experimental setup and development of advantageous control system. Since a task for the device is highly dependent on the mass of handling object, an adaptive strategy is a major concern of control system design. The controller design is represented by two loops to control admittance and velocity. To reduce the response time of the device, two velocity controllers are designed and compared with the embedded one. The first is a conventional proportional-integral-derivative controller which has shown better performance than the native controller. The second is derived from the first using fuzzy logic for better handling of different manipulation scenarios. The results illustrate that a faster response of the device can be achieved using a fuzzy logic controller due to the nonlinear nature that allows adapting to changes in velocity error and applied load

Experimental Investigation Of Different Rules Size Of Fuzzy Logic Controller For Vector Control Of Induction Motor Drives

There is lack of performance comparison investigation between 49,25 and 9 rules size speed controller of Induction Motor (IM) drives of the rules size toward the motor performance. Furthermore, no study was conducted based on the computation burden time affects by the rules experimentally fuzzy rules sizes in terms of performance based on simulations and experimental analysis as well as the execution time.MATLAB/SIMULINK and dSPACE DSl104 controller platform are used for the analysis. Variation in performance with the shape and number of membership functions.Based on the experimental results,it can be concluded that,higher number of rules increase the Computational Time (CT), hence bigger sampling time is required which will There is lack of performance comparison investigation between 49,25 and 9 rules size speed controller of Induction Motor (IM) drives.Thus,it is difficult to understand the effect of the rules size toward the motor performance.Furthermore, o study was conducted based on the computation burden time affects by the rules experimentally.This paper compares the fuzzy rules sizes in terms of performance based on simulations and experimental analysis as well as the execution time. MATLAB/SIMULINK and dSPACE DSl104 controller platform are used for the analysis.Variation in performance with different rule size may occur due to the shape and number of membership functions.Based on the experimental results,it can be concluded that,higher number of rules increase the Computational Time (CT),hence bigger sampling time is required which will affect the performance.There is lack of performance comparison investigation between 49,25 and 9 rules sizefor the. Thus, it is difficult to understand the effect of the rules size toward the motor performance.Furthermore,no study was conducted based.This paper compares the fuzzy rules sizes in terms of performance based on simulations and experimental analysis as well as the execution time. MATLAB/SIMULINK and dSPACE DSl104 controller platform different rule size may occur due to the shape and number of membership functions.Based on the experimental results, it can be concluded that, higher number of rules increase the Computational Time (CT),hence bigger computational time (CT)

Fuzzy Membership Functions Tuning For Speed Controller Of Induction Motor Drive: Performance Improvement.

Fuzzy logic controller (FLC) has gained high interest in the field of speed control of machine drives in both academic and industrial communities. This is due to the features of FLC of handling non-linearity and variations. FLC system consists of three main elements: scaling factors (SFs), membership functions (MFs), and rule-base. Fuzzy MFs can be designed with different types and sizes. For induction motor (IM) speed control, (3x3), (5x5) and (7x7) MFs are the most used MFs sizes, and normally designed based on symmetrical distribution. However, changing the width and peak position of MFs design enhance the performance. In this paper, tuning of MFs of FLC speed control of IM drives is considered. Considering (3x3), (5x5) and (7x7) MFs sizes, the widths and peak positions of these MFs are asymmetrically distributed to improve the performance of IM drive. Based on these MFs sizes, the widths and peak positions are moved toward the origin (zero), negative and positive side that produces a controller less sensitive to the small error variations. Based on simulation and performance evaluations, improvement of 5% in settling time (Ts), 0.5% in rise time and 20% of steady-state improvement achieved with the tuned MFs compared to original MFs