Load Torque Variations Research Articles

An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares

In this paper, an on-line parameter identification algorithm to iteratively compute the numerical values of inertia and load torque is proposed. Since inertia and load torque are strongly coupled variables due to the degenerate-rank problem, it is hard to estimate relatively accurate values for them in the cases such as when load torque variation presents or one cannot obtain a relatively accurate priori knowledge of inertia. This paper eliminates this problem and realizes ideal online inertia identification regardless of load condition and initial error. The algorithm in this paper integrates a full-order Kalman Observer and Recursive Least Squares, and introduces adaptive controllers to enhance the robustness. It has a better performance when iteratively computing load torque and moment of inertia. Theoretical sensitivity analysis of the proposed algorithm is conducted. Compared to traditional methods, the validity of the proposed algorithm is proved by simulation and experiment results.

Brushless DC motor control with unknown and variable torque load

This article presents the design of an Active Disturbance Rejection Controller (ADRC) for the permanent magnet Brushless DC motor (BLDC) to compensate the load torque variations in the rotor shaft, that does not requires the measurement of the rotor shaft speed. An additional advantage of this controller is that it is easy to implement and tune. A numerical simulation is included to validate the proposed scheme using the test emission cycle US06 that represents an aggressive high speed and high acceleration driving behavior with variable load torque for a commercial motor designed for electrical vehicles.

Mechanical Contact-Less Computational Speed Sensing Approach of PWM Operated PMDC Brushed Motor: A Slotting-Effect and Commutation Phenomenon Incorporated Semi-Analytical Dynamic Model-Based Approach

This brief proposes a mechanical contact-less speed sensing approach for a pulse width modulation (PWM) operated permanent-magnet direct current (PMDC) brushed motor. A recently reported semi-analytical dynamic model which incorporates the space-domain effects, namely slotting-effect and commutation phenomenon, has been taken into consideration to apply the proposed computational speed sensing approach. This speed sensing approach is basically an indirect estimation process and discrete in manner. The proposed method is efficiently applicable at higher range of speed. Zones of estimations with varying load torque and PWM duty cycle are represented with appropriate responses. A simulation of the proposed estimation method is applied over the dynamic semi-analytical model of 24 V, 12 teeth-slots, 100 W PMDC brushed motor, and various responses are represented in this brief.

Transmission Error Analysis and Disturbance Optimization of Two‐Stage Spur Gear Space Driven Mechanism with Large Inertia Load

For the nonlinear disturbance actual issues of the model space drive mechanism two‐stage spur gear system, a nonlinear dynamic model of 14‐DOF (degree of freedom) two‐stage spur gear with time‐varying stiffness and damping was established. This model has been developed previously by the authors to access the large inertia on the dynamic response of spur gear space driving mechanism, and its effectiveness was proved by a motion simulation experiment. In this paper, the profile error (PE) and the index error (IE) were enhanced in the dynamic model. The effects of profile error, index error, and variable load torque on transmission error (TE) were analyzed, while the optimization was proposed according to the analyzed result. The peak‐to‐peak value of the optimized load transmission error (LTE) was reduced by 60.7%, which improved the transmission accuracy and reduced the phenomenon of disturbance. The research of nonlinear dynamical model of two‐stage spur gear and the TE of the large inertia load were enriched, which provided an important reference for the actual design of the gear system.

Nested High Order Sliding Mode Controller Applied to a Brushless Direct Current Motor

A nested high order sliding mode controller is proposed for speed control of a brushless direct current motor using an extended park transformation. Conventional park transformation applied to non sinusoidal back electromotive force motors results in non constant values in the (d, q) reference frame, a modified park transformation is used to obtain constant variables after transformation. Sliding mode control is robust in the presence of matched parametric variations and uncertain disturbances but unmatched variations have a negative effect in the system’s performance. A nested high order sliding mode scheme is presented to improve controller robustness to unmatched perturbations. Simulations are used to show the performance of the proposed controller with load torque and resistance variations.

Evaluating transient performance of servo mechanisms by analysing stator current of PMSM

Abstract Smooth running and rapid response are the desired performance goals for the transient motions of servo mechanisms. Because of the uncertain and unobservable transient behaviour of servo mechanisms, it is difficult to evaluate their transient performance. Under the effects of electromechanical coupling, the stator current signals of a permanent-magnet synchronous motor (PMSM) potentially contain the performance information regarding servo mechanisms in use. In this paper, a novel method based on analysing the stator current of the PMSM is proposed for quantifying the transient performance. First, a vector control model is constructed to simulate the stator current behaviour in the transient processes of consecutive speed changes, consecutive load changes, and intermittent start-stops. It is discovered that the amplitude and frequency of the stator current are modulated by the transient load torque and motor speed, respectively. The stator currents under different performance conditions are also simulated and compared. Then, the stator current is processed using a local means decomposition (LMD) algorithm to extract the instantaneous amplitude and instantaneous frequency. The sample entropy of the instantaneous amplitude, which reflects the complexity of the load torque variation, is calculated as a performance indicator of smooth running. The peak-to-peak value of the instantaneous frequency, which defines the range of the motor speed variation, is set as a performance indicator of rapid response. The proposed method is applied to both simulated data in an intermittent start-stops process and experimental data measured for a batch of servo turrets for turning lathes. The results show that the performance evaluations agree with the actual performance.

A novel adaptive control method for induction motor based on Backstepping approach using dSpace DS 1104 control board

Abstract This paper presents a new adaptive Backstepping technique to handle the induction motor (IM) rotor resistance tracking problem. The proposed solution leads to improve the robustness of the control system. Given the presence of static error when estimating the rotor resistance with classical methods, and the sensitivity to the load torque variation at low speed, a new Backstepping observer enhanced with an integral action of the tracking errors is presented, which can be established in two steps. The first one consists to estimate the rotor flux using a Backstepping observer. The second step, defines the adaptation mechanism of the rotor resistance based on the estimated rotor-flux. The asymptotic stability of the observer is proven by Lyapunov theory. To validate the proposed solution, a simulation and experimental benchmarking of a 3 kW induction motor are presented and analyzed. The obtained results show the effectiveness of the proposed solution compared to the model reference adaptive system (MRAS) rotor resistance observer presented in other recent works.

A Fast Learning Neuro Adaptive Control of Buck Converter driven PMDC Motor: Design, Analysis and Validation

This paper presents a novel fast learning neural network for the estimation of load torque in PMDC motor. The control objective of angular velocity trajectory tracking is achieved by designing a controller for cascaded Buck converter PMDC motor system by utilizing an adaptive backstepping methodology augmented with a new Type-II Chebyshev neural network (CNN). The online learning laws for the neural network are developed, satisfying overall closed loop system stability using Lyapunov stability criterion. A rigorous stability analysis has been provided. Performance of the proposed control method is validated on a digital platform using dSPACE Control Desk DS1103 set-up with TM320F240 Digital Signal Processor. The dynamic response of Buck converter driven PMDC motor is examined for settling time, peak undershoot and overshoot guaranteeing the transient performance under conventional adaptive backstepping control and Type-I CNN based adaptive backstepping control techniques. Further, such results are compared with those obtained using the proposed method under start-up, wide range variations in load torque and reference trajectory.

A novel time varying dynamic modeling for hysteresis motor

Hysteresis motors are used in special applications such as gyroscope and gascentrifuge due to their unique features such as synchronism, self-starting, and developing smooth torque. Dynamic modeling of hysteresis motors is essential to the prediction of the transient performance, the study of the dynamic stability, the development of modern closed-loop control, and the estimation strategies. This paper develops a new time varying Dynamic model for a high-speed, circumferential- flux type hysteresis motor, in which the parameters of equivalent circuit of rotor's material are adjusted based on operational B-H loop. For this purpose and based on the elliptical assumption about B - H loops, the hysteresis lag angle, , is updated due to the applied stator voltage and available load torque. Developed mathematical model satis es many theoretical aspects of hysteresis motor behavior in transient and steady-state situations. The model off ers a tool to study the start-up of hysteresis motor, the change of stator voltage, the variation of load torque, the frequency tracking of variable-speed applications, and transient-state response to design parameters. Some simulations are provided to demonstrate the validity of developed model in Matlab/Simulink and are veri ed by some experimental results. The proposed results verify the advantages of this model over previous research works.

Sliding Mode Controller for Permanent Magnetic Synchronous Motors

Nowadays, the researches on permanent magnetic synchronous motors (PMSMs) as power source of electric vehicles (EVs) have obtained more and more attention. Torque control technology which is one of popular strategies applied in EVs raises higher standards for torque response speed and accuracy of PMSMs control system. To tract the reference torque quickly, a sliding mode torque controller which can decouple q-axis and d-axis currents is proposed in this paper. To eliminate the chattering phenomenon an exponential reaching law is adopted. The proposed method also can guarantee the robust control of PMSMs under model parameters (resistance, inductance) and load torque variations. Simulation results show the effectiveness and robustness of the proposed control approach.

Finite Set Model Predictive Control of Interior PM Synchronous Motor Drives With an External Disturbance Rejection Technique

This paper designs a predictive speed controller of interior permanent-magnet synchronous motor (IPMSM) based on finite control set (FCS)–model predictive control (MPC). The proposed predictive speed controller has a cascade-free structure that comprises a single predictive control function for the speed and dq- axis stator currents. The future rotor speed and stator currents are predicted at each sampling interval. Then, the proposed predictive control function is evaluated to determine the optimal switching states for the IPMSM drive. In high-performance speed-controlled drive, the unknown load torque disturbance has to be suppressed. Therefore, the load torque disturbance estimator using the two-stage extended Kalman filter (TSEKF) is proposed to enhance the dynamic performance of the IPMSM drive. The proposed TSEKF simultaneously estimates the system states, which greatly improve the model prediction process. The proposed predictive speed control is simple and compact, which demonstrates a faster transient response and a robust feature to mechanical system parameter variations when compared with the conventional cascaded speed control under a wide speed range and load torque variations. The experimental results on a prototype IPMSM drive built on a Texas Instruments TMS320F28335 floating point digital signal processing (DSP) board are presented considering the constant torque and flux-weakening regions to confirm the feasibility of the proposed FCS-MPC scheme.

Real time induction motor efficiency optimization

The induction machine efficiency optimization issue is the subject of many contributions in the literature. Few solutions, have shown interest in the rapidity-stability dilemma regarding the presence of sudden load torque variations. This paper presents a new useful hybrid approach for efficiency optimization of the direct vector-controlled induction motor drives. The efficiency improvement approach consists of adjusting the rotor flux with respect to the load torque in order to minimize total losses in the induction machine. The optimal rotor flux is defined in two steps. Firstly, the controller defines a suboptimal operating point by a fast analytical algorithm based on the model of induction machine. The suboptimal flux helps to minimize the size of possible solutions space of the optimization issue. The global optimal flux is achieved in the second step using a simulated annealing method. This latter adjusts the flux level in order to reduce the power input at its minimum. This choice overcomes the influence of parameters uncertainty on the controller stability. The proposed controller has been experimentally tested and validated on a 3 kW squirrel age induction motor.

The Stiffness Calculation and Optimization for the Variable Stiffness Load Torque Simulation System

This paper presents a novel design for a variable stiffness load torque simulation system. The system is applied to the load torque on a rudder in a real-time hardware-in-the-loop system (HILS). Compared with the traditional loading method, in which unavoidable additional torque exists, the variable stiffness loading system employs a “first decomposed and then coupled” approach to output the load torque and to significantly reduce the additional torque. Based on experimental data obtained from a wind tunnel test, a calculation method is proposed to determine the loading parameters of the variable stiffness loading system. Since the load stiffness is related to a variety of factors, the stiffness values obtained from wind tunnel test data, such as the fixed Mach number and the rudder deflection angle, are not definite values. By analyzing the influencing factors of the loading parameters, an optimal set of load stiffness is obtained using an optimization algorithm, and exact tracking of the load torque is achieved. Using the calculation method to obtain a loading torque for the rudder as an example, the torque tracking error is less than 0.05 Nm. The simulation results indicate that the proposed calculation method for variable stiffness loading is effective.

Design and implementation of a neuro-adaptive backstepping controller for buck converter fed PMDC-motor

A neuro-adaptive backstepping control (NABSC) method using single-layer Chebyshev polynomial based neural network is proposed for the angular velocity tracking in buck converter fed permanent magnet dc (PMDC)-motor. Owing to their universal approximation property, neural networks have been utilized for approximating the unknown nonlinear profile of instantaneous load torque. The inherent computational complexity of the neural network based adaptive scheme has been circumvented through the use of orthogonal Chebyshev polynomials as basis functions. A detailed stability and transient performance analysis has been conducted using Lyapunov stability criteria. The proposed control scheme is shown to yield a superior output performance with enhanced robustness for wide variations in load torque and set-point changes, compared to existing conventional approaches based on adaptive backstepping. The theoretical propositions are verified on an experimental prototype using dSPACE, Control Desk DS1103 setup with an embedded TM320F240 Digital Signal Processor proving its applicability to real-time electrical systems. The efficiency of the proposed strategy is quantified using performance measures and are evaluated against the conventional adaptive backstepping control (ABSC) methodology. Ultimately, this investigation confirms the effectiveness of the proposed control scheme in achieving an enhanced output transient performance while faithfully realizing its control objective in the event of abrupt and uncertain load variations.

Capability evaluation of four-leg DSTATCOM for compensating multifarious loads

A four-leg distribution static compensator (FL-DSTATCOM) has been investigated to achieve harmonic elimination, supply current balancing, compensation of reactive power and current in the neutral conductor of a three-phase, four-wire distribution system with various types of static, dynamic and combined loads. The capability of FL-DSTATCOM for compensating reactive power under dynamic condition is evaluated with induction machine having variable load torque. Also, the performance of FL-DSTATCOM is evaluated with combined load, which is a combination of dynamic and unbalanced static loads. The FL-DSTATCOM injects reactive and harmonics components of load current extracted using synchronous reference frame theory. With FL-DSTATCOM, the unbalanced, harmonic and reactive loads will appear to the main power supply as balanced, linear and unity power factor load. Hence, the main power supply would deliver only fundamental frequency positive sequence components of load current. The switching pulses are generated by employing hysteresis current controller. MATLAB/SIMULINK has been used for capability evaluation under multifarious loading conditions considering IEEE standard 519. The simulation results prove that the FL-DSTATCOM is capable of making the supply current as balanced and sinusoidal, maintaining the PF at point of common coupling near to unity and reducing the supply neutral current very close to zero.

Improvement of electronic line-shafting control in multi-axis systems

Electronic line-shafting (ELS) is the most popular control strategy for printing machines with shaftless drives. A slidingmode controller for tracking control is designed in this study as the first step towards an improved ELS control scheme. This controller can eliminate the negative effects on synchronization precision resulting from the friction at low speed present in the pre-registration step of a shaftless driven printing machine. Moreover, it can eliminate the synchronization error of the printing process resulting from nonlinearities and load disturbances. Based on observer techniques, the unknown components of load torque and system parameter variations are estimated. On this basis, a novel ELS control method using equivalent load-torque observers is proposed. Experimental results demonstrate the effectiveness of the proposed control system for four-axis position control.

Load disturbance observer‐based control method for sensorless PMSM drive

This study proposes a sensorless control method for the permanent magnet synchronous machine (PMSM) drive that is robust against load torque variations. The proposed method is based on the disturbance observer-based control (DOBC) method, and involves the use of a back electromotive force observer and a torque observer to estimate rotor position and compensate for load torque disturbance, respectively. This mechanism is simple to implement and requires no state vector derivation. The gains of the two observers are carefully selected to improve estimation accuracy and dynamic performance. The performance of the proposed load DOBC for sensorless PMSM drives was tested through a simulation, where the sensorless estimation error was considered to determine the adjustment laws for the proposed observer gains. Furthermore, results of evaluative experiments verified the effectiveness of the proposed method for high-performance sensorless control and torque disturbance rejection for the PMSM drive.

Speed Control of the Surface-Mounted Permanent-Magnet Synchronous Motor Based on Takagi–Sugeno Fuzzy Models

The speed control of the surface-mounted permanent-magnet synchronous motor (SPMSM) based on Takagi–Sugeno (T–S) fuzzy models is designed and implemented in this paper. In motor drive speed control, proportional-integral controller is generally applied. However, the tracking performance and load regulation capability should be compromised in the designing process of the controller. This study develops a speed controller based on the T–S fuzzy model with good tracking ability and load variation regulation. First, the dynamic model of an SPMSM is demonstrated and the load torque variation is treated as disturbances. Accordingly, the speed control strategy is developed based on the T–S fuzzy model. The defuzzification process and parallel distributed compensation are introduced. The ability of disturbance suppression and the Lyapunov stability of this system are established and analyzed. The controller gains are obtained via the linear matrix inequality toolbox. The speed control and all essential procedures are realized by the microcontroller Renesas RX62T. The tracking performance and load regulation capability are verified by the experimental results.

Precise PI speed control of permanent magnet synchronous motor with a simple learning feedforward compensation

This paper develops a precise proportional–integral (PI) type control system for repeatable tracking control of a permanent magnet synchronous motor (PMSM) under motor parameter and load torque variations. By adding a very simple learning feedforward term, a conventional PI control system can be enforced to have a perfect tracking performance under model parameter and load torque variations. The convergence and stability of the closed-loop control system response are analytically shown. Finally, the simulation and experimental results are given to verify the effectiveness of the proposed PI-type learning control law under the uncertainties such as motor parameter and load torque variations using a prototype PMSM drive system.

Comparison performance between sliding mode control and nonlinear control, application to induction motor

A squirrel cage induction motor has been the workhorse in industry for variable speed applications in a wide power range that covers from fractional horsepower to multi megawatts. But the control and estimation of ac drives in general are considerably more complex than those of dc drives. The advent of vector control techniques has partially solved induction motor control problems. However, these techniques are very sensitive to the variation of motor parameters, result in an undesirable coupling between the flux and the torque of the machine, and loss of dynamic performance. To solve these problems this paper presents a synthesis of two control strategies, for controlling speed and rotor flux of induction motor (IM) via nonlinear control (NLC) and sliding mode control (SMC). Computer simulations are carried out to show the robustness of the proposed method against rotor resistance and load torque variations. The performance of SMC has been successfully compared with nonlinear control.