Motion Control Of Motor Research Articles

Inductance Measurement of the Switched Reluctance Motor with the Current Traversal Response for Exciting Voltage Pulse

The stator inductance of the Switched Reluctance Motor (SRM) is the essential factor for the motor motion control, torque control and energy-efficient driving. However, the inductance possesses strongly nonlinear characteristics against the rotor position and phase current, which poses challenges for accurate measurement of the inductance under varying current and ever-changing rotor position. A inductance measurement method, namely the current traversal response for exciting voltage pulse, is proposed on the basis of an SRM model. The novel approach exerts a pulse voltage on a given stator winding, measures the dynamic process of a current response, and calculates the inductance under the varying current. The proposed method has the advantage of simple measuring facilities, full-range data-acquiring under semi-real driving scenario, and it is free of modeling errors. These merits helps to obtain an accurate full-range inductance under different rotor position and phase current, which renders a parameterized 3-D mesh for the SRM stator inductance. Further a novel cross-validation experimentation according to the magnetizing and demagnetizing status measurement verifies the feasibilities and accuracy of the proposed inductance measuring method.

Optimum Efficiency Control of Traveling-Wave Ultrasonic Motor System

The operational efficiency of ultrasonic motor motion control systems is much lower than that of traditional electromagnetic motors, which badly restricts the application of ultrasonic motor in portable devices. Lower efficiency, robustness, and wear condition are the main problems of ultrasonic motor systems. This paper studies on the optimum efficiency control strategy of an ultrasonic motor system and selects the commercially used ultrasonic motor Shinsei USR60 as the experimental motor. To provide a basis for the optimum efficiency control, the control characteristic of system efficiency is studied first under speed closed-loop control condition. Then, a novel feedforward speed controller compounded with a pole-assignment controller is designed to compensate the disturbance caused by the efficiency optimization process. After that, the optimum efficiency control strategy with varying step length based on fuzzy reasoning is proposed. Experiments show that the proposed optimum control methods can greatly increase the operational efficiency of the ultrasonic motor system.

Research on FPGA Based Closed-Loop Motion Control System

This paper presents a Field Programmable Gate Array based closed-loop motion control system for stepper motors. It consists of three components, including closed-loop control unit, driving unit and feedback unit. To overcome some of the drawbacks with an open-loop stepper motor motion control system or a conventional servo system, a self adaptive algorithm is proposed. By detecting the difference between the command and feedback signals, measures are taken prior to the occurrence of loss synchronization. All of the control logic is implemented in one FPGA chip. Simulation and testing results are presented at the end of this paper.

Supervisory Interval Type-2 TSK Neural Fuzzy Network Control for Linear Microstepping Motor Drives With Uncertainty Observer

This paper proposes a supervisory interval type-2 Tagaki-Sugeno-Kang neural fuzzy network (IT2TSKNFN) control system for precision motion control of linear microstepping motor (LMSM) drives. The IT2TSKNFN incorporates interval type-2 fuzzy sets and TSK fuzzy reasoning into an NFN to handle uncertainties in LMSM drives, including payload variation, external disturbance, and sense noise. Based on the IT2TSKNFN, an uncertainty observer is first introduced to watch compound system uncertainties. Subsequently, an IT2TSKNFN-based controller is developed with robust hybrid control scheme, in which H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sup> approach and a supervisory controller are embedded to overcome the effects of unstructured uncertainties and reconstruction errors. The supervisory controller combines variable structure control and adaptive IT2TSKNFN control with different weights based on the tracking error. Moreover, projection-type adaptive algorithms that can tune parameters of the IT2TSKNFN online are derived from the Lyapunov synthesis approach; thus, the stability and robustness of the overall control system are guaranteed. Finally, the proposed control algorithms are realized within a TMS320VC33 DSP-based control computer. Simulated and experimental results of an LMSM drive are provided to verify the effectiveness of the proposed IT2TSKNFN control system.

Self-adaptive interval type-2 neural fuzzy network control for PMLSM drives

This paper proposes a self-adaptive interval type-2 neural fuzzy network (SAIT2NFN) control system for the high-precision motion control of permanent magnet linear synchronous motor (PMLSM) drives. The antecedent parts in the SAIT2NFN use interval type-2 fuzzy sets to handle uncertainties in PMLSM drives, including payload variation, external disturbance, and sense noise. The SAIT2NFN is firstly trained to model the inverse dynamics of PMLSM through concurrent structure and parameter learning. The fuzzy rules in the SAIT2NFN can be generated automatically by using online clustering algorithm to obtain a suitable-sized network structure, and a back propagation is proposed to adjust all network parameters. Then, a robust SAIT2NFN inverse control system that consists of the SAIT2NFN and an error-feedback controller is proposed to control the PMLSM drive in a changing environment. Moreover, the Kalman filtering algorithm with a dead zone is derived using Lyapunov stability theorem for online fine-tuning all network parameters to guarantee the convergence of the SAIT2NFN. Experimental results show that the proposed SAIT2NFN control system achieves the best tracking performance in comparison with type-1 NFN control systems.

Dynamic Fuzzy Modeling of Ultrasonic Motor Using Ant Colony Algorithm

The model of ultrasonic motor system is an important premise of designing motor motion controller. This paper works out fuzzy modeling method of ultrasonic motor system. Ant colony optimization and least square method are used to obtain the unknown parameters of membership functions and fuzzy rules, respectively. The two-input and one-output Takagi-Sugeno model is established, and the model can well show the nonlinear dynamic relationship of ultrasonic motor system.

Developing and Research on Pulse Motion Controller Based on SCM

As being the core of the machinery and electronic, motion controller is used in industry production widely. In this paper, a motor motion controller based on 16 bits SCM (Single Chip Microcomputer) is designed. Combining peripheral timers, this controller can control four-axes servo motors or stepping motors by sending pulse means. The experiment results have shown that the circuits design is feasible, and the system is stable and reliable, and the desired design objective has achieved. This motion controller can be used in numerical control machine tools, robots, automatic navigation dollies, and so on.

Integration of non-linear H ∞∞ and sliding mode control techniques for motion control of a permanent magnet synchronous motor

A new method using non-linear H ∞ and sliding mode control (SMC) is presented for a permanent magnet synchronous motor (PMSM) position control system. It is a high-performance controller in the presence of motor parameters variations and unknown load torque disturbances. In the proposed technique, the load torque is considered as an external disturbance and non-linear H ∞ is utilised to minimise its influence on the rotor position. Although H ∞ is to some extend robust against motor parameter variations, parameter variations may affect the performance of H ∞ controller, so SMC is used to reject these variations. Integration of these two methods offers an efficient robust position controller for PMSM. Position control of PMSM using the proposed method is compared with those obtained by other methods. Merits of the approach through simulations are verified by the experimental results.

Wavelet network-based motion control of DC motors

In this paper, a wavelet network is presented to design different controllers for DC motors based on the multi-resolution analysis and the wavelet transform. One of the basic advantages of wavelet network is that training is done using the recursive least square method which is suitable for online training usually required for adaptive control. The wavelet network is used to design adaptive speed controllers for a DC motor to achieve high performance speed control even if the motor model is unknown, the load characteristics are also unknown function of speed and the load torque changes online. Simulation and experimental results are presented to validate the effectiveness of the proposed controllers.

Self-Organizing CMAC Control for a Class of MIMO Uncertain Nonlinear Systems

This paper presents a self-organizing control system based on cerebellar model articulation controller (CMAC) for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. The proposed control system merges a CMAC and sliding-mode control (SMC), so the input space dimension of CMAC can be simplified. The structure of CMAC will be self-organized; that is, the layers of CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The control system consists of a self-organizing CMAC (SOCM) and a robust controller. SOCM containing a CMAC uncertainty observer is used as the principal controller and the robust controller is designed to dispel the effect of approximation error. The gradient-descent method is used to online tune the parameters of CMAC and the Lyapunov function is applied to guarantee the stability of the system. A simulation study of inverted double pendulums system and an experimental result of linear ultrasonic motor motion control show that favorable tracking performance can be achieved by using the proposed control system.

High accuracy motion control of linear motor drive wire-EDM machines

This paper aims to develop and investigate a permanent magnet linear-motor-driven table system for a wire-EDM machine. A dynamic model and system identification of `the linear motor system have been derived and analyzed. The linear motor drive system has nonlinear and time-varying behaviors because of the effect of irregular friction of the sliding surface and cogging force. Therefore, a conventional digital controller may not suffice to provide a high contouring accuracy as well as adequate disturbance rejection and parameter variation robustness. An indirect adaptive controller (IAC), combined with a neural network-based feedforward controller (NNBFC) is proposed to improve the contouring performance of the linear motor system. Experimental results not only indicate that the proposed control scheme can achieve a high contouring accuracy of ± 0.3 μm, they also demonstrate that the developed linear motor drive wire-EDM machine can meet the requirement for micro machining. The maximum contour error of circular trajectory at a feed-rate of 0.1 mm/min was significantly reduced from 8 μm to 0.5 μm in comparison with proportional-integral-derivative (PID) controller.

Development of Lyapunov-Based Genetic Algorithm Control for Linear Piezoelectric Ceramic Motor Drive

In general, the role of genetic algorithm (GA) is operated offline as a minor compensator or tuner in the control engineering because the systematic design and the latent stability problem of a GA-based control scheme are required to be solved. This paper originally designs a Lyapunov-based GA control (LGAC) scheme, and it applies for a practical control engineering example of the online motion control of a linear piezoelectric ceramic motor driven by a hybrid resonant inverter. In this control scheme, a GA control system via backstepping design technique is utilized to be the major controller, and adaptation laws derived from Lyapunov stability analyses are manipulated to adjust appropriate evolutionary steps. As a result, the system stability can be guaranteed directly without strict constraint conditions and detailed system knowledge. The effectiveness of the proposed drive and control system is verified by experimental results in the presence of uncertainties. From the measured results, the LGAC system performs superior high-precision motion control under wide operation range than conventional backstepping control system.

반 자율 무인 잠수정의 개발

This paper is mainly concerned with the development of the semi-autonomous underwater vehicle (SAUV). Underwater vehicles are affected by external disturbances due to the sea conditions such as currents and waves when it is performing various missions In this paper we present a design scheme of the SAUV system with mathematical models. Also. we present a control system including motion control of motors and main controller and a communication based on CAN method for interrelated control between the controllers and actuators.

Adaptive recurrent cerebellar model articulation controller for linear ultrasonic motor with optimal learning rates

In this study, an adaptive recurrent cerebellar model articulation controller (ARCMAC) is investigated for the motion control of linear ultrasonic motor (LUSM). The proposed ARCMAC has superior capability to the conventional cerebellar model articulation controller in efficient learning mechanism and dynamic response. The dynamic gradient descent method is adopted to online adjust the ARCMAC parameters. Moreover, the analytical method based on a Lyapunov function is proposed to determine the learning-rates of ARCMAC so that the stability of the system can be guaranteed. Furthermore, the variable optimal learning-rates are derived to achieve the fastest convergence of tracking error. Finally, the effectiveness of the proposed control system is verified by the experiments of LUSM motion control. Experimental results show that high-precision tracking response can be achieved by using the proposed ARCMAC.

A Globally Stable High-Performance Adaptive Robust Control Algorithm With Input Saturation for Precision Motion Control of Linear Motor Drive Systems

This paper focuses on the synthesis of nonlinear adaptive robust controller with saturated actuator authority for a linear motor drive system, which is subject to parametric uncertainties and uncertain nonlinearities such as input disturbances as well. Global stability with limited control efforts is achieved by breaking down the overall uncertainties to state-linearly-dependent uncertainties (such as viscous friction) and bounded nonlinearities (such as Coulomb friction, cogging force, etc.), and dealing with them via different strategies. Furthermore, a guaranteed transient performance and final tracking accuracy can be obtained by incorporating the well-developed adaptive robust control strategy and effective parameter identifier. Asymptotic output tracking is also achieved in the presence of parametric uncertainties only. Meanwhile, in contrast to the existing saturated control structures that are designed based on a set of transformed coordinates, the proposed saturated controller is carried out in the actual system states, which have clear physical meanings. This makes it much easier and less conservative to select the design parameters to meet the dual objective of achieving global stability with limited control efforts for rare emergency cases and the local high-bandwidth control for high performance under normal running conditions. Real-time experimental results are obtained to illustrate the effectiveness of the proposed saturated adaptive robust control strategy

Motion Control of Linear Induction Motor via Petri Fuzzy Neural Network

This paper focuses on the development of a Petri-fuzzy-neural-network (PFNN) control for an indirect field-oriented linear-induction-motor (LIM) drive. First, an indirect field-oriented mechanism for a LIM drive is derived to preserve the decoupling control characteristic. Then, the concept of a Petri net (PN) is incorporated into a traditional FNN (TFNN) to form a new type of PFNN framework for alleviating the computation burden. Moreover, the supervised gradient descent method is used to develop the online training algorithm for the PFNN. In order to guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the PFNN. With the proposed PFNN control system, the mover position of the controlled LIM drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. In addition, the effectiveness of the proposed control scheme is verified by both numerical simulations and experimental results. Furthermore, the superiority of the proposed PFNN control system is indicated in comparison with the TFNN control system

Intelligent motion control of linear ultrasonic motor with H ∞ tracking performance

As the dynamic characteristics of the linear ultrasonic motor (LUSM) are highly nonlinear and time varying, and the model difficult to obtain, it is difficult to design a suitable controller to achieve high-precision position control using conventional control techniques. An intelligent control system using an adaptive recurrent cerebellar model articulation controller (RCMAC) is proposed for the motion control of the LUSM. In this study, by adding feedback connections in the association memory space, the proposed dynamic structure of RCMAC has superior capability to the conventional static cerebellar model articulation controller in efficient learning mechanism and dynamic response. The control laws of the intelligent motion control system are derived on the basis of the H∞ control technique so that robust tracking performance can be achieved. By using the proposed intelligent motion control system, the LUSM control system possesses the advantages of high-precision tracking performance with robustness to system uncertainties. The effectiveness of the proposed control system is verified by hardware experiments of the LUSM motion control under the conditions of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with an integral-proportional position control system.

Robust adaptive motion control of permanent magnet linear motors based on disturbance compensation

Unlike rotational motors, permanent magnetic linear motors (PMLMs) are more sensitive to various force disturbances because of the reduction of gears. The parameters of PMLMs are difficult to accurately measure and vary in experiments and applications. To efficiently control a PMLM and alleviate the influence of parameter variation, robust adaptive control method is proposed to be used in the motion control of PMLMs. The force ripple is the main force disturbance when the velocity is close to zero, but it is complex and difficult to be described by an accurate model. So, in the designed robust adaptive controller, an off-line backpropagation neural network is proposed to approximate the function of the force ripple. Simulation and experimental results show that the designed robust adaptive controller can obtain higher control precision and be robust to the parameter variation.

Motion control of single F1-ATPase rotary biomolecular motor using microfabricated local heating devices

Biomolecular motors are major targets in single-molecule studies, which reveal molecular behaviors usually hidden in the emsemble- and time-averaging of bulk experiments. Methods for rapid experimental condition control during single-biomolecule observation are a key technology to elucidate the molecular mechanisms of proteins. One of the most promising methods is real-time rapid temperature alternation. A microheater and a microthermosensor were integrated on the glass plate for controlling the temperature locally; the maximum response speeds were 71.5 and 56.9K∕s for temperature rise and fall, respectively. Rapid temperature alternation with microfabricated thermodevice allowed rapid and reversible angular velocity control of a single F1-ATPase, a rotary biomolecular motor. The rapid control of the temperature enabled us to perform rotation assay at temperatures higher than that would “normally” denature them. This revealed that the torque of F1-ATPase seems to increase at higher temperatures with the increasing rate of 4% per 10°C. This method and knowledge for controlling the biomolecular motor can also be applied to future hybrid organic-inorganic nanosystems, which use biomolecular motors as nanoactuators.

Simultaneous Motion and Normal Forces Control of Flat Permanent Magnet Linear Synchronous Motors Employed as Actuators

This paper analyses the dynamic performance of flat single-sided Permanent Magnet Linear Synchronous Motors (PMLSMs) employed as actuators, when the alternate motion and the attractive or repulsive forces between the primary and the secondary are controlled simultaneously. The control of the normal forces when the motor is in dynamic operation provides multiples advantages, such as the reduction of friction forces and audible noise, and the minimization of mechanical stresses of guides and supports. Two PMLSM prototypes, one with a ferromagnetic primary core, and another with a non-magnetic armature, were tested under these special operation conditions. A modified Rotor Flux Oriented Control (RFOC) strategy was employed to carry out the tests, and several conclusions were taken. The main purposes of this study are to show the feasibility of the simultaneous control of motion and normal forces employing vector control techniques and to analyse the dynamic response of the prototypes.