A Control Strategy of Actively Actuated Eccentric Mass System for Imbalance Rotor Vibration

This paper explores the new control strategy of an actively actuated eccentric mass system (AAEMS) for cancelling the rotor imbalance vibration. The AAEMS consists of an eccentric mass with an actuator that actively moves around the circular guided track attached to the rotating rotor thus can generate an effective centrifugal force perpendicular to any tangential direction of the guided circular trajectory. Therefore, once the magnitude and angular position of the inherited static imbalance of the rotor are identified, this actively controlled system can be dispatched to the target angular position(s) where the effective centrifugal force due to rotor imbalance is completely or partially removed. This novel device is currently available and widely used in the vibration isolation problem. However, the study of its control strategy is quite limited, thus, herein, we proposed a new possible control technique, guaranteeing both the robust vibration isolation performance and less control power consumption. To meet such needs, three primary functions of AAEMS are addressed here. First, two (Extended) Kalman filters were employed to sequentially estimate the unknown imbalance of the rotor and the unknown coulomb friction induced between the contact surface of the circular track and the counter-contacted parts of AAEMS. Second, the position control of the AAEMS is achieved by a linear quadratic regulator (LQR)-based optimal control law, simultaneously minimizing the imbalance vibration of the rotor as well as the power consumption of its own actuator. Third, for the situation where the estimation and control errors are presented, thus causing the failure to an acceptable threshold for imbalance vibration, the trial-error-based fine-tuning angular position control was proposed. The effectiveness of the proposed control strategy was evaluated via the simulations and this study shows the practical potential for addressing the AAEMS-based imbalance vibration elimination.

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Cogging Torque Reduction in Brushless Motors by a Nonlinear Control Technique

Energies ◽

10.3390/en12112224 ◽

2019 ◽

Vol 12 (11) ◽

pp. 2224 ◽

Cited By ~ 8

Author(s):

Pierpaolo Dini ◽

Sergio Saponara

Keyword(s):

Permanent Magnet ◽

Electric Drive ◽

Feedback Linearization ◽

Permanent Magnets ◽

Position Control ◽

Angular Position ◽

Control Technique ◽

Electrical Machine ◽

Brushless Motors ◽

Cogging Torque

This work addresses the problem of mitigating the effects of the cogging torque in permanent magnet synchronous motors, particularly brushless motors, which is a main issue in precision electric drive applications. In this work, a method for mitigating the effects of the cogging torque is proposed, based on the use of a nonlinear automatic control technique known as feedback linearization that is ideal for underactuated dynamic systems. The aim of this work is to present an alternative to classic solutions based on the physical modification of the electrical machine to try to suppress the natural interaction between the permanent magnets and the teeth of the stator slots. Such modifications of electric machines are often expensive because they require customized procedures, while the proposed method does not require any modification of the electric drive. With respect to other algorithmic-based solutions for cogging torque reduction, the proposed control technique is scalable to different motor parameters, deterministic, and robust, and hence easy to use and verify for safety-critical applications. As an application case example, the work reports the reduction of the oscillations for the angular position control of a permanent magnet synchronous motor vs. classic PI (proportional-integrative) cascaded control. Moreover, the proposed algorithm is suitable to be implemented in low-cost embedded control units.

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Vibration Suppression in Turning Using a Kalman Estimator–Based Feedforward Controller

10.1115/imece2000-2359 ◽

2000 ◽

Author(s):

Ameen H. ElSinawi ◽

Reza Kashani

Keyword(s):

Machine Tool ◽

Control Strategy ◽

Vibration Isolation ◽

Vibration Suppression ◽

Control Technique ◽

Tool Vibration ◽

Machine Tool Structure ◽

Transmitted Force ◽

Workpiece Interaction ◽

Machine Structure

Abstract Vibration suppression of the tool results in improved surface texture, dimensional accuracy and enhanced productivity in machining operations. The vibration of the machine tool structure, as well as the tool/workpiece interaction are the main contributors to the tool vibration. In light of this, abating the tool vibration can be approached by isolating the tool from the machine structure in the presence of a random process representing the tool/workpiece interaction. An active tool holder capable of isolating the cutting tool from the vibration of the machine tool structure is constructed. Vibration isolation is accomplished by means of a novel, Kalman estimator–based control strategy, a high bandwidth magnetostrictive actuator, and two accelerometers. The proposed control technique focuses on lowering the transmitted force to the tool that is subject to the machine structure vibration (base excitation) in presence of cutting process disturbance. The important aspect of this control strategy is that, while it is designed based on a full order model of the plant, its implementation is reduced to the realization of a second order estimator irrespective of the order of the plant model. Machining experiments showed that up to 30% improvement in surface roughness of the workpiece has been achieved using the proposed technique.

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Model-Based Design of an Improved Electric Drive Controller for High-Precision Applications Based on Feedback Linearization Technique

Electronics ◽

10.3390/electronics10232954 ◽

2021 ◽

Vol 10 (23) ◽

pp. 2954

Author(s):

Pierpaolo Dini ◽

Sergio Saponara

Keyword(s):

Embedded System ◽

Control Strategy ◽

Feedback Linearization ◽

Position Control ◽

Design Flow ◽

Control Technique ◽

End Effector ◽

Electrical Drives ◽

Advanced Control ◽

Physical Phenomena

This paper presents the design flow of an advanced non-linear control strategy, able to absorb the effects that the main causes of torque oscillations, concerning synchronous electrical drives, cause on the positioning of the end-effector of a manipulator robot. The control technique used requires an exhaustive modelling of the physical phenomena that cause the electromagnetic torque oscillations. In particular, the Cogging and Stribeck effects are taken into account, whose mathematical model is incorporated in the whole system of dynamic equations representing the complex mechatronic system, formed by the mechanics of the robot links and the dynamics of the actuators. Both the modelling procedure of the robot, directly incorporating the dynamics of the actuators and the electrical drive, consisting of the modulation system and inverter, and the systematic procedure necessary to obtain the equations of the components of the control vector are described in detail. Using the Processor-In-the-Loop (PIL) paradigm for a Cortex-A53 based embedded system, the beneficial effect of the proposed advanced control strategy is validated in terms of end-effector position control, in which we compare classic control system with the proposed algorithm, in order to highlight the better performance in precision and in reducing oscillations.

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Design of an Observer-Based Architecture and Non-Linear Control Algorithm for Cogging Torque Reduction in Synchronous Motors

Energies ◽

10.3390/en13082077 ◽

2020 ◽

Vol 13 (8) ◽

pp. 2077 ◽

Cited By ~ 1

Author(s):

Pierpaolo Dini ◽

Sergio Saponara

Keyword(s):

Control Algorithm ◽

Permanent Magnets ◽

Position Control ◽

Deterministic Model ◽

Initial Conditions ◽

Angular Position ◽

Observer Design ◽

Control Technique ◽

Servo Drive ◽

Cogging Torque

The problem of cogging torque is due to a magnetic behavior, intrinsic to synchronous machines and due to the presence of permanent magnets themselves. Cogging torque is a significant problem when the servo drive is used for applications where high precision in terms of position control is required. In this paper we present a method of cogging torque reduction by means of a control technique based on mathematical modeling of the cogging phenomenon itself in order to exploit this knowledge directly in the controller design. The mathematical model is inserted in the dynamic model of the synchronous machine in order to exploit the feedback linearization, providing an expression of the control law in which the contribution of the deterministic knowledge of the phenomenon is directly present. The cogging phenomenon physically depends on the angular position of the rotor, as well as the deterministic model we use to define the control vector. This makes it interesting and innovative to determine whether the control algorithm can be inserted within a sensor-less architecture, where rotor position and angular velocity measurements are not available. For this purpose, we present the use of an extended Kalman filter (EKF) in the continuous-time domain, discussing the advantages of an observer design based on a dynamic motor model in three-phase and direct-square axes. Results are presented through very accurate simulation for a trajectory-tracking problem, completing with variational analysis in terms of variation of initial conditions between EKF and motor dynamics, and in terms of parametric variation to verify the robustness of the proposed algorithm. Moreover, a computational analysis based on Simulink Profiler is proposed, which provides some indication for possible implementation on an embedded platform.

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Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton

Applied Bionics and Biomechanics ◽

10.1155/2016/5017381 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 13

Author(s):

Yi Long ◽

Zhi-jiang Du ◽

Wei-dong Wang ◽

Wei Dong

Keyword(s):

Sliding Mode Control ◽

Control Strategy ◽

Lower Limb ◽

Position Control ◽

Sliding Mode ◽

Angular Position ◽

Human Motion ◽

Cerebellar Model Articulation Controller ◽

Mode Control ◽

First Case

A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user’s intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA), and SMC with CMAC compensation (SMC_CMAC), all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems.

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A Proposal of Cyclic Sleep Control Technique for Backup Resources in ROADM Systems to Reduce Power Consumption of Photonic Network

IEICE Transactions on Communications ◽

10.1587/transcom.e97.b.2698 ◽

2014 ◽

Vol E97.B (12) ◽

pp. 2698-2705

Author(s):

Tomoyuki HINO ◽

Hitoshi TAKESHITA ◽

Kiyo ISHII ◽

Junya KURUMIDA ◽

Shu NAMIKI ◽

...

Keyword(s):

Power Consumption ◽

Control Technique ◽

Reduce Power Consumption ◽

Photonic Network ◽

Sleep Control

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Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning

Scientific Reports ◽

10.1038/s41598-021-90688-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Simon Tam ◽

Mounir Boukadoum ◽

Alexandre Campeau-Lecours ◽

Benoit Gosselin

Keyword(s):

Real Time ◽

Transfer Learning ◽

Control Strategy ◽

High Density ◽

Fine Tuning ◽

Mode Switching ◽

Correct Prediction ◽

Learning Approach ◽

Predictive Values ◽

Hand Prosthesis

AbstractMyoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

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