486 research outputs found

    High Precision Positioning and Very Low Velocity Control of a Permanent Magnet Synchronous Motor

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    The purpose of this report is to evaluate a direct driven permanent magnet motor in high accuracy position and low speed operation. Actuation in this case is usually accomplished by stepping motors combined with belts and pulleys. High accuracy positioning is considered to be within 0.1 degrees and low speed 0.05 degrees per second, while at the same time have a 180 degree step response within 0.5 second. A model is derived of the motor along with methods for model parameter identification. This model is the basis for simulation of the motor in closed loop control. A prototype is developed in order to prove the validity of the results made by simulations. Experiments on the prototype resulted in two control methods, namely field oriented control and synchronous control. Conclusions drawn from the projects are as follows. The simulations do mirror the inherent problems with the permanent magnet motor. The prototype developed for the project is functioning and highly capable. Field oriented control was unable to meet the specified requirements. However, combined with iterative learning control the performance was improved significantly. Synchronous control satisfied most of the requirements, although its responsiveness and low efficiency are possible areas of improvement in future research

    High Precision Positioning and Very Low Velocity Control of a Permanent Magnet Synchronous Motor

    Get PDF
    The purpose of this report is to evaluate a direct driven permanent magnet motor in high accuracy position and low speed operation. Actuation in this case is usually accomplished by stepping motors combined with belts and pulleys. High accuracy positioning is considered to be within 0.1 degrees and low speed 0.05 degrees per second, while at the same time have a 180 degree step response within 0.5 second. A model is derived of the motor along with methods for model parameter identification. This model is the basis for simulation of the motor in closed loop control. A prototype is developed in order to prove the validity of the results made by simulations. Experiments on the prototype resulted in two control methods, namely field oriented control and synchronous control. Conclusions drawn from the projects are as follows. The simulations do mirror the inherent problems with the permanent magnet motor. The prototype developed for the project is functioning and highly capable. Field oriented control was unable to meet the specified requirements. However, combined with iterative learning control the performance was improved significantly. Synchronous control satisfied most of the requirements, although its responsiveness and low efficiency are possible areas of improvement in future research

    Control of robot-assisted gait trainer using hybrid proportional integral derivative and iterative learning control

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    An inexpensive exoskeleton of the lower limb was designed and developed in this study. It can be used as a gait trainer for persons with lower limb problems. It plays an essential role in lower limb rehabilitation and aid for patients, and it can help them improve their physical condition. This paper proposes a hybrid controller for regulating the lower limb exoskeleton of a robot-assisted gait trainer that uses a proportional integral and derivative (PID) controller combined with an iterative learning controller (ILC). The direct current motors at the hip and knee joints are controlled by a microcontroller that uses a preset pattern for the trajectories. It can learn how to monitor a trajectory. If the trajectory or load is changed, it will be able to follow the change. The experiment showed that the PID controller had the smallest overshoot, and settling time, and was responsible for system stability. Even if there are occasional interruptions, the tracking performance improves with the ILC

    Iterative learning control experimental results in twin-rotor device

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    This paper presents the results of applying the Iterative Learning Control algorithms to a Twin-Rotor Multiple-Input Multiple-Output System (TRMS) in order to achieve high performance in repetitive tracking of trajectories. The plant, which is similar to a prototype of helicopter, is characterized by its highly nonlinear and cross-coupled dynamics. In the first phase, the system is modelled using the Lagrangian approach and combining theoretical and experimental results. Thereafter, a hierarchical control architecture which combines a baseline feedback controller with an Iterative Learning Control algorithm is developed. Finally, the responses of the real device and a complete analysis of the learning behaviour are exposed.Postprint (published version

    High-performance torque control of switched reluctance motor

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    Ph.DDOCTOR OF PHILOSOPH

    Model-Guided Data-Driven Optimization and Control for Internal Combustion Engine Systems

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    The incorporation of electronic components into modern Internal Combustion, IC, engine systems have facilitated the reduction of fuel consumption and emission from IC engine operations. As more mechanical functions are being replaced by electric or electronic devices, the IC engine systems are becoming more complex in structure. Sophisticated control strategies are called in to help the engine systems meet the drivability demands and to comply with the emission regulations. Different model-based or data-driven algorithms have been applied to the optimization and control of IC engine systems. For the conventional model-based algorithms, the accuracy of the applied system models has a crucial impact on the quality of the feedback system performance. With computable analytic solutions and a good estimation of the real physical processes, the model-based control embedded systems are able to achieve good transient performances. However, the analytic solutions of some nonlinear models are difficult to obtain. Even if the solutions are available, because of the presence of unavoidable modeling uncertainties, the model-based controllers are designed conservatively

    Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

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    This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. Three separate low-cost SISO fuzzy controllers are employed in terms of first order discrete-time intelligent Proportional-Integral (PI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. Iterative Learning Control (ILC) system structures with PD learning functions are involved in the current iteration SISO ILC structures. Optimization problems are defined in order to tune the parameters of the learning functions. The objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic Slime Mould Algorithm (SMA). The experimental results prove the performance improvement of the SISO control systems after ten iterations of SMA

    Design and Optimal Control of a Magnet Assisted Scanning Stage for Precise and Energy Efficient Positioning

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    Scanning stages are characterized by repeated back and forth motions and are widely used in advanced manufacturing processes like photo-lithography, laser-scribing, inspection, metrology, 3D printing, and precision parts assembly, many of which are closely related to the semiconductor (i.e., integrated circuit) manufacturing industry. In order to deliver more high- performance semiconductor chips, i.e., to keep up with predictions made by Moore’s Law, the scanning stages employed by the industry need to move faster while maintaining nanometer-level precision. Achieving these two goals simultaneously requires extensive use of thermal and vibration-induced error mitigation methods, because the motors, and subsequently the surrounding stage components, become heated and flexible parts of scanning stages are easily excited by their aggressive motions (with high acceleration/deceleration). Most of the available solutions tackle the heat and vibration mitigation problems separately, even though the two problems originate from one source, i.e., the large inertial loads generated by the scanning stage’s actuators. Much benefit (e.g., size and cost reductions) can be achieved by considering the two problems simultaneously by addressing their root cause. This dissertation proposes a design-based approach to simultaneously mitigate thermal and vibration-induced errors of scanning stages. Exploiting the repeated back-and-forth motions of scanning, permanent magnet (PM) based assist devices are designed to provide assist force needed during the motion reversal portions of scanning trajectories. The PM-based assist devices store the kinetic energy of the moving table during deceleration and release the stored energy when the table accelerates. Consequently, the force requirements of the primary actuator decrease, thus lowering its heat generation due to copper (resistive) losses. Moreover, the reaction forces borne by the PM assistive devices are channeled to the ground, bypassing the vibration isolated base upon which the scanning stage rests, thus reducing unwanted vibration. To increase the force density of the PMs, a 2D Halbach arrangement is adopted in a prototype scanning stage. Moreover, an efficient and low-cost servo system, optimized for versatility, is integrated into the scanning stage for automatic positioning of the PMs. The designed magnet assisted scanning stage is an over-actuated system, meaning that it has more control inputs than outputs. For the best utilization of its actuators, a feedforward approach for optimal allocation of control efforts to its actuators is developed. The stage, controlled with the optimal feedforward control inputs, achieves significant reductions of actuator heat and vibration-induced errors when applied to typical scanning motions used in semiconductor manufacturing (silicon wafer processing). To further improve the positioning accuracy of the stage, an Iterative Learning Control (ILC) approach for over-actuated systems is developed, exploiting the repeated motion of scanning stages. The optimal ILC update law is designed, considering model and input force uncertainties, for robust monotonic convergence of tracking errors, and the resultant control force is efficiently allocated to multiple actuators. Applied to the magnet assisted scanning stage, the proposed ILC approach additionally reduces tracking errors arising from the mismatch between the model and actual system, thus significantly improving the positioning accuracy of the stage.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149847/1/yydkyoon_1.pd

    Resource-aware motion control:feedforward, learning, and feedback

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    Controllers with new sampling schemes improve motion systems’ performanc
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