The Experts below are selected from a list of 6657 Experts worldwide ranked by ideXlab platform
Taro Nakamura - One of the best experts on this subject based on the ideXlab platform.
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Novel Feedforward Controller for straight-fiber-type artificial muscle based on an experimental identification model
2018 IEEE International Conference on Soft Robotics (RoboSoft), 2018Co-Authors: Ryuji Suzuki, Manabu Okui, Shingo Iikawa, Yasuyuki Yamada, Taro NakamuraAbstract:This paper reports on an improvement to a Feedforward Controller for a straight-fiber-type artificial muscle that can control the amount of contraction, stiffness, and contraction force by use of an experimental identification model. This straight-fiber-type artificial muscle has a higher contraction force and a higher contraction rate than a McKibben artificial muscle. In a previous study, we developed a Feedforward Controller for a straight-fiber-type artificial muscle based on a mechanical model. However, this Controller could not accurately control the stiffness or the contraction force. A feedback Controller was necessary to compensate for the lack of Feedforward control accuracy, which increased the system complexity. In addition, the calculations of the previous Controller were so complex that the microController could not keep up with the sequential calculations. This is not practical when the Controller is used in devices such as an assist suit. In this paper, to solve these problems, we propose a novel Feedforward Controller based on an experimental identification model whose calculations are simpler than the previous ones. An experimental identification model enables the Feedforward Controller to improve the accuracy by identifying the parameters used in the model. Also, we compare the accuracy of the proposed Controller with the previous one.
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RoboSoft - Novel Feedforward Controller for straight-fiber-type artificial muscle based on an experimental identification model
2018 IEEE International Conference on Soft Robotics (RoboSoft), 2018Co-Authors: Ryuji Suzuki, Manabu Okui, Shingo Iikawa, Yasuyuki Yamada, Taro NakamuraAbstract:This paper reports on an improvement to a Feedforward Controller for a straight-fiber-type artificial muscle that can control the amount of contraction, stiffness, and contraction force by use of an experimental identification model. This straight-fiber-type artificial muscle has a higher contraction force and a higher contraction rate than a McKibben artificial muscle. In a previous study, we developed a Feedforward Controller for a straight-fiber-type artificial muscle based on a mechanical model. However, this Controller could not accurately control the stiffness or the contraction force. A feedback Controller was necessary to compensate for the lack of Feedforward control accuracy, which increased the system complexity. In addition, the calculations of the previous Controller were so complex that the microController could not keep up with the sequential calculations. This is not practical when the Controller is used in devices such as an assist suit. In this paper, to solve these problems, we propose a novel Feedforward Controller based on an experimental identification model whose calculations are simpler than the previous ones. An experimental identification model enables the Feedforward Controller to improve the accuracy by identifying the parameters used in the model. Also, we compare the accuracy of the proposed Controller with the previous one.
C. N. Riviere - One of the best experts on this subject based on the ideXlab platform.
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Feedforward Controller of ill conditioned hysteresis using singularity free prandtl ishlinskii model
IEEE-ASME Transactions on Mechatronics, 2009Co-Authors: W. T. Latt, Cheng Yap Shee, C. N. RiviereAbstract:Piezoelectric, magnetostrictive, and shape memory alloy actuators are gaining importance in high-frequency precision applications constrained by space. Their intrinsic hysteretic behavior makes control difficult. The Prandtl-Ishlinskii (PI) operator can model hysteresis well, albeit a major inadequacy: the inverse operator does not exist when the hysteretic curve gradient is not positive definite, i.e., ill condition occurs when slope is negative. An inevitable tradeoff between modeling accuracy and inversion stability exists. The hysteretic modeling improves with increasing number of play operators. But as the piecewise continuous interval of each operator reduces, the model tends to be ill-conditioned, especially at the turning points. Similar ill-conditioned situation arises when these actuators move heavy loads or operate at high frequency. This paper proposes an extended PI operator to map hysteresis to a domain where inversion is well behaved. The inverse weights are then evaluated to determine the inverse hysteresis model for the Feedforward Controller. For illustration purpose, a piezoelectric actuator is used.
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Feedforward Controller of Ill-Conditioned Hysteresis Using Singularity-Free Prandtl–Ishlinskii Model
IEEE ASME Transactions on Mechatronics, 2009Co-Authors: W. T. Latt, Cheng Yap Shee, C. N. RiviereAbstract:Piezoelectric, magnetostrictive, and shape memory alloy actuators are gaining importance in high-frequency precision applications constrained by space. Their intrinsic hysteretic behavior makes control difficult. The Prandtl-Ishlinskii (PI) operator can model hysteresis well, albeit a major inadequacy: the inverse operator does not exist when the hysteretic curve gradient is not positive definite, i.e., ill condition occurs when slope is negative. An inevitable tradeoff between modeling accuracy and inversion stability exists. The hysteretic modeling improves with increasing number of play operators. But as the piecewise continuous interval of each operator reduces, the model tends to be ill-conditioned, especially at the turning points. Similar ill-conditioned situation arises when these actuators move heavy loads or operate at high frequency. This paper proposes an extended PI operator to map hysteresis to a domain where inversion is well behaved. The inverse weights are then evaluated to determine the inverse hysteresis model for the Feedforward Controller. For illustration purpose, a piezoelectric actuator is used.
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Adaptive rate-dependent Feedforward Controller for hysteretic piezoelectric actuator
2008 IEEE International Conference on Robotics and Automation, 2008Co-Authors: F. Widjaja, W. T. Latt, K. C. Veluvolu, C. Y. Shee, C. N. RiviereAbstract:With the increasing popularity of actuators involving smart materials like piezoelectric, control of such materials becomes important. The existence of the inherent hysteretic behavior hinders the tracking accuracy of the actuators. To make matters worse, the hysteretic behavior changes with rate. One of the suggested ways is to have a Feedforward Controller to linearize the relationship between the input and output. Thus, the hysteretic behavior of the actuator must be first modeled by sensing the relationship between the input voltage and output displacement. Unfortunately, the hysteretic behavior is dependent on individual actuator and also environmental conditions like temperature. In this fast moving world, time is money and it is very costly to model the hysteresis regularly. In addition, the hysteretic behavior of the actuators also changes with age. Base on the studies done on the phenomena hysteretic behavior with rate, this paper proposes an adaptive rate-dependent Feedforward Controller with Prandtl-Ishlinskii (PI) hysteresis operators for piezoelectric actuators. This adaptive Controller is achieved by adapting the coefficients to manipulate the weights of the play operators. Actual experiments are conducted to demonstrate the effectiveness of the adaptive Controller.
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ICRA - Adaptive rate-dependent Feedforward Controller for hysteretic piezoelectric actuator
2008 IEEE International Conference on Robotics and Automation, 2008Co-Authors: F. Widjaja, W. T. Latt, K. C. Veluvolu, C. Y. Shee, C. N. RiviereAbstract:With the increasing popularity of actuators involving smart materials like piezoelectric, control of such materials becomes important. The existence of the inherent hysteretic behavior hinders the tracking accuracy of the actuators. To make matters worse, the hysteretic behavior changes with rate. One of the suggested ways is to have a Feedforward Controller to linearize the relationship between the input and output. Thus, the hysteretic behavior of the actuator must be first modeled by sensing the relationship between the input voltage and output displacement. Unfortunately, the hysteretic behavior is dependent on individual actuator and also environmental conditions like temperature. In this fast moving world, time is money and it is very costly to model the hysteresis regularly. In addition, the hysteretic behavior of the actuators also changes with age. Base on the studies done on the phenomena hysteretic behavior with rate, this paper proposes an adaptive rate-dependent Feedforward Controller with Prandtl-Ishlinskii (PI) hysteresis operators for piezoelectric actuators. This adaptive Controller is achieved by adapting the coefficients to manipulate the weights of the play operators. Actual experiments are conducted to demonstrate the effectiveness of the adaptive Controller.
Ryuji Suzuki - One of the best experts on this subject based on the ideXlab platform.
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Novel Feedforward Controller for straight-fiber-type artificial muscle based on an experimental identification model
2018 IEEE International Conference on Soft Robotics (RoboSoft), 2018Co-Authors: Ryuji Suzuki, Manabu Okui, Shingo Iikawa, Yasuyuki Yamada, Taro NakamuraAbstract:This paper reports on an improvement to a Feedforward Controller for a straight-fiber-type artificial muscle that can control the amount of contraction, stiffness, and contraction force by use of an experimental identification model. This straight-fiber-type artificial muscle has a higher contraction force and a higher contraction rate than a McKibben artificial muscle. In a previous study, we developed a Feedforward Controller for a straight-fiber-type artificial muscle based on a mechanical model. However, this Controller could not accurately control the stiffness or the contraction force. A feedback Controller was necessary to compensate for the lack of Feedforward control accuracy, which increased the system complexity. In addition, the calculations of the previous Controller were so complex that the microController could not keep up with the sequential calculations. This is not practical when the Controller is used in devices such as an assist suit. In this paper, to solve these problems, we propose a novel Feedforward Controller based on an experimental identification model whose calculations are simpler than the previous ones. An experimental identification model enables the Feedforward Controller to improve the accuracy by identifying the parameters used in the model. Also, we compare the accuracy of the proposed Controller with the previous one.
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RoboSoft - Novel Feedforward Controller for straight-fiber-type artificial muscle based on an experimental identification model
2018 IEEE International Conference on Soft Robotics (RoboSoft), 2018Co-Authors: Ryuji Suzuki, Manabu Okui, Shingo Iikawa, Yasuyuki Yamada, Taro NakamuraAbstract:This paper reports on an improvement to a Feedforward Controller for a straight-fiber-type artificial muscle that can control the amount of contraction, stiffness, and contraction force by use of an experimental identification model. This straight-fiber-type artificial muscle has a higher contraction force and a higher contraction rate than a McKibben artificial muscle. In a previous study, we developed a Feedforward Controller for a straight-fiber-type artificial muscle based on a mechanical model. However, this Controller could not accurately control the stiffness or the contraction force. A feedback Controller was necessary to compensate for the lack of Feedforward control accuracy, which increased the system complexity. In addition, the calculations of the previous Controller were so complex that the microController could not keep up with the sequential calculations. This is not practical when the Controller is used in devices such as an assist suit. In this paper, to solve these problems, we propose a novel Feedforward Controller based on an experimental identification model whose calculations are simpler than the previous ones. An experimental identification model enables the Feedforward Controller to improve the accuracy by identifying the parameters used in the model. Also, we compare the accuracy of the proposed Controller with the previous one.
Janwillem Van Wingerden - One of the best experts on this subject based on the ideXlab platform.
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Feedback-Feedforward individual pitch control design for wind turbines with uncertain measurements
2019 American Control Conference (ACC), 2019Co-Authors: Róbert Ungurán, Janwillem Van Wingerden, Vlaho Petrović, Sjoerd Boersma, Martin KühnAbstract:As the diameters of wind turbine rotors increase, the loads across the rotors are becoming more uneven due to inhomogeneous wind fields. Therefore, more advanced passive or active load reduction techniques are introduced to mitigate these uneven loads. Furthermore, measuring the disturbance can help to improve the control performance. This paper first examines how robust stability and performance are affected by uncertain sensor measurements when an integrator-based feedback is extended with an inversion-based Feedforward individual pitch Controller with similar bandwidth. A fixed-structured H∞feedback-Feedforward Controller is proposed. The proposed feedback-Feedforward Controller ensures robust stability and performance and achieves better load reduction than a classical integrator-based feedback Controller combined with inversion-based Feedforward Controller.
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wind tunnel tests with combined pitch and free floating flap control data driven iterative Feedforward Controller tuning
Wind Energy Science Discussions, 2016Co-Authors: Sachin T Navalkar, Lars O Bernhammer, Jurij Sodja, Edwin Van Solingen, Gijs Van Kuik, Janwillem Van WingerdenAbstract:Wind turbine load alleviation has traditionally been addressed in the literature using either full-span pitch control, which has limited bandwidth, or trailing-edge flap control, which typically shows low control authority due to actuation constraints. This paper combines both methods and demonstrates the feasibility and advantages of such a combined control strategy on a scaled prototype in a series of wind tunnel tests. The pitchable blades of the test turbine are instrumented with free-floating flaps close to the tip, designed such that they aerodynamically magnify the low stroke of high-bandwidth actuators. The additional degree of freedom leads to aeroelastic coupling with the blade flexible modes. The inertia of the flaps was tuned such that instability occurs just beyond the operational envelope of the wind turbine; the system can however be stabilised using collocated closed-loop control. A Feedforward Controller is shown to be capable of significant reduction of the deterministic loads of the turbine. Iterative Feedforward tuning, in combination with a stabilising feedback Controller, is used to optimise the Controller online in an automated manner, to maximise load reduction. Since the system is non-linear, the Controller gains vary with wind speed; this paper also shows that iterative Feedforward tuning is capable of generating the optimal gain schedule online.
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iterative feedback tuning of an lpv Feedforward Controller for wind turbine load alleviation
IFAC-PapersOnLine, 2015Co-Authors: Sachin T Navalkar, Janwillem Van WingerdenAbstract:Abstract Modern wind turbines incorporate active control techniques such as Individual Pitch Control (IPC) to reduce lifetime dynamic loads. However, system identification and Controller design are typically difficult for turbine load control on account of the LPV nature of the system and disturbance. This challenge is addressed by extending the data-based Iterative Feedback Tuning (IFT) technique to systems with LPV output matrices, controlled with a parameterised Feedforward Controller LPV in output matrices. In order to compensate for the parameter-varying nature of the plant, Controller and disturbance, and to estimate the IFT cost gradients in an unbiased manner, an increased number of experiments is required. Such an LPV Controller, tuned and tested in a high-fidelity simulation environment, is able to show enhanced load reductions as compared to an LTI Controller.
James Richard Forbes - One of the best experts on this subject based on the ideXlab platform.
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$\mathcal{H}_{\infty}$-Optimal Strictly Positive Real Parallel Feedforward Control
2019 American Control Conference (ACC), 2019Co-Authors: Ryan James Caverly, James Richard ForbesAbstract:This paper presents static and dynamic parallel Feedforward Controller synthesis methods that render a linear time-invariant system strictly positive real (SPR) in an H∞-optimal fashion. The parallel Feedforward Controller is designed in such a manner that when the output of the system is added to the output of the parallel Feedforward Controller, the transfer matrix from the system input to the new output is SPR. In order to ensure that the difference between the new output and the original system output is small, the maximum singular value of a static parallel Feedforward Controller or the weighted H∞ norm of a dynamic parallel Feedforward Controller is minimized. The proposed synthesis methods are convex optimization problems that make use of linear matrix inequality and equality constraints. The Controllers are implemented numerically on a flexible-joint robotic manipulator and compared to a parallel Feedforward Controller from the literature. It is shown in closed-loop simulation that a significant improvement in tracking error is achieved with one of the proposed dynamic parallel Feedforward Controller synthesis methods.
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$\mathcal{H}_\infty$ -Optimal Parallel Feedforward Control Using Minimum Gain
IEEE Control Systems Letters, 2018Co-Authors: Ryan James Caverly, James Richard ForbesAbstract:This letter presents static and dynamic parallel Feedforward Controller synthesis methods that render a linear time-invariant system minimum phase by augmenting its output. The system output is perturbed the least amount possible by minimizing the gain of the parallel Feedforward Controller while ensuring the augmented system is minimum phase. This is done by minimizing the maximum singular value of a static parallel Feedforward Controller or the weighted H∞ norm of a dynamic parallel Feedforward Controller. Static and dynamic parallel Feedforward Controllers are synthesized using direct and indirect methods that involve bilinear matrix inequality constraints and are solved iteratively using linear matrix inequalities. The direct method enforces a minimum gain constraint directly on the augmented system, while the indirect method solves for an asymptotically stabilizing negative feedback Controller that is inverted to obtain the parallel Feedforward Controller. Numerical examples are provided to demonstrate the effectiveness of the proposed Controller synthesis methods.
