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Makoto Iwasaki - One of the best experts on this subject based on the ideXlab platform.

  • initial friction compensation using rheology based rolling friction model in fast and precise positioning
    IEEE Transactions on Industrial Electronics, 2013
    Co-Authors: Yoshihiro Maeda, Makoto Iwasaki
    Abstract:

    This paper presents an initial friction compensation by a disturbance observer which is designed on the basis of a rolling friction model (RFM) for the fast and precise positioning of ball-screw-driven table systems. Rolling friction in the table drive mechanism behaves as a nonlinear elastic component in the microdisplacement region, deteriorating the fine settling performance. The effects of the rolling friction on the positioning, therefore, should be compensated to provide the desired control performance. In the compensator design, a feedback control with a disturbance observer allows the plant system to behave as a nominal one with a robust stability and compensates for the effects of nonlinear friction on the positioning performance. The disturbance observer, however, inherently includes an Estimation Delay at the starting motion due to low-pass filters and Delay components. In this paper, therefore, an RFM is adopted as an initial value compensation of the disturbance observer to compensate for the initial friction behavior, providing the Delay-free Estimation. The proposed compensation method has been verified by numerical simulations and experiments using a prototype for industrial positioning devices.

  • Initial friction compensation by disturbance observer based on rolling friction model
    2009 35th Annual Conference of IEEE Industrial Electronics, 2009
    Co-Authors: Yoshihiro Maeda, Makoto Iwasaki
    Abstract:

    This paper presents an initial friction compensation by a disturbance observer in the fast and precise positioning of ball screw-driven table systems, on the basis of a rolling friction model. The rolling friction behaves as a nonlinear elastic element in the table drive mechanism, especially in the micro-displacement region, deteriorating the fine positioning performance. Effects of the rolling friction on the positioning, therefore, should be compensated to achieve the desired control performance. In the controller design, a feedback control with a disturbance observer allows the plant system to behave as a nominal one with robust stability, and compensates for effects of nonlinear friction on the positioning performance. However, the disturbance observer has an Estimation Delay for the initial friction behavior at the starting motion in positioning due to a low pass filter and Delay components. In this paper, therefore, a rolling friction model is adopted as an initial compensation of the disturbance observer to compensate for the initial friction behavior, and provides the Delay-free Estimation. The proposed compensation method has been verified by experiments using a prototype of industrial positioning devices.

Yoshihiro Maeda - One of the best experts on this subject based on the ideXlab platform.

  • initial friction compensation using rheology based rolling friction model in fast and precise positioning
    IEEE Transactions on Industrial Electronics, 2013
    Co-Authors: Yoshihiro Maeda, Makoto Iwasaki
    Abstract:

    This paper presents an initial friction compensation by a disturbance observer which is designed on the basis of a rolling friction model (RFM) for the fast and precise positioning of ball-screw-driven table systems. Rolling friction in the table drive mechanism behaves as a nonlinear elastic component in the microdisplacement region, deteriorating the fine settling performance. The effects of the rolling friction on the positioning, therefore, should be compensated to provide the desired control performance. In the compensator design, a feedback control with a disturbance observer allows the plant system to behave as a nominal one with a robust stability and compensates for the effects of nonlinear friction on the positioning performance. The disturbance observer, however, inherently includes an Estimation Delay at the starting motion due to low-pass filters and Delay components. In this paper, therefore, an RFM is adopted as an initial value compensation of the disturbance observer to compensate for the initial friction behavior, providing the Delay-free Estimation. The proposed compensation method has been verified by numerical simulations and experiments using a prototype for industrial positioning devices.

  • Initial friction compensation by disturbance observer based on rolling friction model
    2009 35th Annual Conference of IEEE Industrial Electronics, 2009
    Co-Authors: Yoshihiro Maeda, Makoto Iwasaki
    Abstract:

    This paper presents an initial friction compensation by a disturbance observer in the fast and precise positioning of ball screw-driven table systems, on the basis of a rolling friction model. The rolling friction behaves as a nonlinear elastic element in the table drive mechanism, especially in the micro-displacement region, deteriorating the fine positioning performance. Effects of the rolling friction on the positioning, therefore, should be compensated to achieve the desired control performance. In the controller design, a feedback control with a disturbance observer allows the plant system to behave as a nominal one with robust stability, and compensates for effects of nonlinear friction on the positioning performance. However, the disturbance observer has an Estimation Delay for the initial friction behavior at the starting motion in positioning due to a low pass filter and Delay components. In this paper, therefore, a rolling friction model is adopted as an initial compensation of the disturbance observer to compensate for the initial friction behavior, and provides the Delay-free Estimation. The proposed compensation method has been verified by experiments using a prototype of industrial positioning devices.

Khalid Alsuhaili - One of the best experts on this subject based on the ideXlab platform.

  • Communications over the Best Singular Mode of a Reciprocal MIMO Channel
    IEEE Transactions on Communications, 2010
    Co-Authors: Saeed Gazor, Khalid Alsuhaili
    Abstract:

    We consider two nodes equipped with multiple antennas that intend to communicate i.e. both of which transmit and receive data. We model the responses of the communication channels between these nodes as linear and reciprocal (time invariant or with very slow time variations). In practice, we exploit the closed loop conversation between these nodes and present an efficient algorithm allowing to adaptively identify the Best Singular Mode (BSM) of the channel. We consider two scenarios. In the first scenario, the initial communication link is established over the BSM assuming that the exchanged data is partially known at both nodes. This scenario is suitable for channel training. In the second scenario, the BSM is adaptively updated while the real unknown data is exchanged between the nodes i.e. no capacity is wasted for channel identification. The proposed adaptive algorithm is robust to noise as the involved step-size allows a trade-off to reduce the impact of the additive noise at the expense of some Estimation Delay. Our computer simulations show that the proposed algorithm works efficiently in both modes of operations (training mode and simultaneous training/data transmission mode) for both static and slow fading MIMO channels and for both white and colored noises.

  • Communications Over Multiple Best Singular Modes of Reciprocal MIMO Channels
    2010
    Co-Authors: Khalid Alsuhaili
    Abstract:

    We consider two transceivers equipped with multiple antennas that intend to communicate i.e. both of which transmit and receive data in a TDD fashion. Assuming that the responses of the physical communication channels between these two nodes are linear and reciprocal (time invariant or with very slow time variations), and by exploiting the closed loop conversation between these nodes, we have proposed efficient algorithms allowing to adaptively identify the Best Singular Mode (BSM) of the channel (those algorithms are for training, blind, and semi-blind channel identification). Unlike other proposed algorithms, our proposed adaptive algorithms are robust to noise as the involved step-size allows a trade-off to reduce the impact of the additive noise at the expense of some Estimation Delay. In practice, however, the reciprocity of the equivalent channels is lost because of the mismatch between the transmit and the receive filters of the communicating nodes. This mismatch causes significant degradation in the performance of the BSM Estimation. Therefore, we have also proposed adaptive self-calibrating algorithms (which do not require any additional RF circuitry) that account for such a mismatch. In addition, we have conducted a convergence analysis of the BSM algorithm and extended it to estimate multiple modes simultaneously. Finally, we have also proposed an adaptive, iterative algorithm that is capable of allocating power in such a way that maximizes the capacity of a SISO OFDM communication system.

K. Khorasani - One of the best experts on this subject based on the ideXlab platform.

  • Inversion-based output tracking and unknown input reconstruction of square discrete-time linear systems
    Automatica, 2018
    Co-Authors: E. Naderi, K. Khorasani
    Abstract:

    In this paper, we propose a framework for output tracking control of both minimum phase (MP) and non-minimum phase (NMP) systems as well as systems with transmission zeros on the unit circle. Towards this end, first the problem of unknown state and input reconstruction of non-minimum phase systems is addressed. An unknown input observer (UIO) is designed that accurately reconstructs the minimum phase states of the system. The reconstructed minimum phase states serve as inputs to an FIR filter that is designed for a Delayed non-minimum phase state reconstruction. It is shown that a quantified upper bound of the reconstruction error exponentially decreases as the Estimation Delay is increased. Therefore, an almost perfect state and input reconstruction can be achieved by selecting the Delay to be sufficiently large. Our proposed inversion scheme is then applied to solve the output-tracking control problem. Furthermore, we have also proposed a methodology to handle the output tracking problem of systems that have transmission zeros on the unit circle in addition to MP and NMP zeros. Simulation case studies are also presented to demonstrate and illustrate the merits and capabilities of our proposed methodologies.

  • Inversion-Based Output Tracking and Unknown Input Reconstruction of Square Discrete-Time Linear Systems
    arXiv: Systems and Control, 2016
    Co-Authors: E. Naderi, K. Khorasani
    Abstract:

    In this paper, we propose a framework for output tracking control of both minimum phase (MP) and non-minimum phase (NMP) systems {as well as systems with transmission zeros on the unit circle}. Towards this end, we first address the problem of unknown state and input reconstruction of non-minimum phase systems. An unknown input observer (UIO) is designed that accurately reconstructs the minimum phase states of the system. The reconstructed minimum phase states serve as inputs to an FIR filter for a Delayed non-minimum phase state reconstruction. It is shown that a quantified upper bound of the reconstruction error exponentially decreases as the Estimation Delay is increased. Therefore, an almost perfect reconstruction can be achieved by selecting the Delay to be sufficiently large. Our proposed inversion scheme is then applied to solve the output-tracking control problem. {We have also proposed a methodology to handle the output tracking problem of systems that have transmission zeros on the unit circle in addition to MP and NMP zeros.} Simulation case studies are also presented that demonstrate the merits and capabilities of our proposed methodologies.

  • Inversion-BasedOutputTrackingandUnknownInput ReconstructionofDiscrete-TimeLinearSystems
    2016
    Co-Authors: E. Naderi, K. Khorasani
    Abstract:

    In this paper, we propose a framework for output tracking control of both minimum phase (MP) and non-minimum phase (NMP) discrete-time linear systems. Towards this end, we first address the problem of unknown state and input reconstruction of non-minimum phase systems. An unknown input observer (UIO) is designed that accurately reconstructs the minimum phase states of the system. The reconstructed minimum phase states serve as inputs to an FIR filter for a Delayed non-minimum phase state reconstruction. It is shown that a quantified upper bound of the reconstruction error exponentially decreases as the Estimation Delay is increased. Therefore, an almost perfect reconstruction can be achieved by selecting the Delay to be sufficiently large. The proposed inversion scheme is then a to the output-tracking control problem. We have also comprehensively addressed and discussed the non-minimum phase dynamics and derived explicit relationships between the system matrices of the above dynamics. Simulation case studies are also presented that demonstrate the merits and capabilities of our proposed methodology.

Rahul Mangharam - One of the best experts on this subject based on the ideXlab platform.

  • Anytime Computation and Control for Autonomous Systems
    IEEE Transactions on Control Systems Technology, 2020
    Co-Authors: Yash Vardhan Pant, Houssam Abbas, Kartik Mohta, Truong X. Nghiem, Joseph Devietti, Rhudii A. Quaye, Rahul Mangharam
    Abstract:

    The correct and timely completion of the sensing and action loop is of utmost importance in safety critical autonomous systems. Crucial to the performance of this feedback control loop are the computation time and accuracy of the estimator which produces state estimates used by the controller. These state estimators often use computationally expensive perception algorithms like visual feature tracking. With on-board computers on autonomous robots being computationally limited, the computation time of such an Estimation algorithm can at times be high enough to result in poor control performance. We develop a framework for codesign of anytime Estimation and robust control algorithms, taking into account computation Delays and Estimation inaccuracies. This is achieved by constructing an anytime estimator from an off-the-shelf perception-based Estimation algorithm and obtaining a trade-off curve for its computation time versus Estimation error. This is used in the design of a robust predictive control algorithm that at run-time decides a contract, or operation mode, for the estimator in addition to controlling the dynamical system to meet its control objectives at a reduced computation energy cost. This codesign provides a mechanism through which the controller can use the tradeoff curve to reduce Estimation Delay at the cost of higher inaccuracy, while guaranteeing satisfaction of control objectives. Experiments on a hexrotor platform running a visual-based algorithm for state Estimation show how our method results in up to a 10% improvement in control performance while simultaneously saving 5%-6% in computation energy as compared to a method that does not leverage the codesign.

  • Co-design of Anytime Computation and Robust Control (Supplemental)
    2015
    Co-Authors: Yash Vardhan Pant, Houssam Abbas, Kartik Mohta, Truong X. Nghiem, Joesph Deveitti, Rahul Mangharam
    Abstract:

    Control software of autonomous robots has stringent real-time requirements that must be met to achieve the control objectives. One source of variability in the performance of a control system is the execution time and accuracy of the state estimator that provides the controller with state information. This estimator is typically perception-based (e.g., Computer Vision-based) and is computationally expensive. When the computational resources of the hardware platform become overloaded, the Estimation Delay can compromise control performance and even stability. In this paper, we define a framework for co-designing anytime Estimation and control algorithms, in a manner that accounts for implementation issues like Delays and inaccuracies. We construct an anytime perception-based estimator from standard off-the-shelf Computer Vision algorithms, and show how to obtain a trade-off curve for its Delay vs estimate error behavior. We use this anytime estimator in a controller that can use this tradeoff curve at runtime to achieve its control objectives at a reduced energy cost. When the Estimation Delay is too large for correct operation, we provide an optimal manner in which the controller can use this curve to reduce Estimation Delay at the cost of higher inaccuracy, all the while guaranteeing basic objectives are met. We illustrate our approach on an autonomous hexrotor and demonstrate its advantage over a system that does not exploit co-design.

  • RTSS - Co-design of Anytime Computation and Robust Control
    2015 IEEE Real-Time Systems Symposium, 2015
    Co-Authors: Yash Vardhan Pant, Houssam Abbas, Kartik Mohta, Truong X. Nghiem, Joseph Devietti, Rahul Mangharam
    Abstract:

    Control software of autonomous robots has stringent real-time requirements that must be met to achieve the control objectives. One source of variability in the performance of a control system is the execution time and accuracy of the state estimator that provides the controller with state information. This estimator is typically perception-based (e.g., Computer Vision-based) and is computationally expensive. When the computational resources of the hardware platform become overloaded, the Estimation Delay can compromise control performance and even stability. In this paper, we define a framework for co-designing anytime Estimation and control algorithms, in a manner that accounts for implementation issues like Delays and inaccuracies. We construct an anytime perception-based estimator from standard off-the-shelf Computer Vision algorithms, and show how to obtain a trade-off curve for its Delay vs estimate error behaviour. We use this anytime estimator in a controller that can use this trade-off curve at runtime to achieve its control objectives at a reduced energy cost. When the Estimation Delay is too large for correct operation, we provide an optimal manner in which the controller can use this curve to reduce Estimation Delay at the cost of higher inaccuracy, all the while guaranteeing basic objectives are met. We illustrate our approach on an autonomous hexrotor and demonstrate its advantage over a system that does not exploit co-design.