In this paper, a data driven controller is designed for the positioning control of a piezoelectric tube scanner (PTS) used in an atomic force microscope (AFM). A single-input single-output (SISO) model-based data driv...
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In this paper, a data driven controller is designed for the positioning control of a piezoelectric tube scanner (PTS) used in an atomic force microscope (AFM). A single-input single-output (SISO) model-based data driven controller is synthesized by using a mixed negative imaginary (NI) and small gain (SG) approach. The controller is implemented on an AFM and gives significant damping and tracking performance, with 16.64 dB and 19.33 dB damping at the resonance frequency of the X and Y-axes of the PTS, respectively. Moreover, this type of controller design is an effective approach to give intuition about how the controller frequency response should, depending on the design constraints being applied, which ensures optimized performance in vibration control. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
In this paper, a data driven controller is designed for the positioning control of a piezoelectric tube scanner (PTS) used in an atomic force microscope (AFM). A single-input single-output (SISO) model-based data driv...
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This paper presents a datadriven fuzzy controller for a generic hypersonic vehicle. The fuzzy systems with any bounded nonconstant piecewise continuous membership functions are introduced to reconstruct the unknown s...
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ISBN:
(纸本)9781538666142
This paper presents a datadriven fuzzy controller for a generic hypersonic vehicle. The fuzzy systems with any bounded nonconstant piecewise continuous membership functions are introduced to reconstruct the unknown system dynamics without relying on the exact mathematical models. The control law is designed based on the reconstructed fuzzy models. The stability analysis of the whole control system is presented from the Lyapunov function and shows that the tracking errors converge to zero. In order to save online computation time, the fuzzy membership function parameters are determined based on the idea of the OS-Fuzzy-ELM algorithm by randomly assigning the values to them. The simulation results demonstrate the superior performance of the proposed controller.
A data-driven approximation formulation for the state reconstruction problem of dynamical systems is presented in this paper. Without the assumption of an explicit mathematical model, the Hamilton-Jacobi-Bellman (HJB)...
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A data-driven approximation formulation for the state reconstruction problem of dynamical systems is presented in this paper. Without the assumption of an explicit mathematical model, the Hamilton-Jacobi-Bellman (HJB) based approach of a data-driven state reconstruction design method for monitoring and output-feedback control of dynamical systems is presented. The proposed state reconstruction design is based on a dynamic programming approach. To evaluate the proposed state reconstruction, computational experiments are conducted using only dynamical system model output data. The sensitivity of the algorithm parameters is also analyzed and discussed. The performance evaluation is analyzed in terms of the error metrics of the discrete linear quadratic regulator with output feedback under the value iteration algorithm through a reinforcement learning strategy.
High performance control systems (HPCS), including semiactive, active, and hybrid damping systems, are effective solutions to increase structural performance versus multihazard excitations. However, the implementation...
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High performance control systems (HPCS), including semiactive, active, and hybrid damping systems, are effective solutions to increase structural performance versus multihazard excitations. However, the implementation of HPCS within structural systems is still in its infancy, because of the complexity in designing a robust closed-loop control system that can ensure reliable and high mitigation performance. To overcome this challenge, a new type of controller with high adaptive capabilities is proposed. The control algorithm is based on real-time embedding of measurements to minimally represent the essential dynamics of the controlled system, therefore providing adaptive input space capabilities. This type of controller is termed an input-space dependent controller. In this paper, a specialized case of input-space dependent controller is investigated, where the embedding dimension is fixed, but the time delay used in the construction of the embedding varies with time. This constitutes a variable multidelay controller (VMDC), which includes an algorithm enabling the online selection of a time delay based on information theory. Here, optimal time delay selection is first studied and its applicability of the VMDC algorithm demonstrated. Numerical simulations are conducted on a single-degree-of-freedom (SDOF) system to study the performance of the VMDC versus different control strategies. Results show a significant gain in performance from the inclusion of an adaptive input space, and that the algorithm was robust with respect to noise. Simulations also demonstrate that critical gains in performance could be obtained from added knowledge in the system's dynamics by comparing mitigation results with a linear quadratic regulator (LQR) controller. Additional simulations are conducted on a three degrees-of-freedom (3DOF) system, which consists of a model structure equipped with an actuator and subjected to nonsimultaneous multihazards. Results show enhanced mitigation perfor
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