The objective of the work reported in this paper is to investigate the development of hybrid iterative learning control with input shaping for input tracking and end-point vibration suppression of a flexible manipulat...
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ISBN:
(纸本)0791841731
The objective of the work reported in this paper is to investigate the development of hybrid iterative learning control with input shaping for input tracking and end-point vibration suppression of a flexible manipulator. The dynamic model of the system is derived using the finite element method. Initially, a collocated proportional-derivative (PD) controller utilizing hub-angle and hub-velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate iterative learning control and a feedforward controller based on input shaping techniques for control of vibration (flexible motion) of the system. Simulation results of the response of the manipulator with the controllers are presented in the time and frequency domains. The performance of the hybrid learning control with input shaping scheme is assessed in terms of input tracking and level of vibration reduction. The effectives of the control schemes in handling various payloads are also studied.
This paper investigates the utilisation of feedforward and recurrent neural networks for dynamic modelling of a flexible plate structure. Neuro-modelling techniques are used for non-parametric identification of the fl...
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ISBN:
(纸本)0791841731
This paper investigates the utilisation of feedforward and recurrent neural networks for dynamic modelling of a flexible plate structure. Neuro-modelling techniques are used for non-parametric identification of the flexible plate structure based on one-step-ahead prediction. A multi layer perceptron (MLP) and Elman neural networks are designed to characterise the dynamic behaviour of the flexible plate. Results of the modelling techniques are validated through a range of tests including input/output mapping, training and test validation, mean-squared error and correlation tests. Results are presented in both time and frequency domains. Comparative performance assessments of both neuro-modelling approaches in terms of mean-squared error and estimation of the resonance modes of the system are carried out. It is noted that both techniques have been able to detect the first five vibration modes of the system successfully. Investigations also signify the advantage of a recurrent Elman network over an MLP feedforward network in modelling the flexible plate structure.
Two novel linear matrix inequality (LMI) based procedures to receive a stabilizing robust output feedback gain are presented, one of them being a modification of previous results of OLIVEIRA et al., [5]. The proposed ...
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Two novel linear matrix inequality (LMI) based procedures to receive a stabilizing robust output feedback gain are presented, one of them being a modification of previous results of OLIVEIRA et al., [5]. The proposed robust control law stabilizes the respective uncertain discrete-time system described by a polytopic model with guaranteed cost. The obtained results are compared with other LMI results from literature and illustrated on an example.
Direct adaptive control algorithms using state variables in control structure are known for their good adaptation capability and tracking performances that result from the fact that they use plant state variables in c...
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Fuzzy control has revealed as a practical alternative to several conventional control schemes since it has shown good results in some application areas. However, there are several drawbacks of this approach: (i) the d...
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In this article we are interested in design of a new simple rule-based method of MRAC parameters adaptation aiming to minimize the unmodelled dynamics influence. Firstly, we introduce some recent studies solving the p...
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In this article we are interested in design of a new simple rule-based method of MRAC parameters adaptation aiming to minimize the unmodelled dynamics influence. Firstly, we introduce some recent studies solving the p...
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In this article we are interested in design of a new simple rule-based method of MRAC parameters adaptation aiming to minimize the unmodelled dynamics influence. Firstly, we introduce some recent studies solving the problem of using an adaptive system to control a system with unmodelled dynamic. Secondly, the new method of parameters adaptation based on abs ( sgn ( e ) + sgn ( d 2 dt 2 e ) ) signal is discussed. The proposed method seems to set the controller parameters so as to ensure the closed loop aperiodic response.
Fuzzy control has revealed as a practical alternative to several conventional control schemes since it has shown good results in some application areas. However, there are several drawbacks of this approach: (i) the d...
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Fuzzy control has revealed as a practical alternative to several conventional control schemes since it has shown good results in some application areas. However, there are several drawbacks of this approach: (i) the design of fuzzy controllers is usually performed in an ad hoc manner where it is often difficult to choose some of the controller parameters (e.g., the membership functions), and (ii) the fuzzy controller constructed for the nominal plant may later perform inadequately if significant and unpredictable plant parameter variations occur. A “learning system” possesses the capability to improve its performance over time by interacting with its environment. A learning controlsystem is designed so that its “learning controller” has the ability to improve the performance of the closed-loop system by generating command inputs to the plant and utilizing feedback information from the plant. Learning controllers are often designed to mimic the manner in which a human in the control loop would learn how to control a system while it operates. Some characteristics of this human learning process my include: (i) after learning how to control the plant for some operating condition, if the operating conditions change, then the best way to control the system may have to be relearned; (ii) a human with significant amount of experience at controlling the system in one operating region should not forget this experience if the operating condition changes. To mimic these types of human learning behavior, we introduce strategy that can be used to learning controller onto the current operating region of the system.
Direct adaptive control algorithms using state variables in control structure are known for their good adaptation capability and tracking performances that result from the fact that they use plant state variables in c...
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Direct adaptive control algorithms using state variables in control structure are known for their good adaptation capability and tracking performances that result from the fact that they use plant state variables in control law as well as in adaptation law. The implementation of standard model reference adaptive control (MRAC) with state variable control structure requires some a priori knowledge about the plant to be controlled including the plant order. This information is crucial for the proper choice of reference model describing the desired closed loop dynamical behavior and consequently for the adaptive system performances. The aim of our paper is to propose the fuzzy adaptation law for MRAC with state variable structure of control law that is able to ensure the adaptation process convergence and tracking capability even in the presence of unmodelled dynamics.
The problem of robust output feedback stabilization is studied. The new robust stability LMI condition including guaranteed cost and the respective simple control design procedure is developed. Though the proposed con...
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