To solve the trajectory tracking problem for rigid robot manipulators subject to external disturbances and performing repetitive tasks, we present in this paper a controller scheme containing a feedback action plus an...
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To solve the trajectory tracking problem for rigid robot manipulators subject to external disturbances and performing repetitive tasks, we present in this paper a controller scheme containing a feedback action plus an iteratively learning term representing the disturbance estimation. The proof of the asymptotic stability is based upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed control scheme. In this proof of the stability, the saturation technique is used. Finally, simulation results on two-link manipulator are provided to illustrate the effectiveness of the proposed controller.
To enhance the stability and robustness of the DC converter, a two-dimensional (2D) iterativelearning control method suitable for the Buck converter is proposed. This control method is derived through a continuous-di...
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To enhance the stability and robustness of the DC converter, a two-dimensional (2D) iterativelearning control method suitable for the Buck converter is proposed. This control method is derived through a continuous-discrete model. Combining the error dynamics equation and the iterative learning method (ILM) described with Roesser theory, effective learning rules and sufficient conditions for convergence are obtained. By contrast with the iterativelearning algorithm of the conventional Buck converter, this algorithm clarifies the learning gain, deeply learns the structure and parameters of the converter, and obtains an accurate learning dynamics model. Moreover, numerical simulations of the Buck converter with specific circuit parameters are also conducted. The simulation results demonstrate that the control learning rules are less restrictive with a swift transient response, which are robust without complex feedback compensation circuits. Thereby, it elucidates a new approach to the application of the Buck converter.
This work focuses on the accurate identification of lithium-ion battery's non-linear parameters by using an iterative learning method. First, the second-order resistance-capacitance model and its regression form o...
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This work focuses on the accurate identification of lithium-ion battery's non-linear parameters by using an iterative learning method. First, the second-order resistance-capacitance model and its regression form of the battery are introduced. Then, when the battery repeatedly implements a discharge trial from the state of charge (SOC) 100 to 0%, an iterativelearning based recursive least square (IL-RLS) algorithm is presented to accurately identify the non-linear parameters of the regression model. The essential idea of the IL-RLS algorithm is to improve the current parameter estimates by learning the predictive errors of the previous trials. After that, the parameters are identified as the functions of SOC by using the IL-RLS, which are verified by comparing with the results of the classic identification method for current pulses. As a result, an application-oriented SOC estimation scheme is proposed, where the IL-RLS calibrates the battery parameters offline and the classic extended Kalman filter (EKF) estimates the SOC in real-time. Finally, based on the EKF as well as the parameters identified by the IL-RLS, one static and three dynamic operating conditions are given to show the efficiency of the IL-RLS, where all the SOC estimation errors are <2%.
This paper explores the question about iterativelearning observer design about a kind of nonlinear plants have repetitive operating characteristics. Different from traditional methods, the proposed iterativelearning...
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ISBN:
(纸本)9781509054626
This paper explores the question about iterativelearning observer design about a kind of nonlinear plants have repetitive operating characteristics. Different from traditional methods, the proposed iterativelearning state observer is conducted and updated along the iteration direction. Furthermore, the proposed method has data-driven nature and derives from nonlinear systems directly, where no any model information is required except for the input and output measurements. A simulation case was employed to prove the performance of the given observer.
Extracting aerobomb's aerodynamic drag coefficient curve C df from 3-dimensional theodolite film data is formulated as an optimal tracking control problem (OTCP), where the C df the control function and the flight...
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Extracting aerobomb's aerodynamic drag coefficient curve C df from 3-dimensional theodolite film data is formulated as an optimal tracking control problem (OTCP), where the C df the control function and the flight testing data are the desired trajectories. An iterative learning method is introduced to solve this OTCP efficiently. The convergence is shown to be robust to the initial control guess. For a better convergence performance, several choices of learning gains have been discussed. Results from actual flight testing data for three bombing flight paths have been presented to validate the effectiveness of the proposed iterativelearning scheme.
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