Composite Energy Function-Based Spatial Iterative Learning Control in Motion Systems

2018 ◽  
Vol 26 (5) ◽  
pp. 1834-1841 ◽  
Author(s):  
Jiaolong Liu ◽  
Xinmin Dong ◽  
Deqing Huang ◽  
Miao Yu
Keyword(s):  
Energy Function ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Composite Energy ◽  
Composite Energy Function

scholarly journals Iterative Learning Control of a Nonlinear Aeroelastic System despite Gust Load

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xing-zhi Xu ◽  
Ya-kui Gao ◽  
Wei-guo Zhang
Keyword(s):  
Control Strategy ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Surface ◽  
Stream Velocity ◽  
Control Input ◽  
Parameter Uncertainties ◽  
Composite Energy

The development of a control strategy appropriate for the suppression of aeroelastic vibration of a two-dimensional nonlinear wing section based on iterative learning control (ILC) theory is described. Structural stiffness in pitch degree of freedom is represented by nonlinear polynomials. The uncontrolled aeroelastic model exhibits limit cycle oscillations beyond a critical value of the free-stream velocity. Using a single trailing-edge control surface as the control input, a ILC law under alignment condition is developed to ensure convergence of state tracking error. A novel Barrier Lyapunov Function (BLF) is incorporated in the proposed Barrier Composite Energy Function (BCEF) approach. Numerical simulation results clearly demonstrate the effectiveness of the control strategy toward suppressing aeroelastic vibration in the presence of parameter uncertainties and triangular, sinusoidal, and graded gust loads.

scholarly journals Adaptive Iterative Learning Control for Complex Dynamical Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Xiuqing Hao ◽  
Junmin Li
Keyword(s):  
Iterative Learning Control ◽  
Tracking Error ◽  
Reference Signal ◽  
Learning Control ◽  
Iterative Learning ◽  
Complex Dynamical Networks ◽  
Time Interval ◽  
Adaptive Iterative Learning Control ◽  
Dynamical Networks ◽  
Composite Energy

A new adaptive iterative learning control scheme is proposed for complex dynamical networks with repetitive operation over a fixed time interval. By designing difference type updating laws for unknown time-varying parameters and coupling strength, the state of each node in complex dynamical networks can track the reference signal. By constructing a composite energy function, a sufficient condition of the convergence of tracking error sequence is achieved in the iteration domain. Finally, a numerical example is given to show the effectiveness of the designed method.

scholarly journals Observer-Based Adaptive Iterative Learning Control for a Class of Nonlinear Time Delay Systems with Input Saturation

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Jian-ming Wei ◽  
Yun-an Hu ◽  
Mei-mei Sun
Keyword(s):  
Iterative Learning Control ◽  
Input Saturation ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Varying ◽  
Hyperbolic Tangent ◽  
Adaptive Iterative Learning Control ◽  
Hyperbolic Tangent Function ◽  
Tangent Function ◽  
Composite Energy

This paper presents an adaptive iterative learning control scheme for the output tracking of a class of nonlinear systems with unknown time-varying delays and input saturation nonlinearity. An observer is presented to estimate the states and linear matrix inequality (LMI) method is employed for observer design. The assumption of identical initial condition for ILC is relaxed by introducing boundary layer function. The possible singularity problem is avoided by introducing hyperbolic tangent function. The uncertainties with time-varying delays are compensated for by the combination of appropriate Lyapunov-Krasovskii functional and Young’s inequality. Both time-varying and time-invariant radial basis function neural networks are employed to deal with system uncertainties. On the basis of a property of hyperbolic tangent function, the system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapunov-like composite energy function in two cases, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.

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