Adaptive Iterative Learning Boundary Control of a Flexible Manipulator with Guaranteed Transient Performance

An iterative learning control algorithm with iteration decreasing gain is proposed for stochastic point‐to‐point tracking systems. The almost sure convergence and asymptotic properties of the proposed recursive algorithm are strictly proved. The selection of learning gain matrix is given. An illustrative example shows the effectiveness and asymptotic trajectory properties of the proposed approach.

Section: Remarkmentioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

“…The parameters in (8) are set as a k = 1∕k and L = (ΦG 1 ) T . The algorithm is run for 200 iterations.…”

Section: Illustrative Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Stochastic Point‐to‐Point Iterative Learning Control Based on Stochastic Approximation

Shen

Zhang³

2017

Boundary Control for a Vibrating String System with Bounded Input

Zhao

Liu

Luo

2017

In this study, we deal with the control problem of a vibrating string system under the condition of restricted input and external disturbance. The major objectives are the development of a boundary vibration control scheme for globally stabilizing the string system and simultaneously for compensating the effect of the input saturation, and the design of a disturbance observer for tracking the external disturbance. To this end, a boundary control is proposed based on smooth hyperbolic function to suppress the vibration and eliminate the input restriction effect, and a disturbance observer is employed to mitigate the external disturbance. The asymptotic stability of the controlled system is demonstrated employing the extended LaSalle's invariance principle. In order to verify the control performance of the proposed control, simulation results are presented by the choice of proper control design parameters.

An E‐HOIM Based Data‐Driven Adaptive TILC of Nonlinear Discrete‐Time Systems for Non‐Repetitive Terminal Point Tracking

Lin

Chi

Huang

et al. 2017

This work proposes a new adaptive terminal iterative learning control approach based on the extended concept of highorder internal model, or E-HOIM-ATILC, for a nonlinear non-affine discrete-time system. The objective is to make the system state or output at the endpoint of each operation track a desired target value. The target value varies from one iteration to another. Before proceeding to the data-driven design of the proposed approach, an iterative dynamical linearization is performed for the unknown nonlinear systems by using the gradient of the nonlinear system with regard to the control input as the iteration-and-time-varying parameter vector of the equivalent linear I/O data model. By virtue of the basic idea of the internal model, the inverse of the parameter vector is approximated by a high-order internal model. The proposed E-HOIM-ATILC does not use measurements of any intermediate points except for the control input and terminal output at the endpoint. Moreover, it is data-driven and needs merely the terminal I/O measurements. By incorporating additional control knowledge from the known portion of the high order internal model into the learning control law, the control performance of the proposed E-HOIM-ATILC is improved. The convergence is shown by rigorous mathematical proof. Simulations through both a batch reactor and a coupled tank system demonstrate the effectiveness of the proposed method.