An Application of Fictitious Reference Iterative Tuning to State Feedback Control

The recently proposed data-driven pole placement method is able to make use of measurement data to simultaneously identify a state space model and derive pole placement state feedback gain. It can achieve this precisely for systems that are linear time-invariant and for which noiseless measurement datasets are available. However, for nonlinear systems, and/or when the only noisy measurement datasets available contain noise, this approach is unable to yield satisfactory results. In this study, we investigated the effect on data-driven pole placement performance of introducing a prefilter to reduce the noise present in datasets. Using numerical simulations of a self-balancing robot, we demonstrated the important role that prefiltering can play in reducing the interference caused by noise.

Section: Data-driven Pole Placementmentioning

confidence: 99%

“…Remark 2. To obtain the datasets in (12) by applying state feedback in (5) to the system in (4), the initial feedback gain F should be based on ( )…”

Section: Data-driven Pole Placementmentioning

confidence: 99%

An Improvement on Data-Driven Pole Placement for State Feedback Control and Model Identification

Shwe¹,

Yamamoto²

2017

ICA

“…The contribution of this paper is the combination of high order feedback-feedforward ILC and a variable forgetting factor. Feedback control [9–11] enhances the anti-interference performance and improves the robustness of the system; thus, the tracking deviation decreases during the iterative process. By adding feedforward control [12–14], the system can avoid the high gain that occurs in the feedback control method and can eliminate the actuator's saturation.…”

Section: Introductionmentioning

confidence: 99%

High Order Feedback-Feedforward Iterative Learning Control Scheme with a Variable Forgetting Factor

Dong

International Journal of Advanced Robotic Systems

2016

In this work, we present a new iterative learning control (ILC) scheme for a class of non-linear systems with uncertain and non-repetitive disturbances, in order to achieve perfect tracking by proposing a high order feedbackfeedforward ILC algorithm with a variable forgetting factor. The high order feedback-feedforward iterative learning controller can fully apply the previous control data to the system, which allows the system to track expectations more rapidly and precisely. Introducing a variable forgetting factor can weaken the former control output and its variance in the control law, while strengthening the robustness of the ILC. Through rigorous analyses, we demonstrate that uniform convergence of the state tracking error is guaranteed under this new ILC scheme. Simulation examples are also included to demonstrate the feasibility and effectiveness of the proposed learning controls.

“…The algorithm based on ILC is further improved by combining with existing feedback controller, such as PID [11], ALINEA [12], model-free adaptive control [13], and other general feedback controls [14,15]. These combinations can retain the functionality of existing feedback loop such as robustness and meanwhile enjoy the extra performance improvement from ILC.…”

Section: Introductionmentioning

confidence: 99%

High-Order Feedback Iterative Learning Control Algorithm with Forgetting Factor

Dong

Mathematical Problems in Engineering

2015

A novel iterative learning control (ILC) algorithm is proposed to produce output curves that pass close to the desired trajectory.The key advantage of the proposed algorithm is introducing forgetting factor, which is a function of the number of iterations. Due to the forgetting factor characteristic of ILC, the proposed scheme not only stabilizes the nonlinear system with uncertainties but also weakens interference on the tracking desired trajectory. Simulation examples are included to demonstrate feasibility and effectiveness of the proposed algorithm.