A model uncertainty compensation filter

In practical application, a nominal model is often used to approximate the design of industrial system. This approximation could make the traditional design method less effective due to the existence of model uncertainty. In this paper, a novel robust design approach is proposed to design the robustness of the dynamic system under model uncertainty. The key idea of this proposed method is that it integrates the advantages of both the model-based dynamic robust design and the data-based uncertainty compensation. A simulation example is conducted to demonstrate the effectiveness of the proposed robust design method.

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Posture Stabilization of Wheeled Mobile Robot Based on Passivity-Based Robust Switching Control with Model Uncertainty Compensation

Applied Sciences ◽

10.3390/app9235233 ◽

2019 ◽

Vol 9 (23) ◽

pp. 5233 ◽

Cited By ~ 2

Author(s):

Jung ◽

Bang

Keyword(s):

Robust Control ◽

Global Convergence ◽

Model Uncertainty ◽

Switching Control ◽

Initial Position ◽

Wheeled Mobile Robots ◽

Control Laws ◽

Rotational Perturbation ◽

Posture Stabilization ◽

Uncertainty Compensation

Thisstudy presents apassivity-based robust switching control for the posture stabilization of wheeled mobile robots (WMRs) with model uncertainty. Essentially, this proposed strategy is switching between (1) passivity-based robust control to lead the robot to the neighborhood of local minima with a finite time and (2) another robust control to perturb the w-rotational motion of the WMR before the v-kinetic energy of the WMR become meaningless, thereby, eventually converging to the desired posture. Thus, combining two switching control laws ensures the global convergence of (x,y)-navigation of WMRs from any initial position to desired set. Especially, the inter-switching time is intentionallyselected before the WMR completely loses its mobility, which ensures a strict decrease in (x,y)-navigation potential energy and a better global convergence rate. In addition, this control architecture also includes model uncertainty compensation, often neglected in practice, and analytical study of rotational perturbation was also conducted. The Lyapunov technique and energetic passivity wereutilized to derive this control law. Simulation results are presented to illustrate the effectiveness of the proposed technique. It wasfound from the results that the WMR wasquickly converged to the desired posture even under the presence of model uncertainty.

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Model-Based Probabilistic Robust Design With Data-Based Uncertainty Compensation for Partially Unknown System

Journal of Mechanical Design ◽

10.1115/1.4005589 ◽

2012 ◽

Vol 134 (2) ◽

Cited By ~ 2

Author(s):

XinJiang Lu ◽

Han-Xiong Li ◽

C. L. Philip Chen

Keyword(s):

Robust Design ◽

Model Uncertainty ◽

Linear Structure ◽

Parameter Variation ◽

Design Approach ◽

Design Stage ◽

Process Data ◽

Sensitivity Matrix ◽

Model Based ◽

Uncertainty Compensation

Model uncertainty often results from incomplete system knowledge or simplification made at the design stage. In this paper, a hybrid model/data-based probabilistic design approach is proposed to design a nonlinear system to be robust under the circumstances of parameter variation and model uncertainty. First, the system is formulated under a linear structure which will serve as a nominal model of the system. All model uncertainties and nonlinearities will be placed under a sensitivity matrix with its bound estimated from process data. On this basis, a model-based robust design method is developed to minimize the influence of parameter variation in relation to performance covariance. Since this proposed design approach possesses both merits from the model-based robust design as well as from the data-based uncertainty compensation, it can effectively achieve robustness for partially unknown nonlinear systems. Finally, two practical examples demonstrate and confirm the effectiveness of the proposed method.

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