Fuzzy control strategy for acrobots combining model-free and model-based control

1999 ◽  
Vol 146 (6) ◽  
pp. 505-510 ◽  
Author(s):  
X. Lai ◽  
Z. Cai ◽  
J. -H. She ◽  
Y. Ohyama
2020 ◽  
Vol 14 (14) ◽  
pp. 2649-2656
Author(s):  
Zeyan Lv ◽  
Yong Zhang ◽  
Miao Yu ◽  
Yanghong Xia ◽  
Wei Wei

2015 ◽  
Vol 114 (3) ◽  
pp. 1577-1592 ◽  
Author(s):  
Barbara La Scaleia ◽  
Myrka Zago ◽  
Francesco Lacquaniti

Two control schemes have been hypothesized for the manual interception of fast visual targets. In the model-free on-line control, extrapolation of target motion is based on continuous visual information, without resorting to physical models. In the model-based control, instead, a prior model of target motion predicts the future spatiotemporal trajectory. To distinguish between the two hypotheses in the case of projectile motion, we asked participants to hit a ball that rolled down an incline at 0.2 g and then fell in air at 1 g along a parabola. By varying starting position, ball velocity and trajectory differed between trials. Motion on the incline was always visible, whereas parabolic motion was either visible or occluded. We found that participants were equally successful at hitting the falling ball in both visible and occluded conditions. Moreover, in different trials the intersection points were distributed along the parabolic trajectories of the ball, indicating that subjects were able to extrapolate an extended segment of the target trajectory. Remarkably, this trend was observed even at the very first repetition of movements. These results are consistent with the hypothesis of model-based control, but not with on-line control. Indeed, ball path and speed during the occlusion could not be extrapolated solely from the kinematic information obtained during the preceding visible phase. The only way to extrapolate ball motion correctly during the occlusion was to assume that the ball would fall under gravity and air drag when hidden from view. Such an assumption had to be derived from prior experience.


2001 ◽  
Author(s):  
Zeyu Liu ◽  
John Wagner

Abstract The mathematical modeling of dynamic systems is an important task in the design, analysis, and implementation of advanced automotive control systems. Although most vehicle control algorithms tend to use model-free calibration architectures, a need exists to migrate to model-based control algorithms which offer greater operating performance. However, in many instances, the analytical descriptions are too complex for real-time powertrain and chassis model-based control algorithms. Therefore, model reduction strategies may be applied to transform the original model into a simplified lower-order form while preserving the dynamic characteristics of the original high-order system. In this paper, an empirical gramian balanced nonlinear model reduction strategy is examined for the simplification process of dynamic system descriptions. The empirical gramians may be computed using either experimental or simulation data. These gramians are then balanced and unimportant system dynamics truncated. For comparison purposes, a Taylor Series linearization will also be introduced to linearize the original nonlinear system about an equilibrium operating point and then a balanced realization linear reduction strategy will be applied. To demonstrate the functionality of each model reduction strategy, two nonlinear dynamic system models are investigated and respective transient performances compared.


2010 ◽  
Vol 4 (1) ◽  
pp. 224-231 ◽  
Author(s):  
Shichun Yang ◽  
Ming Li ◽  
Haoyu Weng ◽  
Bao Liu ◽  
Qiang Li ◽  
...  

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