By Juan Manuel Ramds Arreguin (ed.)
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As a drawback, however, a parameter mismatch leads to a loss of damping resulting in a higher overshooting during settling time. This can be improved by explicitly considering model- and parameter-uncertainties via sliding mode control at the cost of position dependent settling dynamics. e. computed torque control reveals a good performance, and becomes handy when the direct kinematic problem is not computational efficient anymore. , 2004) with experimental benchmarking. CC CT IB SMC Path accuracy + o ++ + Tracking o + + ++ Axis coupling - - - + Robustness against model uncertainties o o - + Disturbance rejection o o + ++ Axis independent design + o + + Velocity noise + ++ ++ o Chattering ++ ++ + o Execution time + o ++ - (++) Tab.
Performance criteria could easily be extended – selected set is sufficient for an overview of performance instead of a claim to be overarching. 2 Data presentation Plots of experimental results and data concerning trajectory are given in fig. 15-19, and used for benchmarks in the following. It can be seen that overshooting during trajectory follow up is in general of higher value than during settling time, due to chosen high dynamics. Examining average settling time on centralized and decentralized control reveals that disturbance observers improve this property as expected by theoretical analysis in section 5.
It is an abnormal rendering of lines of an image, which most commonly appear to be bending inward (pincushion distortion) or outward (barrel distortion), as shown in Fig. 2. Fig. 2. Distortion: lines forming pincushion (left image) and lines forming a barrel (right image) Since distortion is a principal phenomenon that affects the light rays producing an image, initially we have to apply the distortion parameters to the following normalized camera coordinates 46 Automation and Robotics ⎡ xC GC rNormalized =⎢ C ⎣z T yC ⎤ ⎥ = [x norm zC ⎦ y norm ] T 2 2 Using the above and letting h = xnorm + y norm , we can include the effect of distortion as follows: ( ( ) ) xd = 1 + k1h 2 + k 2 h 4 + k5 h 6 xnorm + dx1 yd = 1 + k1h 2 + k 2 h 4 + k5 h 6 ynorm + dy1 (3) where xd and yd stand for normalized distorted coordinates and dx1 and dx2 are tangential distortion parameters defined as: ( ) 2 dx1 = 2k3 xnorm ynorm + k 4 h 2 + 2 xnorm 2 dx2 = 2k3 h 2 + 2 ynorm + 2k 4 xnorm ynorm ( ) (4) The distortion parameters k1 through k5 describe both radial and tangential distortion.
Automation and Robotics by Juan Manuel Ramds Arreguin (ed.)