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By Aleksandar Lazinica

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For the sake of clarity a selection of the control schemes and design parameters presented in section 4 has been made. The focus is on centralized and 32 Automation and Robotics decentralized control (with optimized parameters) and its comparison to disturbance observer based control via input balancing. Sliding mode control with integral action is presented as nonlinear control scheme to compare nonlinear design performance to linearization techniques based ones. 1 Experimental setup and performance criteria For control purposes the concept of skill primitives is used.

Thus we have to 48 Automation and Robotics [ compute a principal point offset C 0 x C0 y ] T from the sensor center, and extend the camera matrix by this parameter so that the projected point can be correctly determined in the Sensor CS (shifted parallel to the Camera CS). Consequently, we have the following mapping: [x C y C z ] C T ⎡ xC → ⎢ f C C + C Ox ⎣ z ⎤ xC f C C + C Oy ⎥ z ⎦ T Introducing this parameter to the camera matrix results in ⎡ fC K = ⎢⎢ 0 ⎢⎣ 0 0 fC 0 C Ox C Oy 1 ⎤ ⎥ ⎥ ⎥⎦ As CCD cameras are never perfect, it is most likely that CCD chips have pixels, which are not of the shape of a square.

This can be matched by use of disturbance observers as the concept of input balancing, reducing Cartesian distortion and time integral of tracking error. 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.

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