New Approaches in Automation and Robotics by Harald Aschemann
By Harald Aschemann
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1998) for five (05) possible movements of vehicles, while in this paper this number is reduced to three (03) possible movements due to the Robucar structure. Second, simulation results of the neural-based navigation are presented. Finally, an implementation of the neural-based navigation on a real bi-steerable robot Robucar is given leading to a learning vehicle able to behave intelligently faced to unexpected situations. 2. Neural-based navigation approach applied to a bi-steerable mobile robot Robucar in partially structured environnments To navigate in partially structured environments, the Robucar must reach its target without collisions with possibly encountered obstacles.
3 -3 x 10 5 FB ex [m] x 10 FB BS 0 0 ez [m] 5 SM SM -5 BS -10 -5 0 5 t [s] 10 15 -15 0 BS 5 t [s] 10 15 Fig. 11. Comparison of the tracking errors in the workspace without disturbance observer. Nonlinear Model-Based Control of a Parallel Robot Driven by Pneumatic Muscle Actuators 39 Fig. 11 shows a comparison of the resulting tracking errors in the workspace for flatnessbased control (FB), backstepping control (BS) and sliding-mode control (SM). Without observer-based disturbance compensation, the best results are obtained using sliding-mode control.
15. From bottom to top (in solid), magnitude responses of Tur (top figure) and Tyr (bottom figure) for a =1,3,6. In dashed it is shown the response of the target model Tref . Fig. 16. Bode diagram for the original 11th order prefilter K 2 (dashed) and the 3rd order prefilter K 2 (solid) finally obtained by applying order reduction techniques. The use of the H∞ optimization techniques traditionally results in very high order controllers. In this case, the resulting K 2 is of order eleven. However, standard order A Model Reference Based 2-DOF Robust Observer-Controller Design Methodology 23 reduction techniques can be applied in order to reduce these orders.