Sliding Mode Control and Vision-Based Line Tracking for Quadrotors
Bachelor thesis
Permanent lenke
https://hdl.handle.net/11250/3073586Utgivelsesdato
2023Metadata
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- Studentoppgaver (TN-IDE) [823]
Sammendrag
This thesis describes the design of Sliding Mode Control applied to quadrotor UAV flight. This is anonlinear control technique in which a discontinuous control signal is applied to drive the so-calledsliding variable to zero, which defines the sliding surface. The sliding variable should be designed insuch a way that approaching the sliding surface is beneficial to tracking the reference signals. Theadvantages of Sliding Mode Control are that the need for simplifying the underlying dynamicalmodel through linearization is avoided, it is robust and adaptive, and works even if the system to becontrolled is highly nonlinear or has model uncertainties. Sliding Mode Control has one major issueassociated with it, namely the chattering phenomena in the control inputs, which is undesirable.This can be tackled by approximating the discontinuous sign function in the control input with aapproximated continuous function, or by applying techniques such as adaptive fuzzy gain scheduling.As with other control methods, Sliding Mode Control requires tuning of the control parametersto obtain an optimal performance. In this work, genetic algorithms were investigated as a way totune the controller parameters. The findings of this thesis were combined with the design of a linetracking algorithm in order to enter the MathWorks Minidrone Competition. This thesis describes the design of Sliding Mode Control applied to quadrotor UAV flight. This is anonlinear control technique in which a discontinuous control signal is applied to drive the so-calledsliding variable to zero, which defines the sliding surface. The sliding variable should be designed insuch a way that approaching the sliding surface is beneficial to tracking the reference signals. Theadvantages of Sliding Mode Control are that the need for simplifying the underlying dynamicalmodel through linearization is avoided, it is robust and adaptive, and works even if the system to becontrolled is highly nonlinear or has model uncertainties. Sliding Mode Control has one major issueassociated with it, namely the chattering phenomena in the control inputs, which is undesirable.This can be tackled by approximating the discontinuous sign function in the control input with aapproximated continuous function, or by applying techniques such as adaptive fuzzy gain scheduling.As with other control methods, Sliding Mode Control requires tuning of the control parametersto obtain an optimal performance. In this work, genetic algorithms were investigated as a way totune the controller parameters. The findings of this thesis were combined with the design of a linetracking algorithm in order to enter the MathWorks Minidrone Competition.