A Novel Trajectory Tracking Methodology Using Structured Adaptive Model Inversion For Uninhabited Aerialvehicles
this paper, we introduce Structured Adaptive Model Inversion (SAMI) and demonstrate global stability and bounded tracking for a class of controllers designed using SAMI, in the presence of bounded disturbances and uncertain model parameters. There has been previous extensions of the conventional Mod...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
| Published: |
2000
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| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.209 http://aero.tamu.edu/~kamesh/Papers/ACC00.pdf |
| Summary: | this paper, we introduce Structured Adaptive Model Inversion (SAMI) and demonstrate global stability and bounded tracking for a class of controllers designed using SAMI, in the presence of bounded disturbances and uncertain model parameters. There has been previous extensions of the conventional Model Reference Adaptive Control (MRAC) approach to Structured MRAC problem 6'7, by imposing the condition that the kinematic subset of differential equations are exactly known, and all learning restricted to the acceleration subset of differential equations. References 6'8 also show examples of tracking a reference trajectory for UAV where adaptive controller has been used to accommodate model uncertainties. Their approach for an adaptive control law has been based on Ref. 5. But in these references, it has been assumed that it is known a priori whether the uncertain control influence matrix is positive definite or negative definite and the stability of tracking dynamics is not guaranteed in all cases. In this paper we present a formulation which does not assume the knowledge of positive or negative definiteness of control influence matrix. The adaptive laws determined in this paper are related to those obtained in Ref. 4, though the analysis has been approached quite differently. In this paper we choose an ideal model for tracking dynamics and then apply Dynamic Model Inversion to solve for the controls explicitly. An adaptive structure is then wrapped around this model inverse controller to account for the uncertainties in the system and guarantee tracking stability in the presence of model errors. The controller structure is designed, seeking to drive the error between the reference and the system states to zero with specified error dynamics, and we show that the closed loop. |
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