Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Linear system theory (2nd ed.)
Linear system theory (2nd ed.)
Nonlinear and Adaptive Control Design
Nonlinear and Adaptive Control Design
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Monocular model-based 3D tracking of rigid objects
Foundations and Trends® in Computer Graphics and Vision
Adaptive Control Tutorial (Advances in Design and Control)
Adaptive Control Tutorial (Advances in Design and Control)
Vision-based 3-D trajectory tracking for unknown environments
IEEE Transactions on Robotics
Brief Sonar-based robot navigation using nonlinear robust observers
Automatica (Journal of IFAC)
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In this work, a cascade of two estimators is proposed as the solution for a joint parameter and state estimation problem associated with a target maneuvering in three-dimensional space. A model for the target that depends on its angular speed is considered and only the target position is measured. A parameter identifier is used to obtain estimates of the target angular speed, which are then fed into an adaptive filter that estimates the position, linear velocity, and linear acceleration of the target. The synthesis of the parameter identifier resorts to Lyapunov techniques and the adaptive filter is synthesized using H"2 optimization strategies. Under persistence of excitation conditions, the error in the angular speed identification and the error in the target state estimates provided by the H"2 adaptive filter are: (i) proved to converge exponentially fast to zero in the deterministic setup, i.e., in the absence of noise, and (ii) proved to be bounded when bounded stochastic disturbances are considered and there is an upper bound on the target linear velocity and angular speed. To assess the proposed methods, simulations showing that the aforementioned stability and convergence properties hold, even when the estimates provided by an Extended Kalman Filter (EKF) diverge, are presented.