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A discrete-time robust extended Kalman filter (REKF) formulation for uncertain systems expressed in terms of a set-valued state estimator is described in this paper. The robust filter and Riccati equations are derived as an approximate solution to a reverse-time optimal control problem defining this set-valued state estimator. As presented, the uncertainties are modeled by a sum quadratic constraint...
This paper describes a novel indirect adaptive control technique based on neural networks for unmanned aerial vehicles (UAV). Two neural networks are amalgamated to provide faster tracking of the commanded reference. A pre-trained internal model network and an online trained controller network form the core of the dual neural network (DNN) architecture. A Telemaster UAV equipped with various sensors...
Real time flight implementation of a neural network based black-box identification (NNID) scheme to a rotary wing unmanned aerial vehicle (RUAV) is presented in this paper. The applicability of NNID scheme for real time identification of longitudinal and lateral dynamics of the RUAV is evaluated in flight. To show the efficacy of the method for real time applications, the identification results and...
This paper proposes a backup attitude estimation scheme for small fixed-wing unmanned aerial vehicles (UAVs) in the event of gyroscopic failure. The attitude of the system is propagated in terms of 3 degrees-of-freedom (DoF) aircraft dynamics while a combined accelerometer and global positioning system (GPS) based attitude measurement scheme is used to update attitude errors. The uncertainties in...
This paper presents a novel system identification framework for small unmanned aerial vehicles (UAVs) by combining an unscented Kalman filter (UKF) estimator with a neural network (NN) identifier. The method is effective for systems with low-cost, erroneous sensors where the sensor outputs cannot be used directly for system identification and control. The UKF state estimator computes error-compensated...
Dynamic multi-objective optimization (DMO) is one of the most challenging class of optimization problems where the objective functions change over time and the optimization algorithm is required to identify the corresponding Pareto optimal solutions with minimal time lag. DMO has received very little attention in the past and none of the existing multi-objective algorithms perform satisfactorily on...
An important issue in the preliminary design of autonomous underwater vehicles (AUVs) is the selection of the optimal dimensions for the vehicle (sizing). These dimensions are dependent on the characteristics of the mission, the operating environment and the chosen technology. This paper presents a computer-based design-tool, which integrates all major design parameters to allow an efficient and rapid...
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