RESUMO
This work presents a method for estimating the model parameters of multi-rotor unmanned aerial vehicles by means of an extended Kalman filter. Different from test-bed based identification methods, the proposed approach estimates all the model parameters of a multi-rotor aerial vehicle, using a single online estimation process that integrates measurements that can be obtained directly from onboard sensors commonly available in this kind of UAV. In order to develop the proposed method, the observability property of the system is investigated by means of a nonlinear observability analysis. First, the dynamic models of three classes of multi-rotor aerial vehicles are presented. Then, in order to carry out the observability analysis, the state vector is augmented by considering the parameters to be identified as state variables with zero dynamics. From the analysis, the sets of measurements from which the model parameters can be estimated are derived. Furthermore, the necessary conditions that must be satisfied in order to obtain the observability results are given. An extensive set of computer simulations is carried out in order to validate the proposed method. According to the simulation results, it is feasible to estimate all the model parameters of a multi-rotor aerial vehicle in a single estimation process by means of an extended Kalman filter that is updated with measurements obtained directly from the onboard sensors. Furthermore, in order to better validate the proposed method, the model parameters of a custom-built quadrotor were estimated from actual flight log data. The experimental results show that the proposed method is suitable to be practically applied.
RESUMO
Atherosclerotic plaque rupture and erosion are the most important mechanisms underlying the sudden plaque growth, responsible for acute coronary syndromes and even fatal cardiac events. Advances in the understanding of the culprit plaque structure and composition are already reported in the literature, however, there is still much work to be done toward in-vivo plaque visualization and mechanical characterization to assess plaque stability, patient risk, diagnosis and treatment prognosis. In this work, a methodology for the mechanical characterization of the vessel wall plaque and tissues is proposed based on the combination of intravascular ultrasound (IVUS) imaging processing, data assimilation and continuum mechanics models within a high performance computing (HPC) environment. Initially, the IVUS study is gated to obtain volumes of image sequences corresponding to the vessel of interest at different cardiac phases. These sequences are registered against the sequence of the end-diastolic phase to remove transversal and longitudinal rigid motions prescribed by the moving environment due to the heartbeat. Then, optical flow between the image sequences is computed to obtain the displacement fields of the vessel (each associated to a certain pressure level). The obtained displacement fields are regarded as observations within a data assimilation paradigm, which aims to estimate the material parameters of the tissues within the vessel wall. Specifically, a reduced order unscented Kalman filter is employed, endowed with a forward operator which amounts to address the solution of a hyperelastic solid mechanics model in the finite strain regime taking into account the axially stretched state of the vessel, as well as the effect of internal and external forces acting on the arterial wall. Due to the computational burden, a HPC approach is mandatory. Hence, the data assimilation and computational solid mechanics computations are parallelized at three levels: (i) a Kalman filter level; (ii) a cardiac phase level; and (iii) a mesh partitioning level. To illustrate the capabilities of this novel methodology toward the in-vivo analysis of patient-specific vessel constituents, mechanical material parameters are estimated using in-silico and in-vivo data retrieved from IVUS studies. Limitations and potentials of this approach are exposed and discussed.
RESUMO
Muscle models can be used to estimate muscle forces in motor tasks. Muscle model parameters can be estimated by optimizing cost functions based on error between measured and model-estimated joint torques. This paper is a numerical simulation study addressing whether this approach can accurately identify the parameters of the quadriceps femoris. The simulated identification task is a single joint maximum voluntary knee concentric-eccentric extension in an isokinetic dynamometer, keeping the hip fixed at a neutral position. A curve considered as the nominal torque was obtained by simulating the quadriceps femoris model exerting a maximum knee extension torque using a set of known parameter values. Three parameters, with different expected sensitivities of force estimations by Hill-type muscle models, were studied: very sensitive, sensitive and not sensitive, corresponding to slack tendon length, maximum isometric force, and pennation angle, respectively. The initial values of the parameters were randomly changed, simulating an ignorance of nominal values. EMG generation and torque measurement error models were used to obtain realistic simulated data corrupted by noise. Simulated annealing was chosen as the optimization algorithm. Different sequences of parameter identification and cost functions were tested. The best nominal torque curve reconstruction was obtained by optimizing the parameters sequentially, starting from slack tendon length using the Euclidean norm cost function. However, the simultaneous estimation of all parameters resulted in the most accurate values for the parameters, although dispersion was relatively large. In conclusion, in the present simulation study using realistic synthetic torque and EMG data, the optimization approach based on torque error curve was able to closely approximate the parameter values of the model's quadriceps femoris muscle.