Khaled S. Hatamleh

A UAV MODEL PARAMETER IDENTIFICATION METHOD: A SIMULATION STUDY

Dynamics modeling of Unmanned Aerial Vehicles (UAVs) is an essential step for design and evaluation of an UAV system. Many advanced control strategies for nonlinear dynamical or robotic systems which are applicable to UAVs depend upon known dynamics models. The accuracy of a model depends not only on the mathematical formulae or computational algorithm of the model but also on the values of model parameters. Many model parameters are very difficult to measure for a given UAV. This paper presents the results of a simulation based study of an in-flight model parameter identification method. Assuming the motion state of a flying UAV is directly or indirectly measureable, the method can identify the unknown inertia parameters of the UAV. Using the recursive least-square technique, the method is capable of updating the model parameters of the UAV while the vehicle is in flight. A scheme of estimating an upper bound of the identification error in terms of the input data errors (or sensor errors) is also discussed.

The Unscented Smooth Variable Structure Filter Application Into a Robotic Arm

Robotic arms are becoming increasingly popular in industrial applications. However, improving the response and accuracy of robotic arms while reducing their cost has become challenging. The Kalman Filter (KF) has attracted a significant amount of research as it improves the control quality by filtering the feedback signal. On the other hand, KF solution becomes very challenging when the system under study is nonlinear. This work proposes a new online state estimation algorithm that combines the Smooth Variable Structure Filter (SVSF) with the Unscented Kalman Filter (UKF). The proposed method overcomes the limitations of SVSF and UKF in terms of stability and sensitivity to noise. A simulation study is conducted in this paper to demonstrate the results of the proposed method when applied to estimate the states of a PRRR industrial robotic arm.Copyright

UAV dynamics model parameters estimation techniques: A comparison study

Unmanned Aerial Vehicles (UAVs) dynamics modeling and parameter estimation has recently occupied great interest due to their vast use in military, civilian, industrial and agricultural applications. Accurate Online UAV parameter estimation is essential for robust autonomous control design. This study presents two different online UAV parameter estimation methods; the smooth variable structure filter (SVSF) method and the recursive least squares (RLS) method. This work presents the application of the SVSF method; including chattering signals information which previously was applied to linear models, over a nonlinear dynamics model. Moreover, the work presents a simulation study to assess the performance of the methods in terms of accuracy and speed of convergence when applied to estimate the parameters of a general Quadrotor dynamics model. The better method might be considered for deployment in an experimental UAV parameter estimation project under run by the authors.

In-flight UAV Model Parameter Identification: A Simulation Study

Dynamics modeling of Unmanned Aerial Vehicles (UAVs) is an essential step for design and evaluation of an UAV system. Many advanced control strategies for nonlinear dynamical or robotic systems which are applicable to UAVs also rely on known dynamics models. The accuracy of a model depends not only on the mathematical formulae of the model but also on the values of model parameters. Model parameters are very difficult to measure for a given UAV. This paper presents the results of a simulation based study of an in-flight model parameter identification method. Assuming the motion state of a flying UAV is directly or indirectly measureable, the method can identify the unknown inertia parameters of the UAV. Using the recursive least-square technique, the method is capable of updating the model parameters of the UAV while the vehicle is in flight.

The sigma-point central difference smooth variable structure filter application into a robotic arm

Recent Mobile-robots/Robotic-manipulators based industrial applications require accurate control despite the blurry and the noisy feedback signals. As a result, there is an increasing demand for new estimation techniques and filters to overcome accompanying system disturbances especially when nonlinearity present in the system. Industrial applications control quality will improve if a robust filter is used to reduce the effect of noise and to improve the quality of feedback signals by handling those nonlinearities. In this work, a new filter that combines the Smooth Variable Structure Filter (SVSF) with the Central Difference Kalman Filter (CDKF) is proposed. The presented method results in robust, stable and accurate estimation algorithm for motion states which are measured to be feedback signals. Results are demonstrated by applying the proposed filter to estimate the states of a 4-axis industrial robot arm with one Prismatic, and three Rotational joints (PRRR).

Development of a Special Inertial Measurement Unit for UAV Applications

Dynamics modeling is becoming more and more important in the development and control of unmanned aerial vehicles (UAV). An accurate model of a vehicle requires good knowledge of the dynamics properties and motion states, which are usually estimated with the help of integrated inertial measurement units (IMUs). This work develops a special six degrees of freedom IMU, which has the capability of measuring the angular accelerations. This paper introduces the design of the new IMU along with its sensor models and calibration procedures. The work introduces two experimental methods to verify the calibrated IMU readings. The IMU was designed to support an on-line methodology to estimate the parameters of UAV’s dynamics model that is currently being developed by the authors. [DOI: 10.1115/1.4007122]

A comprehensive comparison of sigma-point Kalman filters applied on a complex maneuvering road

In this paper, a comprehensive comparison is made of the following sigma-point Kalman filters: unscented Kalman filter (UKF), cubature Kalman filter (CKF), and the central difference Kalman filter (CDKF). A simulation based on a complex maneuvering road (an s-path) is used as a benchmark problem. This paper studies the response, stability, robustness, convergence, and computational complexity of the filters. Future work will look at implementing the methods on a robot built for experimentation.

Application of SMC and NLFC Into a PRRR Robotic Arm

Industrial robotic arms are widely used nowadays. Accuracy and efficiency that fulfill user’s requirements are achieved through robust controller. This paper investigates dynamics modeling and control of a four DOF (PRRR) robot that is dedicated to perform a Pick-and-Place move of a certain product. The arm is undergoing manufacturing process. Forward and inverse kinematics solutions are introduced to solve the joint space trajectories associated with the desired End Effector (EE) Cartesian space path. The performance of two controllers under the presence of model uncertainties is inspected through a simulation study; Non-Linear Feedback Control (NLFC) and Sliding Mode Control (SMC) are designed and tested over the required joint space trajectories and Cartesian space path. Results showed that NLFC achieved better results than SMC in terms of RMSE when model uncertainties were absent. However, when model uncertainties were introduced, SMC performance was more robust than NLFC. Simulation results are very encouraging towards using the SMC over the actual robotic arm.© 2014 ASME

An UAV Model Parameter Identification Method

This paper presents a preliminary study of an in-flight model parameter identification method using a hardware-in-the-loop experiment-testbed. Assuming that the motion state of the UAV is known, the method estimates the unknown inertia and other dynamics-model parameters from a linearized dynamics model of an UAV. Using the recursive least-square technique, the method can update the dynamics parameters of the UAV while it is in flight. The method has been demonstrated by a simulation-based study; this work tests the method using a hardware-in-the-loop testbed. Nomenclature g = Generalized gravity force vector. p J = Total moment of inertia about the pitch axis. y J = Total moment of inertia about the yaw axis. θθ

Scanning electron microscope fine tuning using four-bar piezoelectric actuated mechanism

Abstract Scanning Electron Microscopes are extensively used for accurate micro/nano images exploring. Several strategies have been proposed to fine tune those microscopes in the past few years. This work presents a new fine tuning strategy of a scanning electron microscope sample table using four bar piezoelectric actuated mechanisms. The introduced paper presents an algorithm to find all possible inverse kinematics solutions of the proposed mechanism. In addition, another algorithm is presented to search for the optimal inverse kinematic solution. Both algorithms are used simultaneously by means of a simulation study to fine tune a scanning electron microscope sample table through a pre-specified circular or linear path of motion. Results of the study shows that, proposed algorithms were able to minimize the power required to drive the piezoelectric actuated mechanism by a ratio of 97.5% for all simulated paths of motion when compared to general non-optimized solution.