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In this paper, an on-line closed-loop identification of mainly mass and inertia of an under-actuated aerial vehicle, namely a quadrotor acting as an aerial manipulator, is presented. Being treated as a rigid body, only one set of eighteen- or six tri-axial linear accelerometers is used to facilitate such estimation. Force and torque disturbances acting upon the vehicle during identification are also estimated which will refine the overall estimation quality of the unknown parameters. Namely, recursive linear and nonlinear least squares methods are used to obtain initial and refined estimations, respectively. Random disturbances are introduced to the closed-loop system to ensure enough excitation of the overall system. Issues related to identifiability, stability and performance are discussed. Since the proposed method depends on general kinematical and dynamical analyses of motion, it is claimed that the presented method will be also applicable to a wider range of, mainly, aerial vehicles. [ABSTRACT FROM AUTHOR]

This paper proposes a novel continuous-time robust direct adaptive controller for the attitude control of the three-dimensional unknown chaotic spacecraft system. It considers that the plant's nonlinear terms, exogenous disturbances, and model uncertainties are unknown and bounded; the controller design is independent of the system's nonlinear terms. These controller attributes flourish the robust performance of the closed-loop and establish smooth state vector convergence to zero. The proposed controller consists of three parts: (1) a linear controller establishes the stability of the closed-loop at the origin, (2) a nonlinear controller component that autonomously adjusts the feedback gain, and (3) a nonlinear adaptive controller compensates for the model uncertainties and external disturbances using the online estimates of bounds and model uncertainties. The output of this part remains within a given upper and lower bound. The feedback controller gain is large when the state variables are away from the origin and become small in the origin's vicinity. This feature is novel and contributes to the synthesis of smooth control effort that establishes robust fast and oscillation-free convergence of the state variables to zero. The Lyapunov direct stability analysis assures the global asymptotic robust stability of the closed-loop. Computer simulations and comparative analysis are included to verify the theoretical findings. [ABSTRACT FROM AUTHOR]

In this paper, multi-model estimation and fault detection using neural network is proposed for an unknown time continuous fractional order nonlinear system. Fractional differentiation is considered based on Caputo concept and the fractional order is considered to be between 0 and 1. In order to estimate a time continuous fractional order nonlinear system with unknown term in its dynamic, single-layer and double-layer RBF neural network is used. First, a parallel-series neural network observer is designed for state estimation. Weights of the neural network are updated adaptively and updating laws are presented in fractional order form. Using Lyapunov method, it is proved that state estimation error and weight estimation error of the neural network are bounded. Parameters of the neural estimator converge to ideal parameters which satisfy excitation condition stability. Then, multi-model estimation structure of fractional order nonlinear systems is presented and its application in fault detection is investigated. Finally, simulation results are presented to show efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This paper introduces a novel metaheuristic model-based scheme for fault monitoring in squirrel cage induction motors (SCIMs). This method relies on the combination of the ant lion optimizer (ALO) and the unscented Kalman filter (UKF) to detect and quantify the number of broken bars. Contrary to the UKF-based fault diagnosis, the improved ALO-UKF algorithm tunes optimally and automatically the noise covariance matrices Q and R, which reduces the estimation errors, and then obtains an effective and accurate fault diagnosis. Firstly, a mathematical model of the fault under study has been developed based on rotor parameter value as signature. Secondly, a sixth order ALO-UKF algorithm has been synthesized for simultaneous estimation of rotor resistance and speed. Several broken bar fault conditions have been simulated. Simulation results show the effectiveness and robustness of the proposed ALO-UKF scheme in broken bar detection and identification, and exhibit a more superior performance than the simple-UKF and EKF algorithms in term of stability, accuracy and response time. [ABSTRACT FROM AUTHOR]

Calibration accuracy of micro inertial measurement unit (MIMU) will affect the navigation accuracy of micro strap-down inertial navigation system. Generally, when the application environment changes (i.e. environment temperature and humidity), the specific force and angular rate output by MIMU will be changed, which were influenced by the zero bias of accelerometers, the zero drift of gyroscopes and so on. Thus, it is necessary to carry out the field calibration for MIMU. Aiming at the application of multi MIMUs, the network dynamic field calibration method is proposed in this paper. According to the navigation attitude and velocity error models, the estimating model is established. Then, the observability for the parameters in the estimating model is analyzed. By fusing the output information of MIMUs and GPS, vehicle experiments are carried out with the designed maneuvers in order to estimate the parameters. The experiment result illustrated that the proposed network dynamic filed calibration can efficiently realize the calibration for the parameters in the model of several MIMUs simultaneously. [ABSTRACT FROM AUTHOR]

This paper is concerned with optimal trajectory control for two unmanned aerial vehicles (UAVs) in a multisource localization environment. The received signal strength (RSS) at the UAVs in specified time intervals permits passive differential RSS (DRSS)-based localization of multiple radio frequency (RF) sources with unknown transmit powers. A steering algorithm is proposed to update the UAV waypoints in order to minimize the summation of the uncertainty of the source locations. The UAV paths are optimized by maximizing the determinant of the Fisher Information Matrix (FIM). The FIM is approximated at successive waypoints using the estimated locations of the sources. In addition to maximizing the localization accuracy, the objectives of the proposed trajectory control are to minimize the number of UAVs, the mission time and the path length. As the DRSS is a non-linear measurement, an extended Kalman filter (EKF), which is a non-linear filtering technique, is considered in this paper. The efficiency of the approach is depicted through simulations. [ABSTRACT FROM AUTHOR]

Cooperative teleoperation combines two traditional areas of robotics, that is, teleoperation and collaborative manipulation. Cooperative telerobotic systems consist of multiple pairs of master and slave robotic manipulators operating in a shared environment. The most common control frameworks for nonlinear systems, that is, Proportional Derivative (PD) controllers, possess considerable deficiency in contact motion. In this paper, a novel control scheme is proposed for a nonlinear bilateral cooperative teleoperation system with time delay. In addition to position and velocity signals, force signals are employed in the control strategy. This modification significantly enhances the poor transparency when the slave robots are in collision with the environment. To cope with external forces measurement, a modified force estimation algorithm is proposed to estimate human and environment forces. The closed loop stability of the nonlinear cooperative teleoperation system with the proposed control scheme is investigated using the Lyapunov theory. The main achievement of this research is the stability of the closed loop cooperative teleoperation system in the presence of estimated operator and environmental forces. In addition, it is theoretically and experimentally proved that force reflection occurs and transparency is improved at the same time. Experimental results demonstrate the efficiency of the presented control strategy in free motion as well as when the slave robots are in contact with the environment. [ABSTRACT FROM AUTHOR]

In this paper, we present Gaussian Process Gauss–Newton (GPGN), an algorithm for non-parametric, continuous-time, nonlinear, batch state estimation. This work adapts the methods of Gaussian process (GP) regression to address the problem of batch simultaneous localization and mapping (SLAM) by using the Gauss–Newton optimization method. In particular, we formulate the estimation problem with a continuous-time state model, along with the more conventional discrete-time measurements. Two derivations are presented in this paper, reflecting both the weight-space and function-space approaches from the GP regression literature. Validation is conducted through simulations and a hardware experiment, which utilizes the well-understood problem of two-dimensional SLAM as an illustrative example. The performance is compared with the traditional discrete-time batch Gauss–Newton approach, and we also show that GPGN can be employed to estimate motion with only range/bearing measurements of landmarks (i.e. no odometry), even when there are not enough measurements to constrain the pose at a given timestep. [ABSTRACT FROM PUBLISHER]

In this paper we describe a side slip angle estimation methodology especially for a drive recorder which has limited vehicle sensing information. The drive recorder has attracted the attention of many researchers recently since its captured data contains valuable information that can enhance traffic safety. As the current algorithm of video capture and data acquisition, it detects hard braking by using acceleration sensor values and records the video image and vehicle data in that situation. However, the conventional trigger algorithm cannot be activated and the data cannot be recorded when the vehicle is in a side slip situation on a low friction road with low acceleration. We propose an estimation method of the vehicle side slip angle which uses lateral acceleration, yaw rate and velocity without requiring information about the steering wheel angle for the drive recorder configuration. In this paper we describe a synthesis of the estimation method based on a two-wheel lateral vehicle dynamics model and validation of the proposed methodology by experiments using an actual vehicle. [ABSTRACT FROM AUTHOR]

This paper introduces a systematic constraint-based approach to specify complex tasks of general sensor-based robot systems consisting of rigid links and joints. The approach integrates both instantaneous task specification and estimation of geometric uncertainty in a unified framework. Major components are the use offeature coordinates, defined with respect to object and feature frames, which facilitate the task specification, and the introduction of uncertainty coordinates to model geometric uncertainly. While the focus of the paper is on task specification, an existing velocity-based control scheme is reformulated in terms of these feature and uncertainty coordinates. This control scheme compensates for the effect of time varying uncertainly coordinates. Constraint weighting results in an invariant robot behavior in case of conflicting constraints with heterogeneous units. The approach applies to a large variety of robot systems (mobile robots, multiple robot systems, dynamic human-robot interaction, etc.), various sensor systems, and different robot tasks. Ample simulation and experimental results are presented. [ABSTRACT FROM AUTHOR]

One of the prevalent methods in linear bilateral teleoperation systems with communication channel time delays is using position and velocity signals in the control scheme. Utilization of force signals in such controllers improves the performance significantly and reduces the tracking error. Measuring force signals in such cases is one of the major problems. In this paper, a control scheme in the presence of human and environment force signals for the linear bilateral teleoperation is proposed. Due to elimination of measuring forces in the control scheme, a force estimation approach based on disturbance observers has been utilized. The proposed approach guarantees asymptotic estimation of constant forces. The estimation error would only be bounded for time varying external forces. To cope with the variation of the human and environment force, a sliding-mode-controller is used. The stability and transparency condition in the teleoperation system with the designed control scheme is derived from absolute stability concept. The designed control scheme guarantees the stability of the teleoperation system in the presence of time varying human and environment forces. Experimental results show that the proposed control scheme improves position tracking in the free motion and in contact with the environment. In addition, the force estimation approach appropriately estimates human and environment forces. [ABSTRACT FROM PUBLISHER]

Brushless DC (BLDC) motors are useful in applications such as process control, robotics, industrial automation, aerospace, electric vehicles etc. due to such advantages as the elimination of rotor losses and magnetizing current. Wider usage of BLDC motors demands optimum position control for high efficiency, accuracy and reliability. For an accurate position control, the estimation of moment of inertia and friction coefficient of the motor with load is essential. This paper incorporates the computation of the moment of inertia and friction coefficient of a BLDC motor with load at different load settings and emphasizes that load has an appreciable effect on the dynamic performance of the system. To obtain the optimum position control, a proportional plus integral plus derivative (PID) controller is employed and tuned using the PARR method. Artificial neural networks are used for computing the moment of inertia and friction coefficient of a BLDC motor with load and PID controller parameters at various load settings. Simulation results of the position control system are obtained at different load settings. From the results, it is evident that the PID controlled position control system responds to the desired position with minimum rise time, settling time and peak overshoot and is in dependent of load settings. [ABSTRACT FROM PUBLISHER]

Computer simulations play an important role in the design and validation of constrained robotic operations. The fidelity of these simulations, however, depends on the specification of contact dynamics parameters, which often need to be determined experimentally. In this paper we investigate the identification of contact parameters from complex stiff multi-point contact scenarios encountered in typical robotic operations using a recently developed least-squares-based method. This method is extended to deal with geometric uncertainties by employing a non-linear separable least-squares formulation. The latter is solved using a variable projection method, and allows simultaneous identification of contact parameters and geometric uncertainties. The conditions for observability of geometric uncertainties are derived and a regularized formulation is proposed in case the identification of geometric uncertainties is ill-conditioned. The applicability of the original method and the benefits of the extended method with identification of geometric uncertainties for the identification of the contact parameters are illustrated by means of experimental data measured with the Special Purpose Dexterous Manipulator (SPDM) Task Verification Facility (STVF) manipulator at the Canadian Space Agency (CSA). [ABSTRACT FROM AUTHOR]

This paper discusses the estimation of the parameters (including the time delay) of a generalized single-input, single-output (SISO) process model from an appropriate number of arbitrarily specified points on the process frequency response. The method involves combining an analytical approach with a least-squares approach using a gradient algorithm, to provide accurate estimates of the parameters. [ABSTRACT FROM AUTHOR]

Renewable energy plays a very important role in solving energy problems, and solar energy is one of the most important renewable sources, especially in sunny countries.This paper deals with two problems: The first one is about optimal sizing in a photovoltaic panel (PVP)-battery system, and the second consists in energy management in smart grids. To achieve the first objective, an adaptive neuro-fuzzy inference system (ANFIS) estimation algorithm is developed in order to estimate a database of instantaneous photovoltaic power. The estimated instantaneous photovoltaic power is used in an optimal algorithm to size a PVP-battery power station to supply a 1.5 kW AC load. For the second objective, a deep learning forecasting algorithm is realized to estimate the smart grid parameters so as to optimize the consumption energy. All results are checked carrying out Matlab simulation using real weather data. The simulation results give a good performance of our proposed sizing and management systems. [ABSTRACT FROM AUTHOR]