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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]

An engine with a cylinder dedicated to providing enriched EGR (D-EGR) has been demonstrated by Southwest Research Institute (SwRI). A vehicle equipped with the D-EGR engine showed more than 10% of fuel economy improvement over various driving cycles. Despite a much higher level of EGR than a conventional engine, combustion in the engine is maintained stable since reformates such as H2 and CO from rich combustion increase dilution tolerance. Thus, it is deemed that precise real-time information of the level of reformates in the engine is critical to maintain stable combustion especially during transients. In this paper, a method to estimate intake burned gas and reformate mass fractions of the D-EGR engine is developed and validated through simulations. The estimated fractions can be used to control spark timing in real-time to prevent knocking or misfire during transients in the D-EGR engine. [ABSTRACT FROM AUTHOR]

This paper proposes a robust fault-tolerant control algorithm for a three-axis satellite. In this regard, an adaptive sliding attitude control algorithm is suggested, which has the capability of fault estimation in the satellite actuators and correction of their effects. For this, the disturbances due to environmental effects and actuator failures and also the satellite unknown parameters are estimated by the adaptive updating law; the sliding mode algorithm compensates the errors due to estimation process. In the suggested design process, the sliding surface is selected so that the unwinding and singularity problems are solved, and also a compensator part is included to remove unstable equilibrium points. In this paper, the failure mode effects criticality analysis have been done to classify different failure modes of reaction wheel according to their severity and probability of occurrence. Accordingly, the critical failure modes and their effects at the control system level are derived. It is shown that the derived critical failures lead to small or severe variations in the generated torques of reaction wheels for which a supervision level will be proposed to correct their effects. Finally, different simulations are conducted to validate expected performance of the suggested algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of unknown input estimation for a class of nonlinear systems with mixed nonlinear terms, namely Linear Parameter Varying (LPV) parts and purely Lipschitz nonlinearities. Three new unknown input estimation algorithms are proposed, where each algorithm depends on the distribution of the unknown inputs in the system. These algorithms provide estimation of the maximum possible unknown inputs in a system, contrarily to the methods available in the literature, which consider only particular cases. Before introducing these estimation algorithms, a general LMI-based H ∞ − observer design methodology is provided, as a preliminary result, for a class of nonlinear descriptor systems with nonlinear outputs. To this end, a specific Lyapunov function is exploited to avoid derivatives of the disturbances. The proposed LMI conditions are less conservative than those existing in the literature. This is due to the specific Lyapunov function, the use of Young inequality in a judicious way, and the reformulation of the Lipschitz inequality. The proposed algorithms are applied to a wastewater treatment model to show their effectiveness and performances. [ABSTRACT FROM AUTHOR]

As is known, estimation of the dynamic states of a vehicle plays an important role in safety control, but it is difficult to obtain the vehicle states accurately without expensive measurement instruments because of the non-linear and huge hysteretic characteristics. Nowadays, many methods have been adopted to solve this problem, the results of which are not ideal because it is assumed that the key parameters are constant, in particular in severe manoeuvres. This paper develops a novel estimation method for the vehicle states using the extended Kalman filter with a fusion algorithm. First, the optimal key parameters (the equivalent roll stiffness and the equivalent roll damping) are identified by genetic algorithms using the data from the relationship between the key parameters and the vehicle real-time states. Then, a novel non-linear observer for the side-slip angle and the roll angle is established on the basis of a four-degree-of-freedom vehicle model by utilizing the identified key parameters and the sensors mounted on normal vehicles. The performance of the observer is investigated using both simulations and real-vehicle experiments. The results demonstrate the reasonable accuracy of the estimation method proposed in this paper. [ABSTRACT FROM AUTHOR]

The electromechanical brake (EMB) is expected to be utilized for future brake systems due to its many advantages. In this paper, keeping commercialization of the EMB in mind, a new EMB clamping force controller is proposed to overcome the limitations of the existing controller, namely, the extra cost for sensor installation and response delay. To design the controller, both mechanical parts and electrical parts in the EMB have to be mathematically analyzed. Also, dynamic models, clamping force, and friction torque are estimated to generate some feed-forward terms of the controller. With an estimation of the contact point where brake pads start to come into contact with a disk wheel, the clamping force is expressed as a polynomial curve versus the motor angle. The estimated clamping force is evaluated in comparison with measured values by a load cell. The proposed controller is based on an adaptive sliding mode control method with an adaptive law reducing errors of the friction torque model. Lastly, the performance of the entire control system is compared with that of the existing controller on a test bench. [ABSTRACT FROM AUTHOR]

Low-pressure cooled exhaust gas recirculation is one of the most promising technologies for improving fuel efficiency of turbocharged gasoline direct injection engines. To realize the beneficial effects of the low-pressure cooled exhaust gas recirculation, the accurate estimation of the low-pressure cooled exhaust gas recirculation rate is essential for precise low-pressure cooled exhaust gas recirculation control. In this respect, previous studies have suggested in-cylinder pressure-based low-pressure cooled exhaust gas recirculation models to obtain the low-pressure cooled exhaust gas recirculation rate into the cylinders with fast response. However, these methods require considerable manual process of feature engineering to extract and analyze the combustion characteristics from the cylinder pressure traces. Furthermore, the performance of the entire model is limited solely to certain hand-crafted characteristics and their mathematical formulations. To resolve these limitations, we propose an in-cylinder pressure-based convolutional neural network for low-pressure cooled exhaust gas recirculation estimation. Because the convolutional neural network model automatically learns the complex function between the raw input of the high-dimensional cylinder pressure traces and the low-pressure cooled exhaust gas recirculation rate through an end-to-end deep learning framework, this convolutional neural network model provides a more effective and precise modeling process compared to the conventional combustion characteristics-based regression models. The proposed convolutional neural network model consists of the input layer with the previous consecutive cycles of the pressure traces to resolve the model uncertainty from cycle-to-cycle variations. This input layer is connected to one convolutional layer, two fully connected layers, and the final output layer that is the target low-pressure cooled exhaust gas recirculation rate. The proposed model was trained, validated, and tested using a total of 50,000 cycles of engine experimental data under various transient driving conditions. The remarkable accuracy of the proposed model was evaluated with R2 values over 0.99 and root mean square error values of less than 1.5% under the transient conditions. Moreover, the real-time performance and low memory requirement were also verified on the target embedded platform.. [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]

Due to the drawbacks in the two typical iterated divided difference filters with additive measurement noise, a state augmentation-based iterated divided difference filter (SIDDF) is proposed in this paper. In each iterated step of the measurement updates of the SIDDF, the state is firstly augmented with the measurement noise and then propagated using the same measurement update steps as the traditional divided difference filter, which made the state statistically independent of the measurement noise in the iterated steps. This filter approach is then applied to a benchmark problem of estimating the trajectory of an entry body from discrete-time noisy range data measured by a radar system. Simulation results show that the proposed filter algorithm can produce better estimation results than those of the previous filter algorithms. [ABSTRACT FROM AUTHOR]

This paper aims to uncover the spatial distribution of waste mobile phones and perform a preliminary optimization of the location of waste mobile phone recycling centers in China. This study first selects the best-suited among the logistic, Gompertz, and Bass models to forecast the per capita holding quantity of mobile phones. Combined with the population projected by the GM(1,1) and logistic models, the spatial distribution of waste mobile phones is uncovered through the stock-based model. Finally, a mixed-integer programming model is established to optimize the location of recycling centers upon the spatial distribution of waste mobile phones. Accordingly, the logistic model is concluded as the best-suited in terms of forecasting the per capita holding quantity of mobile phones for all 31 provinces in mainland China. The spatial distribution of waste mobile phones indicates the largest number in Guangdong, Shandong, and Jiangsu while the fewest are in Xizang, Qinghai, and Ningxia, in 2016. Furthermore, a remarkable increase in waste mobile phones from 2016 to 2036 in Guangdong and Beijing is projected. The location planning study indicates that a total of 175 recycling centers will be required to cover all waste mobile phones in China in 2036. Guangdong, Beijing and Zhejiang are the top three provinces regarding the number of recycling centers, with 29, 12, and 11 centers, respectively. The authors also propose the effective promoting of waste mobile phone recycling through regional cooperation and the establishment of regional management centers. The results of this study are expected to benefit waste mobile phone recycling in China. [ABSTRACT FROM AUTHOR]

Urea-based selective catalytic reduction systems have become a promising technique in controlling nitrogen oxide emissions from diesel engines in the past decade. One of the continuing issues is the cross-sensitivity characteristics of the current nitrogen oxide sensors to ammonia in the exhaust, which makes nitrogen oxide sensor readings inaccurate in the presence of ammonia. Although prototype ammonia sensors for mobile selective catalytic reduction applications have been introduced for diesel emission control in the past few years, ammonia sensors are still too expensive to be available for mass-produced diesel engines and selective catalytic reduction systems. In order to address these issues, in this paper, a novel, cost-effective, and robust approach is proposed for estimating the key selective catalytic reduction states including the nitric oxide concentration, the nitrogen dioxide concentration, the ammonia concentration, and the ammonia surface coverage ratio, by utilizing two post-selective-catalytic-reduction nitrogen oxide sensors with different ammonia cross-sensitivity factors. Experimental verification results show that the proposed approach has the potential for precisely estimating the four selective catalytic reduction states. Such a novel approach can be instrumental in reducing the cost of selective catalytic reduction diagnosis, selective catalytic reduction estimation, and advanced selective catalytic reduction controls. [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 recent years, unmanned aerial vehicles with onboard spectral sensors have been used in detecting diseases in the agricultural fields. Geolocation, i.e. calculating the global coordinate of identified diseased regions based on images taken, is an important step in automating such a scouting operation. In this paper, the problem of geolocating multiple diseased regions in an image is studied. Based on the assumptions of stationary, two-dimensional shallow target plants and hover flight, an orthographic projection-based measurement model is developed. A probabilistic data association method is used to analyze the measurements from different target sources and a Kalman filter is designed to estimate the suspected diseased leaves’ position. To the best of the authors’ knowledge, it is the first time a data association technique is used in for locating multiple-diseased plants in agriculture applications. Additionally, the designed Kalman filter is based on conditions pertinent to small crops and is less computationally intensive than the typically used extended Kalman filter. Both simulation and ad hoc experiments are used to validate the proposed multitarget geolocation algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of state-of-charge recalibration during vehicle key-off mode from battery current, voltage and temperature measurements. A quiescent voltage battery model is formalized and two different observers are proposed; that is, a multiple model adaptive estimator and an unscented Kalman filter. Both approaches are capable of robust quiescent voltage estimation in a computationally inexpensive way. The performance of the observers is numerically quantified by exhaustive simulations within application-relevant boundary conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a battery power management strategy using the estimated lithium-ion concentration in the electrodes to prevent battery over-charging and over-discharging under aggressive driving conditions. Excessive battery operation is moderated by adjusting the allowable battery power limits through the feedback of information on the electrode-averaged lithium-ion concentration estimated by the extended Kalman filter. Uneven discretization of the radii of the solid electrode particles is used in the extended Kalman filter to reduce the computation effort. Safe battery operation with a relatively small size of the battery is possible with the proposed battery power management strategy by preventing extreme conditions, thus providing more flexibility for the development of robust and cost-effective hybrid electric vehicles. [ABSTRACT FROM AUTHOR]

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 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]

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 is concerned with the trajectory tracking control of wheeled mobile robot in the presence of nonholonomic constraint on the robot kinematics and unpredictable uncertainties related to robot dynamics. In this study, by analyzing the practical implementation of the wheeled mobile robot, the unavoidable model errors and external disturbances are merged into a synthesized term which is defined as uncertain dynamics. For attenuating the effects of static tracking error, a PI-type sliding mode manifold is proposed; particularly, in order to suppress inherent chattering, a fuzzy logic system is employed to estimate the uncertain dynamics due to its universal approximation capability. Also, adaptive schemes are applied which make the controllers much more adaptability to overcome the changing environment. Eventually, with the aid of a double closed-loop control structure, the coordinated control objectives of robot posture and uncertainties rejection are able to achieve simultaneously. Simulation studies verify the feasibility and effectiveness of the proposed control approaches. [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]

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]

The use of control systems, especially vehicle dynamic control systems, is increasing at a remarkable rate. A key parameter that affects the performance of a vehicle dynamic control system is the interaction forces between the tyre and the road since these forces determine the acceleration, braking and steering properties of a car. Consequently, the accurate estimate of these forces is highly desired. Technical limitations and cost considerations mean that it is standard practice to estimate this data instead of performing measurements. Since available tyre models are generally an algebraic representation of the actual tyre and neglect variations in road conditions and uncertainties, an improved model for vehicle dynamic control applications is required. This paper proposes a tyre model which is formulated based on the least squares method and variable exponential forgetting estimation. The proposed estimator calculates tyre forces using information obtained from the sensors in a vehicle dynamic control system. The performance and robustness of the proposed tyre model is evaluated via virtual handling simulations in ADAMS software. [ABSTRACT FROM AUTHOR]

Modulation of the effective compression ratio, a measure of the amount of compression of in-cylinder gases above intake manifold conditions, is a key enabler of advanced combustion strategies aimed at reducing emissions while maintaining efficiency, and is directly influenced by modulation of intake valve closing time. To date, the effective compression ratio has most commonly been calculated from in-cylinder pressure data, requiring reliable in-cylinder pressure sensors. These sensors are generally not found on production engines, and thus a method is needed to determine effective compression ratio without in-cylinder pressure data. The work presented here outlines an estimation scheme that combines a high-gain observer with a physically-based volumetric efficiency model to estimate effective compression ratio using only information available from stock engine sensors, including manifold pressures and temperatures and air flows. The estimation scheme is compared to experimental engine data from a unique multi-cylinder diesel engine test bed with flexible intake valve actuation. The effective compression ratio estimator was tested transiently at five engine operating points and demonstrates convergence within three engine cycles after a transient event has occurred, and exhibits steady-state errors of less than 3%. [ABSTRACT FROM AUTHOR]

The management planning of construction and demolition (C&D) waste uses a single indicator which does not provide enough detailed information. Therefore the determination and implementation of other innovative and precise indicators should be determined. The aim of this research work is to improve existing C&D waste quantification tools in the construction of new residential buildings in Spain. For this purpose, several housing projects were studied to determine an estimation of C&D waste generated during their construction process. This paper determines the values of three indicators to estimate the generation of C&D waste in new residential buildings in Spain, itemizing types of waste and construction stages. The inclusion of two more accurate indicators, in addition to the global one commonly in use, provides a significant improvement in C&D waste quantification tools and management planning. [ABSTRACT FROM PUBLISHER]

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 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]

Fault tolerant control (FTC) methods considering known faults for quadrotor applications are presented; however, unknown statistical faults characteristics particularly for the FTC design is not discussed. This study tackles this problem and presents a robust two-stage unscented Kalman filter (RTSUKF) that is robust against unknown faults and is able to accurately estimate multi-actuator faults with unknown statistical characteristics. The estimated unknown faults are then compensated through second-order sliding mode controllers which are designed for each of the translational and rotational movements. The fast convergence speed of the proposed estimator reduces the instability time interval of the quadrotor after fault occurrence. To discuss the reliability of the proposed method, stability analysis based on the Lyapunov theory is carried out for the whole closed-loop system. Finally, the robustness of the proposed FTC structure against unknown faults with its convergence speed are validated through numerical analysis. The results confirm the effective and good performance of the proposed method compared to another FTC with a conventional two-stage unscented Kalman filter. [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]

This paper describes research into modelling, estimation and control of fed batch fermentation and an application to a large fermentation plant producing secondary metabolites. The problems are examined in a general sense from the viewpoints of both biological science and control engineering. Very extensive modelling and estimation work has been carried out. Only the most significant outcomes are reported here but references to the detailed research are given. Some of the research has resulted in the development of a novel advanced supervisory system- the Intelligent Process Management System (IPMS). Experience of applying the IPMS to a large-scale production process is described. The software has been designed on a modular basis and although implemented initially at one production plant, the approach is generic and fundamentally plant-independent so that further application to a range of other plants is envisaged. [ABSTRACT FROM PUBLISHER]

In this paper a novel approach for the fault detection and diagnosis of the actuators in nonlinear systems is presented. The faults are assumed to be any unexpected changes of the parameters in the actuators, which include either abrupt changes or slowly drifting changes. Firstly a known nonlinear system is considered, where an adaptive diagnostic model incorporating the estimate of fault is constructed. The diagnostic algorithm is then developed which produces the estimate of the fault so that the error between the system output and that of the model is minimised. Unknown nonlinear systems are then studied using a feedforward neural network trained to estimate the system under healthy conditions. Taking the trained neural network as the neuro model of the system, similar detection and diagnostic algorithms to that of known systems are obtained which are still capable of producing a good estimate for the fault. The method is demonstrated using a simulated nonlinear system and good results are obtained. [ABSTRACT FROM PUBLISHER]

Microburst (MB) wind shear is one of the most important meteorological dangers threatening the aircraft (AC) safety and the life of passengers. Though there are some ground-based 3D Lidar systems to detect low-level MB wind shears to alert the pilots, there have been fewer scientific attempts to identify model-based MB parameters via AC onboard air and position data. The latter refers to the development and identification of an acceptable MB model upon which an automatic flight control (AFC) system can be designed to control the AC through wind shear microburst. In essence, accurate knowledge of MB model is an essential prerequisite for design and analysis of AFC systems that can safely fly the AC against microbursts, especially in crucial phases of flight such as takeoff and landing. The present study focuses on online estimation of MB parameters whose results pave the way for effective MB autopilot designs for safe flights through MB. The proposed task is accomplished via a model-based approach using the AC six degrees of freedom (6 DoF) equations of motion (EOM) integrated with the latest verified model of the MB utilizing the extended Kalman filter (EKF). In addition to a sensitivity analysis to determine the key MB model parameters, the performance of the estimation process is enhanced via a hybridization of the genetic algorithm (GA) with the EKF. The results are promising and indicate that the proposed scheme can identify the MB model parameters with sufficient accuracy needed for online applications with AFC design. [ABSTRACT FROM AUTHOR]

Limited driving range and availability of charging infrastructures are still among the main barriers of adoption of electric vehicles (EVs) in the market. Combination of those limiting factors causes 'range anxiety' in EV users. While different EV battery technologies and charging infrastructures are under development, one short-term solution to reduce EV users' range anxiety is to provide the EV user with an accurate range estimation. In this study, an EV range estimation technique is proposed that recognises the current driving pattern and then classifies it into one of the predefined clusters (driving modes). The future energy consumption per kilometre is then tuned according to the average energy consumption of each cluster. Having an updated energy consumption rate, the EV range is calculated based on the battery state-of-charge. Different features are considered for driving pattern clustering where 'average speed' and 'average power' were identified as the best choices for this application. The effectiveness of the proposed EV range estimator is validated using real driving data that gives an average error of 9% in EV energy consumption estimation ahead. [ABSTRACT FROM AUTHOR]

Cycle-to-cycle combustion variability in spark-ignition engines during normal operation is mainly caused by random perturbations of the in-cylinder conditions such as the flow velocity field, homogeneity of the air-fuel distribution, spark energy discharge, and turbulence intensity of the flame front. Such perturbations translate into the variability of the energy released observed at the end of the combustion process. During normal operating conditions, the cycle-to-cycle variability (CCV) of the energy release behaves as random uncorrelated noise. However, during diluted combustion, in either the form of exhaust gas recirculation (EGR) or excess air (lean operation), the CCV tends to increase as dilution increases. Moreover, when the ignition limit is reached at high dilution levels, the combustion CCV is exacerbated by sporadic occurrences of incomplete combustion events, and the uncorrelation assumption no longer holds. The low or null energy released by partial burns and misfires has an impact on the following combustion event due to the residual gas that carries burned and unburned gases, which contributes to the deterministic coupling between engine cycles. Many residual gas fraction estimation methods, however, only address the nominal case where complete combustion occurs and combustion events are uncorrelated. This study evaluates the efficacy of such methods on capturing the effects of partial burns and misfires on the residual gas estimate for high-EGR operation. The advantages and disadvantages of each method are discussed based on their ability to generate cycle-to-cycle estimates. Finally, a comparison between the different estimation techniques is presented based on their usefulness for control-oriented modeling. [ABSTRACT FROM AUTHOR]

As active chassis controllers are becoming increasingly complex and sophisticated, the performance of these controllers is very sensitive to a vehicle's inertial parameters such as the vehicle mass and mass centre height. Also, these parameters vary with the number of passengers and with different load situations. In this paper, in order to determine the vehicle mass during arbitrary vehicle manoeuvring, an online estimation system is proposed considering available driving conditions, which includes two estimation algorithms. One is designed on the basis of the vehicle's longitudinal motion using the recursive least-squares method with the disturbance observer technique. The other is designed on the basis of the vehicle's lateral motion by estimating the lateral velocity. Then, two estimation algorithms are combined to extract the vehicle mass information using the recursive least-squares method. The performance of the proposed estimation system is demonstrated through experimental tests. [ABSTRACT FROM AUTHOR]

There is increasing interest in arranging festivals or special events in many cities. This paper presents an econometric model to account for the tourism accommodation impact of such events. The autoregressive count data model incorporates some of the more important factors in the planning and evaluation of an event, such as spare capacity, displacement effects and the costs that visitors face. The results for two large Swedish festivals indicate that there are some displacement effects but that the net tourism effect is positive, since the average visitor stays longer during festival periods. In the final year of the sample the festival increased the accommodation receipts for the hotels in Stockholm and Gothenburg by 2% and 6%, respectively. [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]

Trolling mode atomic force microscopy (TR-AFM) can considerably reduce the liquid-resonator interaction forces, and hence, has overcome many imaging problems in liquid environments. This mode increases the quality factor (QF) significantly compared with the conventional AFM operation in liquid; therefore, the duration to reach the steady-state periodic motion of the oscillating probe is relatively high. As a result, utilizing conventional imaging techniques, which are based on measuring the amplitude and phase, are significantly slower when compared to our proposed method. This research presents a high-speed scanning technique based on an estimation law to obtain the topography of various samples utilizing a two-degree-of-freedom model of TR-AFM. The effect of the nanoneedle tip horizontal displacement on the estimation process is investigated, and a solution to compensate for its undesirable effect is also presented. [ABSTRACT FROM AUTHOR]

The current global food production is enough to meet the caloric needs of the 7 billion individuals. On the other hand, 821 million people are currently malnourished. Living on a planet with this contradiction should lead to more effective actions in combating food insecurity. Food loss and waste contribute to this scenario, both in the economic sphere and in the use of inputs such as water, energy and work for the production process, and greenhouse gases emissions. The lack of data and a precise method of quantification should not prevent the producing countries and food suppliers, such as Brazil, from quantifying their food loss and waste. Through a methodology used by Oelofse and Nahman, we performed an exercise in quantifying FLW in the Brazilian food supply chain. Thus, an improved understanding of this topic can be initiated. An annual average of loss and waste of 82,200 tons was identified between the years 2007 and 2013. This represents 42% of the average national food supply for the period. The average amount of loss and waste were 427 kg/inhabitant/year. The edible portion corresponds to 327 kg/inhabitant/year. Specific studies that quantify and detail the loss and waste of food by the productive chain are necessary to assist in the resolution of this problem. [ABSTRACT FROM AUTHOR]

This article analyses pay determination as a process of commensuration as well as a process in which commensuration can fail. The analysis is based on an empirical study of two collective firms in Germany and the United Kingdom and their attempts to self-determine fair pay. Due to the formal equality of members and their democratic decision-making processes, these cases are a specifically interesting context for studying the determination of pay. Through the analysis of a failed attempt at finding a formula for fair pay, as well as a fragile compromise formula, a contribution is made to the literature on commensuration and the construction of compromises. The article also extends this literature by explaining the obstacles to the creation of a compromise that would go beyond the need for a common interest. Callon and Muniesa's work on calculation is used to clarify the steps that are necessary to move from questions of worth to the assessment of worth and its expression in measures. To introduce the question of legitimacy in evaluation processes, Callon and Muniesa's framework is supplemented with Boltanski and Thévenot's work on critical capacities. [ABSTRACT FROM AUTHOR]

The precise estimation of the battery's state of charge is one of the most significant and difficult techniques for battery management systems. In order to improve the accuracy of estimation of the state of charge, the forgetting-factor recursive least-squares method is used to achieve online identification of the model parameters based on the first-order RC battery model, and a back-propagation neural-network-assisted adaptive Kalman filter algorithm is proposed. A back-propagation neural network is established by using the MATLAB neural network toolbox and is trained offline on the basis of the battery test data; then the trained back-propagation neural network is used to realize the online optimized results of an adaptive Kalman filter algorithm for estimation of the state of charge. The proposed methodology for estimation of the state of charge is demonstrated using experimental lithium-ion battery module data in dynamic stress tests. The results indicate that, in comparison with the common adaptive Kalman filter algorithm, the back-propagation-adaptive Kalman filter algorithm significantly improved precise estimation of the state of charge. [ABSTRACT FROM AUTHOR]

A model-based indicated torque estimation method for a turbocharged diesel engine is presented in this study. The proposed model consists of two submodels: a steady-state indicated torque model; a transient torque coefficient model using the Elman neural network. Experiments are designed to acquire the database for the model. The optimal parameters of the Elman neural network are determined; the results show that the mean absolute percentage error of the transient torque coefficient for the estimated values using the Elman neural network and the experimental values is within 2% and the maximum error is about 7%. A comparison of the usability of the back-propagation network and that of the Elman neural network for transient estimation problems is studied; the results show that the Elman neural network is more applicable in terms of the transient accuracy and the convergence time. To validate the accuracy of the model, the experimental results for a new engine speed with two new processes are employed as test data; it is shown that the mean absolute percentage error of the indicated torque is within 2% and the maximum error is about 6%. Furthermore, explicit formulation of the Elman neural network model is acquired and rewritten as C code. Then, online validation is conducted and the results show that the mean absolute percentage error of the indicated torque is within 6%, with a maximum error of 15%. [ABSTRACT FROM AUTHOR]

The slope angle is one of the most influential factors which limit the control performance and the energy efficiency of not only internal-combustion engine vehicles but also electrified vehicles such as hybrid vehicles and plug-in hybrid electric vehicles. However, measuring the slope angle is not easy because slope sensors are not sufficiently accurate and they are too expensive to implement in vehicles. In this study, the climbing angle as well as the slope angle are considered in the presence of yawing motion on a sloping road, and both angles are estimated. In order to describe the combined vehicle motions when turning on a sloping road, a new three-dimensional dynamics model is proposed including the heading angle with respect to the road slope orientation. The extended Kalman filter is designed on the basis of the three-dimensional dynamics model on a sloping road, and the key parameters for the sloping road are updated using the geometry relationship. The proposed estimation method is evaluated using simulations and field tests with a hybrid electric vehicle. [ABSTRACT FROM AUTHOR]

A novel model-based algorithm for fault detection in stochastic linear and non-linear systems is proposed. The non-linear minimum variance estimation technique is used to generate a residual signal, which is then used to detect actuator and sensor faults in the system. The main advantage of the approach is the simplicity of the non-linear estimator theory and the straightforward structure of the resulting solution. Simulation examples are presented to illustrate the design procedure and the type of results obtained. The results demonstrate that both actuator and sensor faults can be detected successfully. [ABSTRACT FROM AUTHOR]

In practical engineering applications, the performance of the standard cubature Kalman filter (CKF) and its square-root version can be severely degraded due to outliers in measurement or contaminated distribution. In order to address the problem, a robust version of CKF is presented using Huber’s M-estimation methodology and square-root filtering framework. By making use of the Huber technique to reformulate the measurement update of CKF in square-root filtering framework, the proposed filter can exhibit robustness and numerical stability against deviation from Gaussian distribution assumption. In simulation tests, four versions of CKF—the standard, the square-root, Huber-based, and the proposed are evaluated in terms of estimation accuracy, numerical stability, and robustness under Gaussian and non-Gaussian distribution. The results are concluded that the square-root version outperforms the others under Gaussian distribution, whereas the proposed filter has improved performance in maintaining the robustness and numerical stability under non-Gaussian distribution. The investigated robust framework can be extended to other Gaussian filtering algorithms and the study is expected to facilitate applications of CKF in practical engineering as well. [ABSTRACT FROM PUBLISHER]

Dynamic soaring can save fuel by the extraction of low-altitude wind shear energy. The problem of generating energy-saving trajectories in real time for an unmanned powered sailplane is considered. A method called ‘inverse dynamics in the virtual domain’ is investigated. The method results in rapid solution generation and is suitable for real-time applications. Three-dimensional unmanned aerial vehicle equations of motion and a non-linear wind gradientmodel are used. Trajectories are generated to minimize the total power used per horizontal distance travelled for cross-wind travelling and in the total power used per total time in air for loitering mode. Trajectory following issues are addressed and an architecture for the control system is briefly described. A method for the estimation of wind shear gradient parameters is proposed. Validation is performed using a full six-degree-of-freedom model of a powered sailplane to verify the trajectory generation method. [ABSTRACT FROM AUTHOR]

In this article, we propose a Moving-Horizon State-Estimation method, applied to a neural dynamical process model. Firstly, the approach chosen to represent a nonlinear dynamical system by a neural network is explained. After that, the MHSE method, used to perform the state estimation, is presented. The algorithm performances are showed on a biotechnological process. The combination of the MHSE method and the neural network permits a particularly efficient estimation of the state of the process. with a nonlinear model easy to build thanks to the neural network, and with an easy tuning due to the choice of the MHSE method. [ABSTRACT FROM PUBLISHER]

Estimating the customer demand for electricity plays a crucial role in the equitable determination of customer electric utility rates. To estimate the customer demand accurately, it is necessary to determine the appropriate sample size. Due to the unique requirements imposed by the Public Utilities Regulatory Policies Act of 1978, existing procedures to determine sample size fail to provide the optimal sample size that will satisfy the re quirements at the minimum cost. The objective of this study is to develop and describe a simulation based procedure that is capable of achieving both criteria. The effectiveness of the pro cedure is tested for selected distributions of electrical demand. The study also identifies other instances where similar sample size determination problems occur and how the newly developed procedure can be used in these situations as well. [ABSTRACT FROM PUBLISHER]