This paper investigates the problem of calibrating sensors affected by (i) heteroskedastic measurement noise and (ii) a polynomial bias, describing a systematic distortion of the measured quantity. First, a set of increasingly complex statistical models for the measurement process was proposed. Then, for each model the authors design a Bayesian parameters estimation method handling heteroskedasticity and capable to exploit prior information about the model parameters. The Bayesian problem is solved using MCMC methods and reconstructing the unknown parameters posterior in sampled form. The authors then test the proposed techniques on a practically relevant case study, the calibration of Light Detection and Ranging (Lidar) sensor, and evaluate the different proposed procedures using both artificial and field data.
Accurate and robust mobile robot localization is very important in many robot applications. Monte Carlo localization (MCL) is one of the robust probabilistic solutions to robot localization problems. The sensor model used in MCL directly influence the accuracy and robustness of the pose estimation process. The classical beam models assumes independent noise in each individual measurement beam at the same scan. In practice, the noise in adjacent beams maybe largely correlated. This will result in peaks in the likelihood measurement function. These peaks leads to incorrect particles distribution in the MCL. In this research, an adaptive sub-sampling of the measurements is proposed to reduce the peaks in the likelihood function. The sampling is based on the complete scan analysis. The specified measurement is accepted or not based on the relative distance to other points in the 2D point cloud. The proposed technique has been implemented in ROS and stage simulator. The result shows that selecting suitable value of distance between accepted scans can improve the localization error and reduce the required computations effectively.
We present an improved statistical model of the measurement process of triangulation Light Detection and Rangings (Lidars) that takes into account bias and variance effects coming from two different sources of uncertainty: {\$}{\$}(i) {\$}{\$} mechanical imperfections on the geometry and properties of their pinhole lens - CCD camera systems, and {\$}{\$}(ii) {\$}{\$} inaccuracies in the measurement of the angular displacement of the sensor due to non ideal measurements from the internal encoder of the sensor. This model extends thus the one presented in [2] by adding this second source of errors. Besides proposing the statistical model, this chapter considers: {\$}{\$}(i) {\$}{\$} specialized and dedicated model calibration algorithms that exploit Maximum Likelihood (ML)/Akaike Information Criterion (AIC) concepts and that use training datasets collected in a controlled setup, and {\$}{\$}(ii) {\$}{\$} tailored statistical strategies that use the calibration results to statistically process the raw sensor measurements in non controlled but structured environments where there is a high chance for the sensor to be detecting objects with flat surfaces (e.g., walls). These newly proposed algorithms are thus specially designed and optimized for inferring precisely the angular orientation of the Lidar sensor with respect to the detected object, a feature that is beneficial especially for indoor navigation purposes.
This paper describes a new calibration procedure for distance sensors that does not require independent sources of groundtruth information, i.e., that is not based on comparing the measurements from the uncalibrated sensor against measurements from a precise device assumed as the groundtruth. Alternatively, the procedure assumes that the uncalibrated distance sensor moves in space on a straight line in an environment with fixed targets, so that the intrinsic parameters of the statistical model of the sensor readings are calibrated without requiring tests in controlled environments, but rather in environments where the sensor follows linear movement and objects do not move. The proposed calibration procedure exploits an approximated expectation maximization scheme on top of two ingredients: an heteroscedastic statistical model describing the measurement process, and a simplified dynamical model describing the linear sensor movement. The procedure is designed to be capable of not just estimating the parameters of one generic distance sensor, but rather integrating the most common sensors in robotic applications, such as Lidars, odometers, and sonar rangers and learn the intrinsic parameters of all these sensors simultaneously. Tests in a controlled environment led to a reduction of the mean squared error of the measurements returned by a commercial triangulation Lidar by a factor between 3 and 6, comparable to the efficiency of other state-of-the art groundtruth-based calibration procedures. Adding odometric and ultrasonic information further improved the performance index of the overall distance estimation strategy by a factor of up to 1.2. Tests also show high robustness against violating the linear movements assumption.
We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser’s datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three.
We aim at developing statistical tools that improve the accuracy and precision of the measurements returned by triangulation Light Detection and Rangings (Lidars). To this aim we: i) propose and validate a novel model that describes the statistics of the measurements of these Lidars, and that is built starting from mechanical considerations on the geometry and properties of their pinhole lens - CCD camera systems; ii) build, starting from this novel statistical model, a Maximum Likelihood (ML) / Akaike Information Criterion (AIC) -based sensor calibration algorithm that exploits training information collected in a controlled environment; iii) develop ML and Least Squares (LS) strategies that use the calibration results to statistically process the raw sensor measurements in non controlled environments. The overall technique allowed us to obtain empirical improvements of the normalized Mean Squared Error (MSE) from 0.0789 to 0.0046
Traction control for off-road vehicles such as articulated all-wheel drive haulers is of great importance to improve the vehicle performance. A well-known method to reduce the slip and thereby improve the traction is to engage differential locks in the driveline of the vehicle. The drawbacks of differential locks engaged are for instance increased wear, increased fuel consumption but also reduced turnability of the vehicle. Therefore, the differentials should be locked only when necessary, ideally only when slip occurs or is about to occur. A number of methods to detect slip has been reported in the literature. Some of them utilize dynamical models of the vehicle where side-slip angles are important inputs. This paper describes an off-line estimator for the side-slip angles of an articulated vehicle based on measurements from Global Positioning System (GPS) and Inertial Navigation System (INS). The current implementation is a proof of concept and the intention is to develop a system that can be used as a reference for on-line estimators. By comparing measurements from two GPS/INS units, mounted on the front and rear part of the vehicle, it is possible to estimate the side-slip angles of both the front and rear part. The method has been tested on a Volvo A25E articulated all-wheel drive hauler equipped with two high precision GPS/INS units (NovAtel's SPAN-CPT). Tests have been performed when driving on asphalt, gravel and snow. The results from the tests are discussed.
This paper describes a method to estimate tyre parameters for traction control applications based on control of individual wheel drives. The tyre parameters that are estimated are the rolling radius in driven mode (i.e. the rolling radius when the input torque to the wheel is zero) and the tyre longitudinal elasticity factor. The rolling radius in driven mode and the tyre longitudinal elasticity factor depend on several factors, among them the normal load. An important property of the method is that no transfer of load occurs during the estimation phase since the actual velocity of the vehicle is kept constant. Results from tests with ArtiTRAX, a 240 kg electric vehicle that carries 80 kg extra weight in three different front axle and rear axle distributions, are presented.
In this work, we propose using extremum seeking control (ESC) as a tool for maximum power point tracking in micro hydro power plants. The phasor ESC, which is based on estimating the phasor of the plant output at the perturbation frequency, was modified by stimating the phasors of multiple harmonics of this frequency. This modification will improve the performance of ESC by reducing the luctuations in control variables that may appear in noisy environments as a result of high-amplitude perturbation signals. A test rig was used to experimentally verify the proposed approach and to demonstrate the usability of ESC in hydro power plants.
The Combinator is an important part in Kaplan turbine control. It ensures that the turbine will operate in an optimum way, in terms of maximum efficiency of the plant. This work suggests a new sinusoidal perturbation based extremum seeking algorithm based on the phasor of the output. We propose to use this algorithm for generating the required data to build and correct the combinator. Simulations are presented showing the applicability of the proposed methods.
In this work, we present a modification for theclassic and phasor extremum seeking control algorithms in orderto improve the accuracy by removing or reducing the convergenceerror. The modulation signals were replaced by a sum of sinusoidsin order to remove the equilibrium shift in the controlled variableof the averaged system. The convergence error is calculated as afunction of the number of sinusoids used in the modulation signal.A simulation example is presented to illustrate the improvement.
This work presents a dynamic model for prediction of flow and output size distribution of cone crushers. The main purpose of the model is for simulation of closed-loop control using the Closed Side Setting (CSS) and the eccentric speed (ω) as manipulated variables. The idea of modeling crushers as cascaded zones is adopted throughout this work. The capacity, the length, the stroke, and the compression ratio of each zone are taken into consideration. Simulation results are presented in the form of the Crusher Performance Map (CPM) and the dynamic response for production of different size classes to steps input in ω and CSS. The simulations also include operation with recycling of oversize output, as well as the input of mixed materials. As an example, closed-loop control of the ratio of the large-size output to the total size output was simulated.
We present an extremum seeking control algorithm based on the estimation of the phasor of the perturbation frequency in the output of the plant. The phasor estimator is based on a continuous time Kalman filter, which is reduced into a variable gain observer by explicitly solving the special case of the Riccati equation. Local stability of the proposed al- gorithm for general non-linear dynamic systems using averaging and singular perturbations is presented for the single input case. The advantage of the presented algorithm is that it can be used on plants with large and even variable phase lag.
In this work we present a semi-global practical asymptotic stability analysis for phasor extremum seeking control with a general non-linear dynamic system. With the same technique applied to the classic band pass filter algorithm, we present a more relaxed (less constrained) semi-global practical asymptotic stability condition compared to earlier work. The results are based on a non approximated averaging for both control techniques.
This article demonstrates the ability of on-lineoptimization of cone crushers, specifically maximization of thetotal throughput of the crusher by adjusting the eccentric speed(ω). The on-line optimization was based on the Extremum-Seeking Control (ESC) approach, which is advantageous whenoptimizing systems with unknown time varying characteristics.Two types of gradient based approaches are tested in simulation,the traditional Band-pass filters method and a methodutilizing the Extended Kalman Filter (EKF). Both methodsperform satisfactory, demonstrating the good potential of ESCfor online-optimization of cone crushers. To deal with unwantedbehavior of the EKF based approach for situations when thegradient is not correctly estimated, a modification is suggestedbased on detecting this condition and accommodating for it.
Small scale model vehicles have been successfully used in multiple projects for research and for evaluation of models. ArtiTRAX, an experimental platform designed at Luleå University of Technology, is introduced to study the behaviour of articulated vehicles with individually driven wheels. Three case studies are presented: energy efficiency due to load transfer, online tyre parameter estimation and articulation angle control. The platform was shown to be a valuable asset for research in this area. It gives insight into the problem of controlling overactuated systems and the design hazard of using multiple integrators. ArtiTRAX is shown to be controllable through a kinematic model by only using actuation of the wheel drives. The difference of energy consumption as a function of longitudinal torque distribution indicates that the effectiveness of the motors should be considered when controlling the motor currents. The lateral distribution of motor current as a function of the articulation angle has to be considered in order to minimise the energy consumption of an articulated vehicle. Further research is necessary for understanding the mechanisms and relations between the energy consumption and the controlled motor currents
This paper presents a quarter vehicle model that is currently being used in optimization for energy-efficient control. The model uses the contact-point tyre model approach together with the mapped behaviour of the powertrain to generate an approximate behaviour of the target vehicle. The model is used to optimize two driving cases. The model shows promising results and is believed to enable future research in optimal control of articulated vehicles.
The modeling of a wood drying process using a state space realization is considered. Based on balance equations for energy and mass in the air gap between the timbers, on the wood surface and within the wood, a state space realization consisting of linear and nonlinear parts is proposed which can describe the moisture content and temperature behavior of timber inside a drying kiln. Finally, the proposed state space realization is illustrated with a simulation and achieved results are evaluated by real measurements.
We consider a novel method to design H-infinity observers for a class of uncertain nonlinear systems subject to unknown inputs. First, the main system dynamics are rewritten as an augmented system with state vector including both the state vector of the main system and the unknown inputs. Then, we design a H-infinity reduced-order observer to estimate both state variables and unknown inputs simultaneously. Based on a Lyapunov functional, we derive a sufficient condition for existence of the designed observer which requires solving a nonlinear matrix inequality. To facilitate the observer design, the achieved condition is formulated in terms of a set of linear matrix inequalities (LMI). By extending the proposed method to a multiobjective optimization problem, the maximum bound of the uncertainty and the minimum value of the disturbance attenuation level are found. Finally, the proposed observer is illustrated with an example.
In this paper, we consider a method to design a full-order nonlinear observer for a class of nonlinear time-delay systems with unknown inputs. Based on Lyapunov-Krasovskii functional, we derive a sufficient condition for existence of the designed observer which requires solving a set of nonlinear matrix inequalities. Then, the achieved condition is formulated in terms of two linear matrix inequalities (LMIs). Finally, the proposed observer is illustrated with an example.
The modeling of flotation is a difficult task due to the influences of mineralogical features of ores, floatability of various minerals governed by pulp environments, flotation kinetics and hydrodynamic variables. In the paper the dynamic modeling of flotation is studied. Based on the first order flotation rate equation, the dynamic description of a continuous flotation process is obtained both for a single continuous flotation cell and n cells arranged in series. A dynamic model for the mUlti-component case of continuous flotation is also discussed. The models obtained give the dynamic behaviors when the flotation parameters are changed due to the variation of the chemical and physical environment.
A general description of a flotation process is given. The dynamic model of a MIMO nonlinear subprocess in flotation, i.e. the pulp levels in five compartments in series is developed and the model is verified with real data from a production plant. In order to reject constant disturbances five extra states are introduced and the model is modified. An exact linearization has been made for the non-linear model and a linear quadratic Gaussian controller is proposed based on the linearized model. The simulation result shows an improved performance of the pulp level control when the set points are changed or a disturbance occur. In future the controller will be tested in production
A physical model of a continuous paper pulp digester is simplified and two subprocesses selected from the digester are modelled by coupled linear partial differential equations. This study focuses on the parameter identification of the simplified linear models. Finite-dimensional approximation of the model is made and a software package developed for identification of distributed parameter processes is applied. This identification system is developed for flexibility to allow identification for different choices of subprocesses and process variables. Unknown parameters of the subprocess models are estimated and the results are illustrated by process simulation and model validation.
A physical model of a nonlinear subprocess in a continuous paper pulp digester is discussed and simplified. Model approximation is carried out in order to produce a simple linear model to be used for unknown parameter estimation of the physical model. The Taylor series expansion and the orthogonal collocation method are applied for the model linearization and model lumping, respectively. The reduced model is expressed as a standard state space form. The model parameters are estimated in the least squares sense, and the parameters retain their own physical meanings. The results of the parameter estimation are discussed and the model is verified using validation data.
The application of fuzzy logic in the control of flotation is studied in this paper. A general description of apatite flotation process is given and a dynamic model is developed. A fuzzy logic controller is proposed for a nonlinear isolated continuous flotation process. The knowledge base of the controller is constructed on the basis of the semi-batch results from the apatite flotation experiments and available knowledge sources. The design of the controller does not need an exact process model. The simulation result shows that the fuzzy logic controller can reduce collector dosage consumption of the apatite flotation process while maintaining the phosphorous content in magnetite concentrate within an acceptable limits less than 0.025% P. Stability of the fuzzy control system is discussed
This paper studies the application of reduced models of a distributed parameter system for robust process control and state estimation. We take the approach of integrating model reduction, parameter identification, and model uncertainty analysis, in purpose to find an appropriate trade-off between complexity and robust performance. The application example is the temperature system in a continuous paper pulp digester. Physical modeling of this process results in coupled linearized partial differential equations which are then reduced into low-order nominal process models using an orthogonal collocation approximation method.Two different approaches to obtaining a model uncertainty description are adapted for use on a distributed parameter system with low-order nominal model and shown to produce similar results when tested with measurement data. It is also demonstrated how this uncertainty description, in combination with the reduced model, may be used for robust control design and verification of the control performance on the distributed parameter system.Finally, the possibility of estimating the distributed process state using a state observer for the reduced process is demonstrated. Measurements of the process state in a certain position is available and is shown to agree with the estimated state at the same position.
Rheology is an indispensable tool for formulation development, which when harnessed, can both predict a material’s performance and provide valuable insight regarding the material’s macrostructure. However, rheological characterizations are under-utilized in 3D printing of drug formulations. In this study, viscosity measurements were used to establish a mathematical model for predicting the printability of fused deposition modelling 3D printed tablets (Printlets). The formulations were composed of polycaprolactone (PCL) with different amounts of ciprofloxacin and polyethylene glycol (PEG), and different molecular weights of PEG. With all printing parameters kept constant, both binary and ternary blends were found to extrude at nozzle temperatures of 130, 150 and 170 C. In contrast PCL was unextrudable at 130 and 150 C. Three standard rheological models were applied to the experimental viscosity measurements, which revealed an operating viscosity window of between 100-1000 Pa.s at the apparent shear rate of the nozzle. The drug profile of the printlets were experimentally measured over seven days. As a proof-of-concept, machine learning models were developed to predict the dissolution behaviour from the viscosity measurements. The machine learning models were discovered to accurately predict the dissolution profile, with the highest f2 similarity score value of 90.9 recorded. Therefore, the study demonstrated that using only the viscosity measurements can be employed for the simultaneous high-throughput screening of formulations that are printable and with the desired release profile.
In the case of Wall Climbing Robot (WCR) design, nature has always been one of the biggest inspirations. While WCR designs have been incorporating adhesion techniques inspired by organisms, including reptiles, insects, amphibians and marine invertebrates, most efforts have been focusing mainly on adhesion for dry surfaces. For WCRs to become widely applicable under all environments, given the vast areas of this planet described by high precipitation, the ability to scale vertical surfaces in wet conditions should be considered a design necessity. To this goal, this article focuses on the most commonly adopted adhesion mechanisms, while providing an overview on recent WCR technological advances through the prism of wet adhesion. An extensive outlook is also detailed, including promising research directions yet to be trialed in bio-inspirations and recent material developments, which could further bridge the gap between WCR design and wet adhesion towards all-environment climbing robots.
Oral films (OFs) continue to attract attention as drug delivery systems, particularly for pedatric and geriatric needs. However, immiscibility between different polymers limits the full potential of OFs from being explored. One example is pullulan (PUL), a novel biopolymer which often has to be blended with other polymers to reduce cost and alter its mechanical properties. In this study, the state-of-the-art in fabrication techniques, three-dimensional (3D) printing was used to produce hybrid film structures of PUL and hydroxypropyl methylcellulose (HPMC), which were loaded with caffeine as a model drug. 3D printing was used to control the spatial deposition of films. HPMC was found to increase the mean mechanical properties of PUL films, where the tensile strength, elastic modulus and elongation break increased from 8.9 to 14.5 MPa, 1.17 to 1.56 GPa and from 1.48% to 1.77%, respectively. In addition, the spatial orientation of the hybrid films was also explored to determine which orientation could maximize the mechanical properties of the hybrid films. The results revealed that 3D printing could modify the mechanical properties of PUL whilst circumventing the issues associated with immiscibility.
In this paper an iterative LMI based approach for solving the H2 control problem using static state feedback for singular perturbation systems is proposed. The proposed controller is given in terms of the solution of a set of matrix inequalities independent of singular parameter . These inequalities are not in the form of linear matrix inequalities (LMI). By introducing a new iterative algorithm, these inequalities are solved through iterative LMI formulation. To show the effectiveness of the proposed algorithm comparing to other standard time-scale decomposition methods, an illustrative example is also provided
In this paper a new approach for solving the H2 control problem using static state-feedback for LTI singular perturbation systems is proposed which achieves a minimum bound on the H2 performance level of closed-loop system. The proposed controller is given in terms of the solution of a set of matrix inequalities independent of singular parameter . These inequalities are not in the form of linear matrix inequalities (LMI). By introducing a new algorithm, these inequalities are solved through LMI formulation. To show the effectiveness of the proposed algorithm comparing to other standard time-scale decomposition methods, an illustrative example is also provided.
In this paper a new approach for solving the H∞ control problem using static state-feedback for LTI singular perturbation systems is proposed. The proposed controller is given in terms of the solution of a set of matrix inequalities independent of singular parameter . These inequalities are not in the form of linear matrix inequalities (LMI). By introducing a new algorithm, these inequalities are solved through LMI formulation. To show the effectiveness of the proposed algorithm comparing to other methods, an illustrative example is also provided.
This paper investigates on the development and implementation of a high integrity navigation system based on the combined use of the Global Positioning System (GPS) and an inertial measurement unit (IMU) for land vehicle applications. The complementary properties of the GPS and the INS have motivated several works dealing with their fusion by using a Kalman Filter. The conventional kalman filter has a fix error covariance matrix in all times of processing. Multi-sensor based navigation system that is implemented in this paper is called data synchronization. Also, multi-rate operations that are compared with conventional Kalman filtering has fix error covariance matrix. Therefore, when GPS outage occurred we have improper treat by kalman filter. In this paper we present an Adaptive method instead of conventional methods. It is shown that proposed method has a better performance rather than conventional method. Experimental results show the effectiveness of the GPS/INS integrated system.
In this paper, we propose a novel approach to detect moving objects in a complex background. The Gaussian mixture model (GMM) is an effective way to extract moving objects from a video sequence. However, the conventional mixture Gaussian method suffers from false motion detection in complex backgrounds and slow convergence. This work, in order to achieve robust and accurate extraction of the shapes of moving objects, applies a hybrid method to remove noise from images. The proposed model consists of two stages. The first stage consists of a fourth order PDE and the second stage is a relaxed median Experimental results show that the proposed model performs well even in the presence of higher levels of noise.
This paper presents a novel and robust algorithm, for multiple motion detection and tracking in dynamic and complex scenes. The algorithm consists of two steps: at first, we use a robust algorithm for human detection. Then, Gaussian mixture model (GMM), Neighborhood-based difference and Overlapping-based classification are applied to improve human detection performance .The conventional mixture Gaussian method suffers from false motion detection in complex backgrounds and slow convergence. We combine three above mentioned methods to obtain detection. The second step of the proposed algorithm is object tracking framework based on Kalman filtering which works well in dynamic scenes. Experimental results show the high performance of the proposed method for multiple object tracking in complex and noisy backgrounds.
In this paper, a generic approach to attitude and position estimation, suited for any type of unmanned aerial vehicle, is presented. This will be achieved by establishing a generic framework, which can be extended using adaptive methods to determine the thrust properties of the engines and the mass of the aircraft, while keeping the overall computational complexity of the system low. Furthermore, the effect of magnetic disturbances will be reduced in a novel way by confining the magnetic errors to affect only heading, without compromising the pitch and roll estimation of the system with error-based estimation. The efficacy of the proposed framework will be evaluated through extended simulations and experimental validations on a multirotor. Finally, guidelines will be provided toward: 1) an implementation with a reduced computational complexity and 2) the utilization of the square-root formulations of the extended Kalman filter for extending the dynamic range of the filter.