In this paper, we utilize the observer/Kalman filter identification (OKID) and the eigensystem realization algorithm (ERA) techniques to identify the modal parameters of a centrifugal machine. To this end, we use an experimental setup to generate a pseudo-impulse input and collect output measurements which are corrupted by noise. We use the pseudo-impulse input and the OKID to find the Markov parameters of the system. Then we form the Hankel matrix of the system and determine the singular values of the system. A minimum-order, state-space model of the system is realized through the Markov parameters and then the natural frequency, damping ratio, mode shapes, and modal amplitudes at the sensor location are estimated by the ERA. We find three models for three separate cases and validate all the three identified models with the measured data and the Waterfall plot. The identified models are useful for designing passive or active vibration suppression control and fault detection systems. The results confirm that OKID/ERA is a reliable time-domain method for identifying the modal parameters of vertical centrifuge machines.

Site assessments for bifacial Photovoltaic (PV) installation are quite challenging to conduct manually due to the area size and the extreme temperature conditions at desert sites. We designed and built an autonomous Unmanned Ground Vehicle (UGV) fitted with a Global Navigation Satellite Network-System Real-Time Kinematic (GNSS-RTK) positioning device, an Inertial Measurement Unit (IMU), encoder to improve and aid site assessments in desert condition. Sandy terrains deserts are challenging for UGV’s because they increase the likelihood of wheel slippage due to reduced traction. Sensor details such as IMU, GNSS-RTK, and encoder should be taken into consideration to account for the errors that the desert terrains pose. This study compared the Extended Kalman Filter (EKF) for standard GPS & GNSS-RTK to verify which performs better for the UGV’s position estimation. The estimated UGV’s position from the kinematics model and EKF are validated using a drone camera system that uses an image processing technique to verify the UGV’s position with the help of the visible reference cones. Throughout the experiments, the GNSS-RTK performed better than GPS. Also, the EKF performed as well as the GNSS-RTK by trusting it more than the encoder/gyroscope reading.

Deep drilling operations are primarily used to produce oil, gas, and geothermal heat from reservoirs in the earth’s crust. A drill string built of thread-connected components is used to transfer mechanical energy from a drill rig on the surface to a drill bit at the bottom end. The lowest part of a drill string, which is called bottom-hole assembly (BHA), contains sophisticated sub-assemblies for process and trajectory control, formation evaluation, surface communication, power generation, and system diagnostics. The BHA can experience critical vibrations without indication further up to the string. These vibrations need to be closely monitored for process control, fatigue management, and design feedback. However, the number of sensors is too small to provide reliable indication of loads on all critical components of the drill string. Adding sensors to each component is currently neither economically nor technically viable. This paper presents an application of existing Kalman Filters, merging information from available sensors and dynamic models to obtain state estimates for all components of the BHA. The expected accuracy and limitations are discussed. The results of load extrapolation are confirmed by comparison with measurements proving the concept under inaccurately defined interaction with a downhole environment.

This paper describes the implementation of various algorithms to control the distance between a lead vehicle and a following (ego) vehicle. The ego robot equipped with a monocular camera and a rotating laser sensor (LDS). The monocular camera used for object detection using the Aggregate Channel Features (ACF) detection algorithm. The width of the bounding box generated by the detection algorithm had used to determine the distance between the lead and the following vehicles. Since this research focused on longitudinal autonomy, the data from the rotating laser sensor downsampled from 360 points to 30 points. These sampled points covered the front view of the vehicle. All data points transformed into a planar world coordinate (two-dimensional plane). The outputs of the camera and laser sensor (LDS) were fused to obtain accurate distance measurements for the lead vehicle. Sensor calibration had achieved by comparing sensor data with the ground truth values. Kalman Filter was used to implementing sensor fusion by combining perception data from the monocular camera and LDS for accurate position and velocity estimation. This calibration provided information about the sensor noise and deviation of sensor data from its ground truth values. These values helped to determine the error covariance matrixes of the Kalman filter. For implementation, the Robot Operating System (ROS)-MATLAB platform used to communicate between robot and host Personal Computer (PC). The experiments evaluated the performance of Proportional Control (P), Proportional-Integral Control (PI), and Model Predictive Control (MPC) in maintaining a minimum distance between the vehicles. For the MPC implementation in MATLAB, Model Predictive Control Quadratic Programming (MPCQP) solver was used to get the optimal solution for control output. The results show that the MPC yields faster response times when compared to P control and PI control. These algorithms evaluated during constant velocity and constant acceleration of the lead vehicle. The steady-state errors of P and PI controllers were around 0.1 meters (m) in both scenarios and 0 to 0.2m for constant velocity and 0 to 0.15m for ramp velocity, respectively. And for MPC, steady-state error varied from −0.05m to 0.05m in both the scenarios. This range in steady-state error was due to varying speed of the ego vehicle with time to maintain the minimum relative distance between the robots, and there was a communication delay in the system that also affected the behavior of the controllers. The MPC was more sensitive to communication delays. However, the effect of this communication delay was negligible to P and PI controllers. This sensitivity resulted in different velocity profiles for the ego vehicle in MPC and P or PI controllers.

With advancements in vehicle electronics and growing focus on vehicle safety systems, state and parameter estimation has become a remarkable sphere of research. In this study, the vehicle vertical dynamics states and parameters were estimated simultaneously and iteratively with relevant vehicle responses. This was achieved through vehicle tests, designed to excite the corresponding vehicle states. A linear Kalman filter was used for state estimation. Vehicle parameters were obtained as a optimal solution using an optimization algorithm. With the help of multi-DOF vehicle ride model along with real vehicle measurements, the state and parameter estimators work concurrently to obtain the results. A cleat test was performed for a Sports Utility Vehicle (SUV) in IPG CarMaker ® and in reality, for evaluation of the proposed framework. The state estimation results showed upto 93% correlation from simulated data and upto 81% correlation from real time measurements. Parameter estimation produced an average error of only 9.1%. This demonstrated the efficacy of the algorithm for use in vehicle systems.

Many applications requiring dynamic tracking have been needed in large-scale. As a novel distributed measurement system, RLATs is presented and the key techniques are shown in detail. Because of the intrinsical drawback of distributed measurement systems, the Extend Kalman Filter approach is introduced to eliminate the tracking error and improve the tracking accuracy. State space model of RLATs are formulated, and an analytical expression for the linearized measurement function is derived. Comparison with the method of LS simulated data which presented a considerable improvement and stability in accuracy and the proposed EKF method while target’s moving speed is less than 100 mm/s.

Wheel Slip Regulation (WSR) is one of the Active Vehicle Safety Systems (AVSSs) for maintaining vehicle stability and maneuverability during emergency braking An approach for wheel slip prediction is proposed in this paper, which involves Auto-Regressive (AR) Time-Series modelling of longitudinal vehicle acceleration. This technique allows the usage of linear longitudinal vehicle dynamics for wheel slip estimation. A wheel slip prediction model was developed considering measurements from accelerometer and wheel speed sensor. This modified the Model Predictive Control (MPC) formulation to a univariate control input problem, involving braking torque. The objective function was devised for solving a least-squares reference tracking problem. An analytical solution for the MPC optimization problem was derived and implemented towards WSR. The proposed framework was programmed in MATLAB Simulink ® and co-simulated with IPG TruckMaker ® (a vehicle dynamic simulation software). The algorithm was tested in a Hardware-in-Loop (HiL) setup consisting of a pneumatic air brake system interfaced with IPG TruckMaker ® . Open loop studies from HiL led to the inclusion of Kalman filter for estimate tuning and PID inner loop control for brake pressure transients, which improved wheel slip regulation.

Free-fall absolute gravimeters are important classical high precision absolute gravimeters in many branches of scientific research. But its performance is always troubled by the ground vibration. Vibration correction method is used to correct the result by detecting the ground vibration with sensors. A Kalman filter based fusion method is proposed to obtain more accurate ground vibration signal by fusing the outputs of the seismometer and the accelerometer. Experiment is conducted with the homemade T-1 absolute gravimeter, the standard deviation of the corrected results using seismometer data and fused data are 586.32 μGal (1 μGal = 10 −8 m/s 2 ) and 508.59 μGal respectively, much better than the uncorrected result’s 6548.96 μGal. The results prove the superiority of fused data over data measured from single sensor. It is believed that the application scene of the vibration correction will be broadened and the performance of the vibration correction will also be improved by using the proposed fusion method in the future.

This paper presents a novel sensorless direct torque control of interior permanent magnet synchronous motor with resilient extended Kalman filter. In comparison to the field-oriented control scheme, direct torque control has advantages due to its simplicity (without Park’s transform or space-vector pulse-width-modulation), excellent transient response, high reliability and robustness. The interior permanent magnet synchronous motors have been extensively used in industrial applications due to their high efficiency, large torque to weight ratio, high performance and high reliability. Traditionally, the rotor position and speed of rotation are directly fed back through encoders or resolvers. In order to provide a more reliable and cost saving approach, the sensorless direct torque control of permanent magnet synchronous motor technique is proposed to eliminate the need of using encoders or resolvers. A novel nonlinear estimator, the resilient extended Kalman filter is proposed, which shows superior estimation accuracy compared with other estimation approaches such as extended Kalman filter. Computer simulation and DSP hardware implementation have been conducted to show the superior performance of the proposed approach.

Scanning beam interference lithography (SBIL) technology is applied to produce large-area grating with nanoscale phase accuracy. One of the greatest challenges of SBIL is locking interference fringe to a moving substrate with nanometer spatial phase error, which requires measuring the fringe phase with subnanometer precision. We are developing a novel homodyne phase-locking interferometer (HPLI) to meet the harsh measurement requirements. The HPLI offers high precision phase measurements as well as the direction recognition of the interference fringe drift with a four-orthogonal detection system. However, nonlinearity error impacts the phase measurement accuracy of HPLI with nanometer scale, which is mainly due to polarization mixing. In this paper, we present a method to estimate and compensate nonlinearity error in real time by applying an extended Kalman filter algorithm. The simulation results show that the method can effectively eliminate the nonlinearity error.

Grid synchronization is a critical concern for proper control of the energy transferred between the Distributed Power Generation System (DPGS) and the utility grid. Nonlinear estimation techniques are proposed to track the voltage magnitude, phase angle, and frequency of the utility grid. Instead of directly analyzing in abc coordinate frame, the symmetrical component is employed to separate the positive, negative, and zero sequences in the transformed αβ stationary coordinate frame. By using αβ stationary coordinate frame, the number of system state variables is reduced to five. The results show that our proposed nonlinear estimation technique is efficient in smart power system synchronization. The MATLAB simulation studies have been conducted to compare the performance of Extended Kalman Filter (EKF), Particle Filter (PF), and Unscented Kalman Filter (UKF). Computer simulations have shown that the efficacy of our proposed nonlinear estimation methods including the Extended Kalman Filter, Particle Filter, and Unscented Kalman Filter. It also shows that the Unscented Kalman Filter, and the Particle Filters are better estimators, because voltage synchronization problem is nonlinear system, and linearization process which the Extended Kalman Filter is based on is not very accurate. The number of particles in Particle Filter can be increased to improve the accuracy, but there exists a trade off between computational effort and estimation accuracy. In our research, with consideration the same computational complexity, we calculate the Mean Square Error (MSE) to examine the performances of different nonlinear estimation approaches. By comparing the MSE of different estimators, we prove that the Unscented Kalman Filter shows the most accurate performance in voltage synchronization for three phase unbalanced voltage. Our results have shown the potential applications in the future smart power grid synchronization.

Inertial and magnetic sensors are commonly used to determine orientation as they do not rely on a line of sight [1, 2]. There are many different techniques to fuse inertial measurement unit (IMU) data and obtain useful rotational data [1–3]. This study uses two separate data fusion techniques; a direction cosine matrix-based (DCM) technique and a quaternion-based Extended Kalman Filter (EKF) technique [1–3]. These techniques were altered based on performance metrics to weight sensor data when certain sensors proved not as reliable as others [2]. IMU sensors were tested on a hand mannequin and filters were developed using MATLAB software. Simulation results displayed a root-mean-squared error of less than .06° for each rotation angle. Experimental results maintained errors of less than 8° in each rotation angle.

The Extended Kalman Filter is commonly used as a non-linear estimator for engineering practices. However, the Extended Kalman Filter’s performance degrades or may even become unstable when the measurement data becomes inaccurate through random measurement failures, which is a widespread problem in many data-intensive control applications. To address this issue, the paper proposes an improved Extended Kalman Filter which is resilient against sensor failures. This work focuses on the Resilient Extended Kalman Filter’s derivation with its application to the single generator infinite bus power system. The sensor failure rate is modeled as a Binomial distribution with a known mean value. The performance of the proposed Resilient Extended Kalman Filter is compared with the traditional Extended Kalman Filter under various chances of sensor failures.

A stamp forming die, whose flexible blank holder was designed using FEA, was built. A closed-loop control system was used to control local punch forces and wrinkling by controlling both blank holder forces and draw bead penetration. The controllers for the draw beads featured an advanced PID controller with a Smith Predictor and Kalman Filter. A Bang-bang controller was also incorporated into the control system in order prevent control saturation. Fuzzy logic was used to transition from once controller to the other. Once closed-loop was implemented, tests were performed to evaluate the strains in the pans for various forming conditions. These results were compared to open-loop tests and it was found that the strains measured from closed-loop control tests resulted in more uniform strains and that the strains were further from the forming limit curves than strains from tests that were performed under open-loop conditions. Furthermore, it was seen that the strains in the regions were local force were controlled resulted in more uniform strain fields. Hence it was concluded that controlling local punch forces resulted in the strain control of critical regions.

Oil and gas refineries present challenging environments in which to work and operate, especially in places like the Middle East where temperatures can reach 50 °C and sand storms which can reduce visibility to a few meters. In addition, there can be gas or steam leaks which present health and safety hazards to the workers. At present, continuous operation of these plants requires that human workers venture out into these conditions in order to observe and report on the conditions within the plant. The goal of this work is to design, fabricate, assemble, and test an inspection robot in an effort to reduce the exposure and risks to human operators while increasing the flexibility and range of remote observations provided by a mobile robot. In this paper, we will report on the design approach taken, the subsystems identified and developed, the software environment chosen, and the application tasks envisioned. We will also report on the challenges of developing a robust localization algorithm for use in the challenging environment of a refinery as well as the needs for robust wireless communications in order to maintain command and control from the operators control room. An overview of the five-degree-of-freedom arm designed and fabricated, and its real-time control will also be presented. Results from GPS navigation and localization experiments will be presented. While average errors during parked operations were often less than one meter for the WAAS enabled GPS system, locational errors during dynamic operations were often more than three meters. This is due to multi-path signals near building structures and piping infrastructure. Real-time arm control has been implemented using FPGAs and while tuning presented some challenge, the FPGA has provided smooth and repeatable operation. Sensors include gas detectors, acoustic sensors, thermal imaging, and video camera streaming. In addition, we will report on a multi-faceted approach to localization using three different sensor technologies and integrated using a Kalman filter.

The goal of this paper is to estimate the mass and auxiliary power of a vehicle simultaneously. Auxiliary power is the portion of the load power that is consumed by any auxiliary devices such as A/C compressor which is connected to the engine directly. This estimation has many potential applications especially in power management control systems of hybrid and plug-in-hybrid vehicles to improve their efficiency. The parameter estimation algorithm is based on power balance of the vehicle. That is, total generated power by the engine should be equal to the power required for moving the vehicle plus the power consumed by the auxiliary devices. After developing the system model, Kalman filter is applied for the estimation of the auxiliary power and vehicle mass. The proposed estimation algorithm uses the signals available through the vehicle control area network (CAN), and no extra sensor is required. It is assumed that the road grade is provided by a Global Positioning System (GPS) installed in the car. Simulations are presented to show the performance of the estimation algorithm in both city and highway driving cycles. The estimated and actual results are in very good agreement.

Moving Horizon Estimation (MHE) has emerged as a powerful technique for tackling the estimation problems of the state of dynamic systems in the presence of constraints, nonlinearities and disturbances. In this paper, the Moving Horizon Estimation approach is applied in estimating the State of Charge (SoC) and State of Health (SoH) of a battery and the results are compared against those for the traditional estimation method of Extended Kalman Filter (EKF). The comparison of the results show that MHE provides improvement in performance over EKF in terms of different state initial conditions, convergence time, and process and sensor noise variations.

Car tracking algorithms are important for a number of applications, including self-driving cars and vehicle safety systems. The probabilistic data association (PDA) algorithm, in conjunction with Kalman Filter (KF), and interacting multiple model (IMM) are well studied, specifically in the aero-tracking applications. This paper studies single targets while performing maneuvers in the presence of clutter, which is a common scenario for road vehicle tracking applications. The relatively new smooth variable structure filter (SVSF) is demonstrated to be robust and stable filtering strategy under the presence of modeling uncertainties. In this paper, SVSF based PDA technique is combined with IMM method. The new method, referred to as IMM-PDA-SVSF is simulated under several possible car motion scenarios. Also, the algorithm is tested on a real experimental data acquired by GPS device.

A longitudinal force estimation based on wheel dynamics and unscented Kalman filter is proposed in this report to address the difficulties in the conventional tire-based approaches. Although it seems that implementation of a tire model in the estimation procedure should result in more accurate results, especially for non-linear regions, complexities in identifying the tire parameters due to the variation of the road and tire conditions leads to inaccurate results for harsh maneuvers on slippery roads. Moreover, the estimation process requires reliable measurements and this necessitates utilizing dynamic models with feasible measurements. Consequently, wheel dynamics is employed to extend the fidelity of the algorithm. For such a model, wheel speeds as reliable and feasible measurements are available. In this strategy, the complex tire-road interaction can be discarded since the wheel speeds are being observed by wheel sensors and the values of the effective torques are provided by motor drives then the longitudinal forces at each individual corner of the vehicle can be estimated independently. Experimental and simulation results confirm the validity of the algorithm in slippery road conditions as well as normal conditions. The newly developed structure has a strong potential to be integrated with other state estimation, such as longitudinal/lateral velocities and lateral forces.

This paper presents a methodology of vision-based pose and motion estimation of non-cooperative targets as well as a control scheme for robotic manipulators to perform autonomous capture of non-cooperative targets. A combination of photogrammetry and extended Kalman filter is proposed for real time state estimation of the non-cooperative target. Once the vision-based estimation is obtained, a real state of the target regarding to the global frame is calculated based on the transformation matrices of coordinate frames. So as to make a capture, a desired state of the end effector is defined in accordance with the real state of the target aforementioned, and further a corresponding desired state of the robotic manipulator is derived by inverse kinematics. Then a close-loop control scheme is adopted to drive the robot to the desired state previously obtained. Experiments have been designed and implemented on a custom built six degrees of freedom robotic manipulator with an eye-in-hand configuration. The experimental results demonstrated the feasibility and effectiveness of the proposed methodology and control scheme.