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In this article, a microcontroller-based high-accuracy dew-point (DP) instrument using a quartz crystal resonance (QCR) sensor is developed. The sensor is a symmetrical double cooled QCR DP sensor, which has the characteristics of efficient cooling and stable oscillation. Based on the proportional—integral—differential control method, the logic level signal and the oscillation amplitude outputted by the drive circuit are used as control feedback to obtain an optimized expert strategy control system, and the automatic DP tracking measurement is realized by the microcontroller. It is proved by experiments that the measurement error of the instrument in the range of 0–15 °C dew point (DP) environment is less than 0.3 °C DP, and it has a good performance of tracking measurement in a dynamic DP environment. [ABSTRACT FROM AUTHOR]

A novel control scheme is proposed in this paper for the sensorless control of interior permanent magnet synchronous machines (IPMSMs) with the automatic tracking of the maximum-torque-per-ampere (MTPA) trajectory. Differing from the conventional method that the estimation of the rotor position and the tracking of MTPA trajectory are two separate processes, the proposed control scheme can achieve sensorless MTPA control of IPMSMs in one step by defining and constructing a proper virtual q-axis inductance to directly calculate the current space vector angle along the MTPA trajectory rather than the rotor position, which greatly simplifies the whole implementation process. Meanwhile, considering that the virtual q-axis inductance is changing with the machine parameters and the applied load, a simple signal processing technique based on the virtual high-frequency signal injection is developed for the real time and accurate acquisition of the virtual q-axis inductance. Furthermore, in order to ensure the reliability and stability of the proposed control scheme, the effective range of the virtual q-axis inductance is analyzed in detail. Finally, the experimental results on an IPMSM demonstrate the proposed control scheme can achieve the high-performance sensorless control with accurate MTPA self-tracking operation. [ABSTRACT FROM AUTHOR]

Even though many public parking lots are located underground and indoors, most existing automatic parking space detection and tracking methods have difficulty handling such scenarios due to severe illumination and complex obstacle conditions. To overcome this problem, this paper proposes a method that detects and tracks parking spaces in underground and indoor environments by fusing sensors already mounted on mass produced vehicles. The proposed detection method finds parking spaces based on a high-level fusion of two complementary approaches: parking slot marking-based and free space-based. Parking slots are detected by estimating parallel line pairs and free spaces are detected by recognizing the positions of parked vehicles as well as pillars. The proposed tracking method enhances the previous method by considering pillar information. Since pillars degrade parking slot tracking performance, this method estimates a pillar region and utilizes it to remove false edges and to estimate the amount of occlusion. [ABSTRACT FROM PUBLISHER]

This paper presents a tracking control method for the lateral motion of an autonomous land vehicle (ALV). This method is based on active disturbance rejection control (ADRC) scheme and differential flatness theory. The lateral motion is hard to control since it is underactuated, nonlinear, and with large uncertainties. By making a small-angle approximation, the dynamic model is linearized. The flatness of the linear model is proved and a flat output is found. An equivalent form of the model is obtained based on the flat output and its derivatives only. Moreover, an ADRC is adopted to guarantee both control accuracy and strong robustness. Simulation results are presented and the results show the effectiveness of the control strategies. [ABSTRACT FROM PUBLISHER]

This paper presents a constructive method to design distributed controllers that force a group of $N$ underactuated ships under sea loads to perform a desired synchronization motion tracking task and guarantee no collisions between the ships. First, an observer is designed to globally exponentially estimate the sea loads. Then, the ship dynamics is transformed to an almost spherical form. Smooth or $p$-times differentiable step functions are next introduced to ensure that the collision avoidance is only active when the ships are in their collision detection zones. For each ship, a dynamical variable is introduced to deal with relaxation of the reference path generation and difficulties caused by underactuation. The control design is based on the above developments, and Lyapunov and backstepping methods. The effectiveness of the proposed results is illustrated through simulations. [ABSTRACT FROM PUBLISHER]

Sparse representation (SR)-based tracking systems have become popular in the past recent years for its effectiveness. However, the underlying assumption of these tracking systems is that the target appearance can be linearly represented by a sparse approximation over a set of templates and a residual term, which usually needs to solve $\ell_1$ norm minimization for many times and brings a heavy computational cost. This paper introduces an efficient tracking system by discovering orthogonalized templates. By orthogonalizing templates from previous frames and removing their correlation, we show that the sparsity of template weights is not necessary in target appearance modeling and thus a least squares regularization can be employed. We also decompose the residual term into two components in observation model to take occlusion cases into consideration. We demonstrate that, in comparison with the SR-based tracking systems that use $\ell_1$ learning, our tracking system is much more computationally efficient while getting an even better performance. Experiments on a variety of challenging video sequences demonstrate both the effectiveness and efficiency of the system. [ABSTRACT FROM PUBLISHER]

Permanent-magnet step motors offer several advantages such as high efficiency, high power density, high torque-to-inertia ratio, and excellent durability and serviceability, as well as the absence of external rotor excitation and windings. The nonuniformity in the developed torque due to the nonsinusoidal flux distribution in the airgap is, however, the major obstacle in achieving global high-precision position tracking. When the position reference profile is a periodic signal of known period, such an obstacle may be however overcome by using recent learning control techniques, which require neither high gains in the inner speed/position control loops nor resetting procedures. An experimental comparison of two different recently designed learning position controls (“adaptive” and “iterative”) is, for the first time, carried out with reference to the same low-speed robotic application. Benefits and drawbacks of the two learning approaches are analyzed in detail. [ABSTRACT FROM AUTHOR]

Cogging force is an important source of disturbances for linear-motor-driven systems. To obtain a higher level of contouring motion control performance for multiaxis mechanical systems subject to significant nonlinear cogging forces, both the coordinated control of multiaxis motions and the effective compensation of cogging forces are necessary. In addition, the effect of unavoidable velocity measurement noises needs to be sufficiently attenuated. This paper presents a discontinuous-projection-based desired compensation adaptive robust contouring controller to address these control issues all at once. Specifically, the presented approach explicitly takes into account the specific characteristics of cogging forces in the controller designs and employs the task coordinate formulation for coordinated motion controls. Theoretically, the resulting controller achieves a guaranteed transient performance and a steady-state contouring accuracy even in the presence of both parametric uncertainties and uncertain nonlinearities. In addition, the controller also achieves asymptotic output tracking when there are parametric uncertainties only. Comparative experimental results obtained on a high-speed Anorad industrial biaxial precision gantry are presented to verify the excellent contouring performance of the proposed control scheme and the effectiveness of the cogging force compensations. [ABSTRACT FROM AUTHOR]

In this paper, a discrete-time output integral sliding-mode control (DOISMC) is developed for the precision control of a piezomotor-driven linear motion stage. A new integral-type sliding surface is first designed for arbitrary output reference tracking control. To estimate the unknown state and disturbance, two observers based on the integral-type sliding surface are designed. It is shown in this paper, through both theoretical analysis and experiments, that the discrete-time output integral sliding mode controller, together with the state and disturbance estimation, achieves an O(T²) tracking precision w.r.t. the sampling period T. The superior performance of DOISMC achieved in the control of piezomotor-driven linear stage indicates that it is a suitable control method for precision control or servo applications. [ABSTRACT FROM AUTHOR]

The importance of laser welding in industry increases. Many welds have high-quality demands, and one possibility to satisfy the quality requirements is to monitor the welding process with high-speed cameras. Laser welding is a highly dynamic process; it is therefore challenging to distinguish between normal process fluctuations and abnormal error events in the recorded sequences. This paper investigates a novel classification method to automatically analyze the recorded welding sequences and robustly find the abnormal error events. To our knowledge, it is the first time that a framework to detect and track sputters in welding sequences is proposed and evaluated. To achieve a high usability of the classification algorithm, in the training phase, the user only needs to mark suspicious sequences but does not need to label individual frames within the sequences. The framework is tested on two challenging data sets from real welding processes. The results show that the material particles can be tracked accurately. On a sample data set, the new approach finds all erroneous welds with a small false-positive rate and outperforms previously developed methods. [ABSTRACT FROM AUTHOR]

Camera motion estimation in image sequences generally focuses on the recovery of the frames when the camera is mounted on a moving platform. Global motion in video sequences is more complex and involves camera operation, camera motion, and other nontarget motions. Global motion compensation is usually handled by compensating the dominant motion using estimation and segmentation techniques. To enhance tracker performance and accuracy, frame recovery operation plays a crucial role by estimating undesired motion. In this paper, a normalized correlation-based regional template-matching (TM) algorithm has been developed to accurately recover frames before the application of the tracking algorithm. Then, a robust multiple-target-tracking system has been developed using a combination of fringe-adjusted joint transform correlator and TM techniques. Joint transform correlation detects a target optoelectronically, while TM technique is performed digitally. To increase the tracking system speed and decrease the effects of clutter, a subframe segmentation and local deviation-based image-preprocessing algorithm has been proposed. The improved performance of multiple-target-tracking system is tested using real-life forward-looking infrared (IR) imagery video sequences obtained from IR sensors mounted on an airborne platform. [ABSTRACT FROM AUTHOR]

Proposes a design and realization of a vision-based target tracking system. Derivation of pan-tilt camera fitting; Need for low-cost hardware for surveillance; Implementation of multi-block search method.

Presents a biologically inspired system for tracking objects in a visual scene. Uniqueness of the system; Mobile vehicle outfitted with a custom analog vehicle locator systems (VLSI) for encoding the position of an object of interest; Function of neural oscillators; Nature of the system.

Presents a position-based tracking system for autonomous mobile target tracking system based on grey-fuzzy control algorithm. Target detection based on adaptive visual detector; Advantages of the integrated system; Mobile robots used to demonstrate integrated system; Target tracking experiments.

Shows the design and prototypal hardware realization of a multiprocessor-oriented visual tracking system. Reliance of target identification on neural network's architecture; Industrial applications of the visual tracking of moving objects; Comparison with other approaches; Composition of the tracking mechanism; Techniques to approach tracking problems.

Proposes a tracking algorithm for the vehicle collision warning and collision avoidance system using a car radar. Use of decision logic algorithm to accomplish target-to-measurement data association; Simulation and experimental results.