Gaussian Mixture Measurements for Very Long Range Tracking

Lateral Control for UAVs using Sliding Mode Technique

This paper presents sliding mode based lateral control for UAVs using piecewise linear sliding surface. A single linear surface is replaced by piecewise linear sliding surface to ensure the performance during small track errors or divergence and a bounded heading error in the case of large track error, thus avoiding the actuator saturations. Stability of the proposed sliding surface is analyzed with piecewise analytical solution of the proposed sliding surface. The proposed scheme uses only the readily available sensor information that is lateral track error and heading error angle and no succeeding mission information are required here. The proposed scheme is implemented on the 6-degrees-of-freedom (6-dof) simulation and different cases of large and small track errors are simulated in the presence/absence of wind to successfully validate the proposed control scheme.

This work investigates the attitude control of reentry vehicle under modeling inaccuracies and external disturbances. A robust adaptive fuzzy PID-type sliding mode control (AFPID-SMC) is designed with the utilization of radial basis function (RBF) neural network. In order to improve the transient performance and ensure small steady state tracking error, the gain parameters of PID-type sliding mode manifold are adjusted online by using adaptive fuzzy logic system (FLS). Additionally, the designed new adaptive law can ensure that the closed-loop system is asymptotically stable. Meanwhile, the problem of the actuator saturation, caused by integral term of sliding mode manifold, is avoided even under large initial tracking error. Furthermore, to eliminate the need of a priori knowledge of the disturbance upper bound, RBF neural network observer is used to estimate the disturbance information. The stability of the closed-loop system is proved via Lyapunov direct approach. Finally, the numerical simulations verify that the proposed controller is better than conventional PID-type SMC in terms of improving the transient performance and robustness.

Aiming at the problems of large tracking error and long tracking time in traditional multiperson target dynamic tracking methods, a new method based on wireless body area network for athlete training multiperson target dynamic tracking is proposed. First, the microinertial sensor in the wireless body area network is used to collect the multiperson image data of the athlete training, and the sparse representation is performed after processing, which improves the reliability of the data and reduces the tracking error. Secondly, the multiperson target dynamic tracking method based on the adaptive search box is used, combined with target isolation and occlusion detection, to judge the athlete’s training target. Finally, the nearest neighbor algorithm is used to construct an adaptive search box to achieve dynamic tracking of multiple targets. Experimental results show that this method can accurately measure the similarity of target features, with small tracking error and short tracking time. The minimum tracking error is only 0.11 frame.

This study examines the dependence of the tropical cyclone (TC) intensity errors on the track errors in the Weather Research and Forecasting (WRF-ARW) model. By using the National Centers for Environmental Prediction global final analysis as the initial and boundary conditions for cloud-resolving simulations of TC cases that have small track errors, it is found that the 2- and 3-day intensity errors in the North Atlantic basin can be reduced to 15 and 19 % when the track errors decrease to 55 and 76 %, respectively, whereas the 1-day intensity error shows no significant reduction despite more than 30 % decrease of the 1-day track error. For the North-Western Pacific basin, the percentage of intensity reduction is somewhat similar with the 2- and 3-day intensity errors improved by about 15 and 19 %, respectively. This suggests that future improvement of the TC track forecast skill in the WRF-ARW model will be beneficial to the intensity forecast. However, the substantially smaller percentages of intensity improvement than those of the track error improvement indicate that ambient environment tends to play a less important role in determining the TC intensity as compared to other factors related to the vortex initialization or physics representations in the WRF-ARW model.

The SynchronyTM Respiratory Tracking System (SRTS) component of the CyberKnife® Robotic Radiosurgery System (Accuray, Inc., Sunnyvale CA) enables real-time tracking of moving targets by modeling the correlation between the targets and external surrogate light-emitting diode (LED) markers placed on the patient's chest. Previous studies reported some cases with respiratory phase shifts between lung tumor and chest wall motions. In this study, the impacts of respiratory phase shifts on the motion-tracking accuracy of the SRTS were investigated. A plastic scintillator was used to detect the position of the x-ray beams. The scintillation light was recorded using a camera in a dark room. A moving phantom moved a U-shaped frame on the scintillator with a 4th power of sinusoidal functions. Three metallic markers for motion tracking and four fluorescent tapes were attached to the frame. The fluorescent tapes were used to identify phantom position and respiratory phase for each video frame. The beam positions collected, when considered relative to the phantom motion, represent the degree of tracking error. Beam position was calculated by adding error value to phantom position. Motions with respiratory phase shifts between the target and an extra stage mimicking chest wall motion were also tested for LED markers. Log files of the SRTS were analyzed to evaluate correlation errors. When target and LED marker motions were synchronized with a respiratory cycle of 4s, the maximum tracking errors for 90% and 95% of beam-on time were 1.0mm and 1.2mm, respectively. The frequency of tracking errors increased when LED marker motion phase preceded target motion. Tracking errors that corresponded to 90% beam-on time were within 2.4mm for 5-15% of phase shifts. In contrast, the tracking errors were very large when the LED marker delayed to the target motions; the maximum errors of 90% beam-on time were 3.0, 3.8, and 7.5mm for 5%, 10%, and 15% of phase shifts, respectively. The patterns of the tracking errors derived from the scintillation light were very similar to those of the correlation data of the SRTS derived from the log files, indicating that the tracking errors caused mainly due to the errors in modeling the correlation data. With long respiratory cycle of 6s, the tracking errors were significantly decreased; the maximum tracking errors for 95% beam-on time were 1.6mm and 2.2mm for early and delayed LED motion. We have investigated the motion-tracking accuracy of the CyberKnife SRTS for cases with the respiratory phase shift between the target and the LED marker. The maximum tracking errors for 90% probability were within 2.4mm when the target delays to the LED markers. When LED marker delays, however, very large tracking errors were observed. With a long respiratory cycle, the tracking errors were greatly improved to less than 2.2mm. Coaching slow breathing will be useful for accurate motion tracking radiotherapy.

Purpose: To assess the geometrical accuracy and estimate adequate PTV margins for liver treatments using the Synchrony respiratory tracking system.Material and methods: Treatment log files are analyzed for 72 liver patients to assess tracking accuracy. The tracking error is calculated as the quadratic sum of the correlation, the predictor and the beam positioning errors. Treatment target rotations and rigid body errors reported by the system are also evaluated. The impact of uncorrected rotations is assessed by rotating the planned dose distribution and reassessing target coverage. Total PTV margins are estimated by summing in quadrature tracking errors and rigid body errors. Relationships are explored between tracking errors, model linearity and motion amplitudes of internal and external markers.Results: Margins of 3, 2, 2 mm in SUP-INF, LT-RT and ANT-POST directions, respectively, are sufficient to account for tracking and beam positioning errors for 95% of patients. If rigid body error is also considered, margins increase to 4 mm isotropic. Rotations could not be corrected for 92% of patients due to imperfect fiducial implantation and limitations in the magnitude of corrections that the system can apply. Uncorrected rotations would lead to average estimated dose reductions of 2.7% ± 5.8% of the prescribed dose for D99 of GTVs (5 mm PTV expansion) in which the target was well covered in the original plan (28 of 31 GTVs). 80% of tracking models exhibit near linear correlation between internal and external marker motions with small tracking errors (<2.2 mm).Conclusions: Isotropic PTV margins considering tracking errors and target rigid body errors could be used for liver SBRT treatments if rotational corrections can be calculated accurately so that systematic rotational offsets can be avoided. The linearity of the internal and external breathing motions might be useful for other types of treatment modalities for liver cancer.

We present a successive interference cancellation (SIC) scheme for a multicarrier (MC) DS CDMA system. In every subband, the SIC receiver successively detects the interferers' signals and substracts them from the user-of-interest. A comparison is made among SIC, matched filter detection with an equal gain combiner (MF-EGC), matched filter followed by maximal-ratio combining (MF-MRC) and minimum mean-squared error (MMSE) receivers with optimal tap coefficients. For this latter receiver, we determine the performance both when the number of taps equals the processing gain, denoted by N, and when the number of taps is roughly one-half of the processing gain. By analysis and simulation, it is shown that the SIC has better ability to suppress multiple-access interference (MAI) than do MF and [N/2]-tap MMSE in single carrier (SC) CDMA, and MF-EGC, MF-MRC and [N/2]-tap MMSE in MC CDMA, and this translates to increased capacity and better fading resistance. Further, SIC and N-tap MMSE yield approximately the same performance in SC CDMA, but SIC has a poorer performance than does an N-tap MMSE in MC CDMA. In addition, we derive results for the performance degradation that a SIC scheme experiences in MC CDMA due to both phase and timing tracking errors. It is shown that with small tracking errors, SIC can still retain a performance advantage over MF-EGC and MF-MRC, although the advantage of SIC decreases with increasing tracking errors.

This study examines the dependence of tropical cyclone (TC) intensity forecast errors on track forecast errors in the Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) model. Using real-time forecasts and retrospective experiments during 2015–18, verification of TC intensity errors conditioned on different 5-day track error thresholds shows that reducing the 5-day track errors by 50%–70% can help reduce the absolute intensity errors by 18%–20% in the 2018 version of the COAMPS-TC model. Such impacts of track errors on the TC intensity errors are most persistent at 4–5-day lead times in all three major ocean basins, indicating a significant control of global models on the forecast skill of the COAMPS-TC model. It is of interest to find, however, that lowering the 5-day track errors below 80 n mi (1 n mi = 1.852 km) does not reduce TC absolute intensity errors further. Instead, the 4–5-day intensity errors appear to be saturated at around 10–12 kt (1 kt ≈ 0.51 m s−1) for cases with small track errors, thus suggesting the existence of some inherent intensity errors in regional models. Additional idealized simulations under a perfect model scenario reveal that the COAMPS-TC model possesses an intrinsic intensity variation at the TC mature stage in the range of 4–5 kt, regardless of the large-scale environment. Such intrinsic intensity variability in the COAMPS-TC model highlights the importance of potential chaotic TC dynamics, rather than model deficiencies, in determining TC intensity errors at 4–5-day lead times. These results suggest a fundamental limit in the improvement of TC intensity forecasts by numerical models that one should consider in future model development and evaluation.

This paper presents a trajectory tracking control scheme for a three wheeled mobile robot using sliding mode control method system. The indoor mobile robot system is subject to parameter perturbation and external interference during trajectory tracking control, and the problem of slow tracking speed and large tracking error will occur. In order to improve the tracking precision, a sliding mode control algorithm based on new approach law is proposed. Firstly, the motion control model of mobile robot is established, and a sliding mode trajectory tracking controller is designed. Then adopting MATLAB to track the linear and circular trajectories, the tracking error converges to zero around 3s. Experimental results show that sliding mode control method effectively reduces sliding mode control input chattering, and the convergence speed and tracking accuracy improve. Finally, the effectiveness of the control method is thus verified in practice.

This paper presents a colon deformation estimation method, which can be used to estimate colon deformations during colonoscope insertions. Colonoscope tracking or navigation system that navigates a physician to polyp positions during a colonoscope insertion is required to reduce complications such as colon perforation. A previous colonoscope tracking method obtains a colonoscope position in the colon by registering a colonoscope shape and a colon shape. The colonoscope shape is obtained using an electromagnetic sensor, and the colon shape is obtained from a CT volume. However, large tracking errors were observed due to colon deformations occurred during colonoscope insertions. Such deformations make the registration difficult. Because the colon deformation is caused by a colonoscope, there is a strong relationship between the colon deformation and the colonoscope shape. An estimation method of colon deformations occur during colonoscope insertions is necessary to reduce tracking errors. We propose a colon deformation estimation method. This method is used to estimate a deformed colon shape from a colonoscope shape. We use the regression forests algorithm to estimate a deformed colon shape. The regression forests algorithm is trained using pairs of colon and colonoscope shapes, which contains deformations occur during colonoscope insertions. As a preliminary study, we utilized the method to estimate deformations of a colon phantom. In our experiments, the proposed method correctly estimated deformed colon phantom shapes.

In high-precision fields such as advanced manufacturing, semiconductor processing, aerospace assembly, and precision machining, motion control systems often face challenges such as large tracking errors and low control efficiency due to complex dynamic environments. To address this, this paper innovatively proposes a data-driven feedforward compensation control strategy based on a Parallel Gated Recurrent Unit (GRU)–Transformer. This method does not require an accurate model of the controlled object but instead uses motion error data and controller output data collected from actual operating conditions to complete network training and real-time prediction, thereby reducing data requirements. The proposed feedforward control strategy consists of three main parts: first, a Parallel GRU–Transformer prediction model is constructed using real-world data collected from high-precision sensors, enabling precise prediction of system motion errors after a single training session; second, a nonlinear PD controller is introduced, using the prediction errors output by the Parallel GRU–Transformer network as input to generate the primary correction force, thereby significantly reducing reliance on the main controller; and finally, the output of the nonlinear PD controller is combined with the output of the main controller to jointly drive the precision motion platform. Verification on a permanent magnet synchronous linear motor motion platform demonstrates that the control strategy integrating Parallel GRU–Transformer feedforward compensation significantly reduces the tracking error and fluctuations under different trajectories while minimizing moving average (MA) and moving standard deviation (MSD), enhancing the system’s robustness against environmental disturbances and effectively alleviating the load on the main controller. The proposed method provides innovative insights and reliable guarantees for the widespread application of precision motion control in industrial and research fields.

In this paper, a non-time based tracking controller of a nonholonomic mobile robot is first analyzed. Non-time based motion controllers have been successfully applied to many areas such as robot motion control, multi-robot coordination, force control, robotic teleoperation and manufacturing automation. However, by the traditional non-time based motion controller many suffer from oscillations in both the linear and angular velocities when there is a large initial tracking error. In this paper, a traditional non-time based tracking controller is optimized using a genetic algorithm, which is used to generate the model parameters that could guarantee the system stability and convergence of tracking error. Simulations using a nonholonomic mobile robot model with a four degree of freedom are conducted to investigate the performance of the proposed controller. The results using the proposed model is compared to those of the conventional model. Generally the proposed model performs better than the conventional model because the genetic algorithm can provide better parameters to minimize tracking error and the oscillation.

In the last decade, robotic systems have penetrated human life more than human can imagine. In particular, the multi-mobile robotic systems have faced a fast growing due to the fact that by deploying a large collection of mobile robots the overall system has a high redundancy and offers the capability of handling more complex tasks. A group of mobile robots increases robustness against failures and provide flexibility to system changes. In order to achieve a fully autonomous operation, the control algorithm to coordinate the robots becomes more important and decisive. The controller has to meet different requirements that sometimes are conflicting. The main goal of this thesis is to evaluate performance of coordination control algorithms for a group of mobile robots. In the first part of this thesis, a modular framework for simulation and experiments of coordination control of mobile robots is presented. All connections between the modules follow the subscriber/publisher paradigm on the exchanged data, i.e. a component publishes data and other components can subscribe on that data. The data is identified by a named magazine, and actual data packets are called issues of the magazine. By means of simulation and experiments, it is demonstrated how the modularity in the framework allows straightforward modification of system configurations, design parameters, and control algorithms. In the second part, hierarchical coordination control of mobile robots is presented. The hierarchy consists of three layers: a high-level control for motion planning of the robots, a low-level control for motion execution, and a flexible layer to accommodate shifting of responsibilities. One advantage of using a hierarchical approach is the isolation of control design in each layer. Changes in the control strategy of a layer do not necessarily require adaptation of other layers. The proposed control algorithms are used to coordinate a group of unicycle mobile robots that realize the transportation system of an automated warehouse. Using the framework, series of simulations with different design parameters are conducted, as well as real-time experiments. Subsequently, performance analysis of the results is carried out. It is shown that the proposed algorithms are flexible to system changes and scalable to variation in the warehouse demands. The algorithms are robust against failures in the transportation systems. It is found that an algorithm, denoted high-level control, with higher throughputs requires more information sharing between the robots. This algorithm is less robust against failures compared to the algorithm, denoted low-level control, that yields lower throughput. In addition, a cost analysis of the proposed control algorithms is given. It is studied that the cost of realizing the transport using a group of mobile robots, coordinated by the proposed control algorithm, has similar performance-to-cost ratio compared to conveyor systems. This conclusion is very promising having in minds that the proposed control algorithm is not necessarily the optimal one. In the third part, the problem of simultaneous tracking of individual references and formation keeping for a group of mobile robots is investigated. The control algorithm is developed using dynamic feedback linearization. The stability proof is analyzed using the theorem on interconnected systems. Using a root-mean-square-like indicator, the trade off between individual tracking and formation keeping, as well as the influences of communication topologies are analyzed. From the analysis of real-time experiment results, it is found that the best formation keeping is obtained when all robots communicate. As a trade off, this requires a larger communication bandwidth and yields large individual tracking errors. Furthermore, the analysis suggests that to achieve optimal individual tracking and formation keeping simultaneously, there is less need to share information between the robots. In the fourth part, as a complement to the hierarchical control approach, a coordination control algorithm based on Model Predictive Control (MPC) is presented. Focusing on the practical aspects, a sequentially decentralized MPC, i.e. a single MPC computes the control signals of all robots where priority rules are used to determine which robots handled earlier in optimization procedure, is implemented. Using the similar automated warehouse environment, the sequentially decentralized MPC is validated in simulations and real-time experiments. For comparison purposes, a centralized MPC, i.e. a single MPC computes the control signals of all robots, is also implemented. Using completion time as indicators, the influences of the MPC parameters are investigated. It is found that the centralized MPC is better than the sequentially decentralizwed MPC, but with the cost of high computation load and limited number of robots in real-time application. Relevance and performance comparison between the MPC and the hierarchical control approach are presented. It is found that regardless of the control algorithm choices, in terms of completion time, a better performance is obtained when the control algorithm uses information from all robots in computing the control signals.

Ocean currents and waves can cause the initial position of an underwater vehicle to deviate from the initial state of the desired trajectory. For such cases, this paper investigates the finite‐time tracking control problem for a vehicle in the presence of large initial tracking errors and external disturbances. A continuous finite‐time tracking control scheme is developed for this scenario based on an improved nonsingular terminal sliding mode surface modified by a piecewise function. Compared to the alternative approaches, a method based on error transformation is added to the sliding mode surface, resulting in improved tracking accuracy in the steady state. Then, to reduce the effect of large initial tracking error on the control input signals, an exponential decay function about the initial location of the vehicle is developed to obtain the control law by combining it with a strictly generalized saturation function. In contrast to alternative approaches, a nonlinear structure is used to counteract the approximation error of the neural network used in the modeling of the dynamics. Moreover, it is shown that the tracking error converges to a small neighborhood of zero in a fixed time. Finally, the new control scheme's effectiveness is verified by simulation studies on an underwater vehicle model and the results compared to other existing control designs.