Sliding mode based lateral control for UAVs using piecewise linear sliding surface

Lateral Control for UAVs using Sliding Mode Technique

Target tracking with very long range is studied in this paper. Such tracking problem has severe measurement nonlinearity that will cause consistency problems and large tracking errors. Gaussian mixture measurements are obtained by dividing the measurement likelihood into several Gaussian components. The Gaussian Mixture Measurement-Integrated Track Splitting (GMM-ITS) is applied to very long range tracking scenarios. The simulation results show that the GMM-ITS can produce consistency in the filtering results crucial to the filter performance. Furthermore, it is also able to estimate the target state accurately with small tracking errors.

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.

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.

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.

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.

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.

To improve the path tracking performance of intelligent hybrid articulated tractors in all working conditions in unmanned operation, a path-tracking control method based on corrected model predictive control is proposed. The kinematic model of the tractor is established by analyzing the tractor’s kinematics. Taking the lateral and longitudinal errors as the target and the speed and articulation angular acceleration as the constraints, a tracking control algorithm based on model predictive control is proposed. In addition, to improve the transient performance of the tractor in the path tracking process, the proportional-integral-derivative controller and fuzzy controller are used to correct the model-predicted output articulation angular acceleration, forming a corrected model predictive control path tracking control method. To verify the effectiveness of the control method, model predictive control is used as a comparison method, and the effectiveness of the proposed method is verified based on the MATLAB 2024a simulation platform. The results show that compared with the MPC algorithm, the speed standard deviation is reduced by 2%, the longitudinal tracking error is reduced by 8%, and the lateral tracking error is reduced by 50%. The proposed method can effectively improve the path-tracking accuracy of the intelligent hybrid tractor.

We present an analytical design and experimental verification of trajectory tracking control of a 7-DOF robot manipulator, which achieves convergence of all tracking errors to the origin within a finite terminal time, also referred to as the “settling time.” A key feature of this control strategy is that the settling time is explicitly assigned by the control designer to a value desired, or “prescribed” by the user and that the settling time is independent of the initial conditions and of the reference signal. In order to achieve this beneficial property with the controller, a scaling of the state by a function of time that grows unbounded toward the terminal time is employed. Through Lyapunov analysis, we first demonstrate that the proposed controller achieves regulation of all tracking errors within the prescribed time as well as the uniform boundedness of the joint torques, even in the presence of a matched, nonvanishing disturbance. Then, through both simulation and experiment, we demonstrate that the proposed controller is capable of converging to the desired trajectory within the prescribed time, despite large distance between the initial conditions and the reference trajectory, i.e., in spite of large initial tracking errors, and in spite of a sinusoidal disturbance being applied in each joint.

A novel tracking controller is proposed in this paper for real-time navigation of a nonholonomic mobile robot based on a neural dynamics model and a conventional backstepping model. By taking advantage of the features of the neural dynamics in a gated dipole model, the proposed controller is capable of generating smooth, continuous control signals to track a desired path. In addition, it can deal with arbitrarily large initial tracking errors. It resolves the velocity jump problem in conventional backstepping controllers resulted from sudden changes of tracking errors. The proposed approach is not very sensitive to model parameters and converges faster than a previous tracking control model. The effectiveness and efficiency of the proposed approach is demonstrated by a series of simulation and comparison studies.

This paper presents an evaluation of the Tango tablet regarding its motion tracking and depth perception capabilities. A methodology for performing such kind of evaluation is proposed. Motion tracking error is assessed in both small workspace and large environments. In the small workspace scenario, the distances reported by the motion tracking system are compared with values measured with the aid of a graph paper. In the large environment condition, the tracking error consists in the difference between initial and final positions given by the system when the device moves around the environment and returns to the same location. Depth sensing precision is evaluated by comparing the 3D coordinates reported by the system of the inner corners of a chessboard pattern with ground truth values obtained from color camera image processing. The results show that Tango tablet sometimes presents large motion tracking errors, which may harm AR experience. In addition, Tango tablet depth sensing presents average error values similar to desktop depth cameras, but it is more sensitive to infrared reflection properties of the objects to be mapped.