As a dynamic projection to latent structures (PLS) method with a good output prediction ability, dynamic inner PLS (DiPLS) is widely used in the prediction of key performance indicators. However, due to the oblique decomposition of the input space by DiPLS, there are false alarms in the actual industrial process during fault detection. To address the above problems, a dynamic modeling method based on autoregressive-dynamic inner total PLS (AR-DiTPLS) is proposed. The method first uses the regression relation matrix to decompose the input space orthogonally, which reduces useless information for the prediction output in the quality-related dynamic subspace. Then, a vector autoregressive model (VAR) is constructed for the prediction score to separate dynamic information and static information. Based on the VAR model, appropriate statistical indicators are further constructed for online monitoring, which reduces the occurrence of false alarms. The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.
To solve the finite-time error-tracking problem in missile guidance, this paper presents a unified design approach through error dynamics and free-time convergence theory. The proposed approach is initiated by establishing a desired model for free-time convergent error dynamics, characterized by its independence from initial conditions and guidance parameters, and adjustable convergence time. This foundation facilitates the derivation of specific guidance laws that integrate constraints such as leading angle, impact angle, and impact time. The theoretical framework of this study elucidates the nuances and synergies between the proposed guidance laws and existing methodologies. Empirical evaluations through simulation comparisons underscore the enhanced accuracy and adaptability of the proposed laws.
To solve the problem that multiple missiles should simultaneously attack unmeasurable maneuvering targets, a guidance law with temporal consistency constraint based on the super-twisting observer is proposed. Firstly, the relative motion equations between multiple missiles and targets are established, and the topological model among multiple agents is considered. Secondly, based on the temporal consistency constraint, a cooperative guidance law for simultaneous arrival with finite-time convergence is derived. Finally, the unknown target maneuvering is regarded as bounded interference. Based on the second-order sliding mode theory, a super-twisting sliding mode observer is devised to observe and track the bounded interference, and the stability of the observer is proved. Compared with the existing research, this approach only needs to obtain the sliding mode variable which simplifies the design process. The simulation results show that the designed cooperative guidance law for maneuvering targets achieves the expected effect. It ensures successful cooperative attacks, even when confronted with strong maneuvering targets.
Final velocity and impact angle are critical to missile guidance. Computationally efficient guidance law with comprehensive consideration of the two performance merits is challenging yet remains less addressed. Therefore, this paper seeks to solve a type of optimal control problem that maximizes final velocity subject to equality point constraint of impact angle constraint. It is proved that the crude problem of maximizing final velocity is equivalent to minimizing a quadratic-form cost of curvature. The closed-form guidance law is henceforth derived using optimal control theory. The derived analytical guidance law coincides with the widely-used optimal guidance law with impact angle constraint (OGL-IAC) with a set of navigation parameters of two and six. On this basis, the optimal emission angle is determined to further increase the final velocity. The derived optimal value depends solely on the initial line-of-sight angle and impact angle constraint, and thus practical for real-world applications. The proposed guidance law is validated by numerical simulation. The results show that the OGL-IAC is superior to the benchmark guidance laws both in terms of final velocity and missing distance.
The quantum entangled photon-pair source, as an essential component of optical quantum systems, holds great potential for applications such as quantum teleportation, quantum computing, and quantum imaging. The current workhorse technique for preparing photon pairs involves performing spontaneous parametric down conversion (SPDC) in bulk nonlinear crystals. However, the current power consumption and cost of preparing entangled photon-pair sources are relatively high, posing challenges to their integration and scalability. In this paper, we propose a low-power system model for the quantum entangled photon-pair source based on SPDC theory and phase matching technology. This model allows us to analyze the performance of each module and the influence of component characteristics on the overall system. In our experimental setup, we utilize a 5 mW laser diode and a typical type-II barium metaborate (BBO) crystal to prepare an entangled photon-pair source. The experimental results are in excellent agreement with the model, indicating a significant step towards achieving the goal of low-power and low-cost entangled photon-pair sources. This achievement not only contributes to the practical application of quantum entanglement lighting, but also paves the way for the widespread adoption of optical quantum systems in the future.
In this paper, a method for spoofing detection based on the variation of the signal’s carrier-to-noise ratio (CNR) is proposed. This method leverages the directionality of the antenna to induce varying gain changes in the signals across different incident directions, resulting in distinct CNR variations for each signal. A model is developed to calculate the variation value of the signal CNR based on the antenna gain pattern. This model enables the differentiation of the variation values of the CNR for authentic satellite signals and spoofing signals, thereby facilitating spoofing detection. The proposed method is capable of detecting spoofing signals with power and CNR similar to those of authentic satellite signals. The accuracy of the signal CNR variation value calculation model and the effectiveness of the spoofing detection method are verified through a series of experiments. In addition, the proposed spoofing detection method works not only for a single spoofing source but also for distributed spoofing sources.
The laser-guided bomb (LGB) is an air-to-ground precision-guided weapon that offers high hit rates, great power, and ease of use. LGBs are guided by semi-active laser ground-seeking technology, which means that atmospheric conditions can affect their accuracy. The spatial release region (SRR) of LGBs is difficult to calculate precisely, especially when there is a poor field of view. This can result in a lower real hit probability. To increase the hit probability of LGBs in tough atmospheric situations, a novel method for calculating the SRR has been proposed. This method is based on the transmittance model of the 1.06 μm laser in atmospheric species and the laser diffuse reflection model of the target surface to determine the capture target time of the laser seeker. Then, it calculates the boundary ballistic space starting position by ballistic model and gets the spatial scope of the spatial release region. This method can determine the release region of LGBs based on flight test data such as instantaneous velocity, altitude, off-axis angle, and atmospheric visibility. By more effectively employing aircraft release conditions, atmospheric visibility and other factors, the SRR calculation method can improve LGB hit probability by 9.2%.
Missile interception problem can be regarded as a two-person zero-sum differential games problem, which depends on the solution of Hamilton-Jacobi-Isaacs (HJI) equation. It has been proved impossible to obtain a closed-form solution due to the nonlinearity of HJI equation, and many iterative algorithms are proposed to solve the HJI equation. Simultaneous policy updating algorithm (SPUA) is an effective algorithm for solving HJI equation, but it is an on-policy integral reinforcement learning (IRL). For online implementation of SPUA, the disturbance signals need to be adjustable, which is unrealistic. In this paper, an off-policy IRL algorithm based on SPUA is proposed without making use of any knowledge of the systems dynamics. Then, a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is presented. Based on the online off-policy IRL method, a computational intelligence interception guidance (CIIG) law is developed for intercepting high-maneuvering target. As a model-free method, intercepting targets can be achieved through measuring system data online. The effectiveness of the CIIG is verified through two missile and target engagement scenarios.
The observation error model of the underwater acoustic positioning system is an important factor to influence the positioning accuracy of the underwater target. For the position inconsistency error caused by considering the underwater target as a mass point, as well as the observation system error, the traditional error model best estimation trajectory (EMBET) with little observed data and too many parameters can lead to the ill-condition of the parameter model. In this paper, a multi-station fusion system error model based on the optimal polynomial constraint is constructed, and the corresponding observation system error identification based on improved spectral clustering is designed. Firstly, the reduced parameter unified modeling for the underwater target position parameters and the system error is achieved through the polynomial optimization. Then a multi-station non-oriented graph network is established, which can address the problem of the inaccurate identification for the system errors. Moreover, the similarity matrix of the spectral clustering is improved, and the iterative identification for the system errors based on the improved spectral clustering is proposed. Finally, the comprehensive measured data of long baseline lake test and sea test show that the proposed method can accurately identify the system errors, and moreover can improve the positioning accuracy for the underwater target positioning.
The industrial Internet of Things (IIoT) is a new industrial idea that combines the latest information and communication technologies with the industrial economy. In this paper, a cloud control structure is designed for IIoT in cloud-edge environment with three modes of 5G. For 5G based IIoT, the time sensitive network (TSN) service is introduced in transmission network. A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration. For a transmission control protocol (TCP) model with nonlinear disturbance, time delay and uncertainties, a robust adaptive fuzzy sliding mode controller (AFSMC) is given with control rule parameters. IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows. IIoT workflow scheduling is a non-deterministic polynomial (NP)-hard problem in cloud-edge environment. An adaptive and non-local-convergent particle swarm optimization (ANCPSO) is designed with nonlinear inertia weight to avoid falling into local optimum, which can reduce the makespan and cost dramatically. Simulation and experiments demonstrate that ANCPSO has better performances than other classical algorithms.
To investigate the real-time mean orbital elements (MOEs) estimation problem under the influence of state jumping caused by non-fatal spacecraft collision or protective orbit transfer, a modified augmented square-root unscented Kalman filter (MASUKF) is proposed. The MASUKF is composed of sigma points calculation, time update, modified state jumping detection, and measurement update. Compared with the filters used in the existing literature on MOEs estimation, it has three main characteristics. Firstly, the state vector is augmented from six to nine by the added thrust acceleration terms, which makes the filter additionally give the state-jumping-thrust-acceleration estimation. Secondly, the normalized innovation is used for state jumping detection to set detection threshold concisely and make the filter detect various state jumping with low latency. Thirdly, when sate jumping is detected, the covariance matrix inflation will be done, and then an extra time update process will be conducted at this time instance before measurement update. In this way, the relatively large estimation error at the detection moment can significantly decrease. Finally, typical simulations are performed to illustrated the effectiveness of the method.
With the development of positioning technology, location services are constantly in demand by people. As a primary location service pedestrian navigation has two main approaches based on radio and inertial navigation. The pedestrian navigation based on radio is subject to environmental occlusion leading to the degradation of positioning accuracy. The pedestrian navigation based on micro-electro-mechanical system inertial measurement unit (MIMU) is less susceptible to environmental interference, but its errors dissipate over time. In this paper, a chest card pedestrian navigation improvement method based on complementary correction is proposed in order to suppress the error divergence of inertial navigation methods. To suppress attitude errors, optimal feedback coefficients are established by pedestrian motion characteristics. To extend navigation time and improve positioning accuracy, the step length in subsequent movements is compensated by the first step length. The experimental results show that the positioning accuracy of the proposed method is improved by more than 47% and 44% compared with the pure inertia-based method combined with step compensation and the traditional complementary filtering combined method with step compensation. The proposed method can effectively suppress the error dispersion and improve the positioning accuracy.
In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF) master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.
Tracking the fast-moving object in occlusion situations is an important research topic in computer vision. Despite numerous notable contributions have been made in this field, few of them simultaneously incorporate both object’s extrinsic features and intrinsic motion patterns into their methodologies, thereby restricting the potential for tracking accuracy improvement. In this paper, on the basis of efficient convolution operators (ECO) model, a speed-accuracy-balanced model is put forward. This model uses the simple correlation filter to track the object in real-time, and adopts the sophisticated deep-learning neural network to extract high-level features to train a more complex filter correcting the tracking mistakes, when the tracking state is judged to be poor. Furthermore, in the context of scenarios involving regular fast-moving, a motion model based on Kalman filter is designed which greatly promotes the tracking stability, because this motion model could predict the object’s future location from its previous movement pattern. Additionally, instead of periodically updating our tracking model and training samples, a constrained condition for updating is proposed, which effectively mitigates contamination to the tracker from the background and undesirable samples avoiding model degradation when occlusion happens. From comprehensive experiments, our tracking model obtains better performance than ECO on object tracking benchmark 2015 (OTB100), and improves the area under curve (AUC) by about 8% and 32% compared with ECO, in the scenarios of fast-moving and occlusion on our own collected dataset.
Detumbling operation toward a rotating target with nutation is meaningful for debris removal but challenging. In this study, a deformable end-effector is first designed based on the requirements for contacting the nutating target. A dual-arm robotic system installed with the deformable end-effectors is modeled and the movement of the end-tips is analyzed. The complex operation of the contact toward a nutating target places strict requirements on control accuracy and controller robustness. Thus, an improvement of the tracking error transformation is proposed and an adaptive sliding mode controller with prescribed performance is designed to guarantee the fast and precise motion of the effector during the contact detumbling. Finally, by employing the proposed effector and the controller, numerical simulations are carried out to verify the effectiveness and efficiency of the contact detumbling toward a nutating target.
This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift. In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79% and 7.16% respectively in comparison to the traditional calibration method.
An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles. The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper, we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’ information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.
Angles-only relative orbit determination for space non-cooperative targets based on passive sensor is subject to weakly observable problem of the relative state between two spacecraft. Previously, the evidence for angles-only observability was found by using cylindrical dynamics, however, the solution of orbit determination is still not provided. This study develops a relative orbit determination algorithm with the cylindrical dynamics based on differential evolution. Firstly, the relative motion dynamics and line-of-sight measurement model for near-circular orbit are established in cylindrical coordinate system. Secondly, the observability is qualitatively analyzed by using the dynamics and measurement model where the unobservable geometry is found. Then, the angles-only relative orbit determination problem is modeled into an optimal searching frame and an improved differential evolution algorithm is introduced to solve the problem. Finally, the proposed algorithm is verified and tested by a set of numerical simulations in the context of high-Earth and low-Earth cases. The results show that initial relative orbit determination (IROD) solution with an appropriate accuracy in a relative short span is achieved, which can be used to initialize the navigation filter.
An adaptive control approach is presented in this paper for tracking desired trajectories in interactive manipulators. The controller design incorporates prescribed performance functions (PPFs) to improve dynamic performance. Notably, the performance of the output error is confined in an envelope characterized by exponential convergence, leading to convergence to zero. This feature ensures a prompt response from admittance control and establishes a reliable safety framework for interactions. Simulation results provide practical insights, demonstrating the viability of the control scheme proposed in this paper.
In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory (LSTM) neural network is nested into the extended Kalman filter (EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states, an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF (AEKF) when there exist large uncertainties in the system model.
Collaborative coverage path planning (CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle (UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment. Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with pattern-based genetic algorithm (PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.
Aiming at the shortcoming that the traditional industrial manipulator using off-line programming cannot change along with the change of external environment, the key technologies such as machine vision and manipulator control are studied, and a complete manipulator vision tracking system is designed. Firstly, Denavit-Hartenberg (D-H) parameters method is used to construct the model of the manipulator and analyze the forward and inverse kinematics equations of the manipulator. At the same time, a binocular camera is used to obtain the three-dimensional position of the target. Secondly, in order to make the manipulator track the target more accurately, the fuzzy adaptive square root unscented Kalman filter (FSRUKF) is proposed to estimate the target state. Finally, the manipulator tracking system is built by using the position-based visual servo. The simulation experiments show that FSRUKF converges faster and with less error than the square root unscented Kalman filter (SRUKF), which meets the application requirements of the manipulator tracking system, and basically meets the application requirements of the manipulator tracking system in the practical experiments.
In this paper, an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle. Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time. The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatio-temporal aligned hull deformation measurement model. In addition, two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation. The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.
It is important to calculate the reachable domain (RD) of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient database-generation method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node (RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than ${0.01^ \circ }$ on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.
With the increasing precision of guidance, the impact of autopilot dynamic characteristics and target maneuvering abilities on precision guidance is becoming more and more significant. In order to reduce or even eliminate the autopilot dynamic operation and the target maneuvering influence, this paper suggests a guidance system model involving a novel integral sliding mode guidance law (ISMGL). The method utilizes the dynamic characteristics and the impact angle, combined with a sliding mode surface scheme that includes the desired line-of-sight angle, line-of-sight angular rate, and second-order differential of the angular line-of-sight. At the same time, the evaluation scenario considere the target maneuvering in the system as the external disturbance, and the non-homogeneous disturbance observer estimate the target maneuvering as a compensation of the guidance command. The proposed system’s stability is proven based on the Lyapunov stability criterion. The simulations reveale that ISMGL effectively intercepted large maneuvering targets and present a smaller miss-distance compared with traditional linear sliding mode guidance laws and trajectory shaping guidance laws. Furthermore, ISMGL has a more accurate impact angle and fast convergence speed.
The design of mini-missiles (MMs) presents several novel challenges. The stringent mission requirement to reach a target with a certain precision imposes a high guidance precision. The miniaturization of the size of MMs makes the design of the guidance, navigation, and control (GNC) have a larger-than-before impact on the main-body design (shape, motor, and layout design) and its design objective, i.e., flight performance. Pursuing a trade-off between flight performance and guidance precision, all the relevant interactions have to be accounted for in the design of the main body and the GNC system. Herein, a multi-objective and multidisciplinary design optimization (MDO) is proposed. Disciplines pertinent to motor, aerodynamics, layout, trajectory, flight dynamics, control, and guidance are included in the proposed MDO framework. The optimization problem seeks to maximize the range and minimize the guidance error. The problem is solved by using the nondominated sorting genetic algorithm II. An optimum design that balances a longer range with a smaller guidance error is obtained. Finally, lessons learned about the design of the MM and insights into the trade-off between flight performance and guidance precision are given by comparing the optimum design to a design provided by the traditional approach.
In order to effectively defend against the threats of the hypersonic gliding vehicles (HGVs), HGVs should be tracked as early as possible, which is beyond the capability of the ground-based radars. Being benefited by the developing mega-constellations in low-Earth orbit, this paper proposes a relay tracking mode to track HGVs to overcome the above problem. The whole tracking mission is composed of several tracking intervals with the same duration. Within each tracking interval, several appropriate satellites are dispatched to track the HGV. Satellites that are planned to take part in the tracking mission are selected by a new derived observability criterion. The tracking performances of the proposed tracking mode and the other two traditional tracking modes, including the stare and track-rate modes, are compared by simulation. The results show that the relay tracking mode can track the whole trajectory of a HGV, while the stare mode can only provide a very short tracking arc. Moreover, the relay tracking mode achieve higher tracking accuracy with fewer attitude controls than the track-rate mode.
The formation control of multiple unmanned aerial vehicles (multi-UAVs) has always been a research hotspot. Based on the straight line trajectory, a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption. In order to avoid the collision between UAVs in the formation process, the concept of safety ball is introduced, and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs. Based on the idea of game theory, a method of UAV motion form setting based on the maximization of interests is proposed, including the maximization of self-interest and the maximization of formation interest is proposed, so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance. Finally, through simulation verification, the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length, and the UAV motion selection method based on the maximization interests can effectively complete the task formation.
In this paper, a bandwidth-adjustable extended state observer (ABESO) is proposed for the systems with measurement noise. It is known that increasing the bandwidth of the observer improves the tracking speed but tolerates noise, which conflicts with observation accuracy. Therefore, we introduce a bandwidth scaling factor such that ABESO is formulated to a 2-degree-of-freedom system. The observer gain is determined and the bandwidth scaling factor adjusts the bandwidth according to the tracking error. When the tracking error decreases, the bandwidth decreases to suppress the noise, otherwise the bandwidth does not change. It is proven that the error dynamics are bounded and converge in finite time. The relationship between the upper bound of the estimation error and the scaling factor is given. When the scaling factor is less than 1, the ABESO has higher estimation accuracy than the linear extended state observer (LESO). Simulations of an uncertain nonlinear system with compound disturbances show that the proposed ABESO can successfully estimate the total disturbance in noisy environments. The mean error of total disturbance of ABESO is 15.28% lower than that of LESO.
The robotic airship can provide a promising aerostatic platform for many potential applications. These applications require a precise autonomous trajectory tracking control for airship. Airship has a nonlinear and uncertain dynamics. It is prone to wind disturbances that offer a challenge for a trajectory tracking control design. This paper addresses the airship trajectory tracking problem having time varying reference path. A lumped parameter estimation approach under model uncertainties and wind disturbances is opted against distributed parameters. It uses extended Kalman filter (EKF) for uncertainty and disturbance estimation. The estimated parameters are used by sliding mode controller (SMC) for ultimate control of airship trajectory tracking. This comprehensive algorithm, EKF based SMC (ESMC), is used as a robust solution to track airship trajectory. The proposed estimator provides the estimates of wind disturbances as well as model uncertainty due to the mass matrix variations and aerodynamic model inaccuracies. The stability and convergence of the proposed method are investigated using the Lyapunov stability analysis. The simulation results show that the proposed method efficiently tracks the desired trajectory. The method solves the stability, convergence, and chattering problem of SMC under model uncertainties and wind disturbances.
