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The tracking control problem for a group of nonlinear lower-triangular systems with multiple uncertainties is investigated in this work. Wherein, a novel performance constraint is first constructed to guarantee the fixed-time convergence of the output tracking error. Subsequently, a linear extended high-gain observer is employed to estimate the system uncertainties including the unmeasured states and the external disturbances. Based on the observer estimations, a novel output-feedback tracking control approach is formulated via using the backstepping technique, to ensure the boundedness of the closed-loop system. Compared with the existing works, the primary advantages of the proposed design are that (1) the problem of "explosion of terms" is avoided by eliminating the need for the derivative of the virtual control signals and (2) without the use of extra auxiliary techniques, the fixed-time convergence can be guaranteed, where the convergence time is independent of the system states and initial conditions. Then, the results about system stability are proved by the theoretical analysis. Moreover, an extension of the proposed approach to multi-input multi-output systems is deduced in this paper to further present its versatility. Finally, two groups of numerical examples are performed to validate the effectiveness of the proposed controller.

Attitude determination is fundamental for spacecraft missions in aerospace engineering. Kalman filter (KF) is the optimal estimator in least square sense and, using the symmetry properties of matrix Lie groups system, the invariant Kalman filter (IKF) has been developed to boost the filtering performance for attitude estimation. However, due to presence of frequent and severer maneuvers, the Lie groups attitude dynamics is usually corrupted by significant biases and heavy-tailed outliers, which usually leads to decreased precision of IKF. For attitude estimation problem troubled by biased and heavy-tailed process noise, this work proposes a new invariant Kalman filter (VBAIKF) by constructing the hierarchical Gaussian system model: the probability density function of prior estimate state is first described using the student’s t distribution, while the unknown scale covariance matrix and degrees of freedom (dof) of the employed student’s t distribution are defined as the inverse Wishart distribution (IWD) and Gamma distribution. In VBAIKF, the Lie groups rotation matrix of spacecraft, the biased mean, the parameters of dof and scale covariance matrix are online estimated together by variational Bayesian fixed-point iterations. The simulation results with Lie groups attitude estimation system further verify the superior filtering adaptability and precision of proposed approach VBAIKF than other methods for attitude determination with biased mean and heavy-tailed process noise.

Celestial navigation using time delay measurement is an innovative autonomous navigation method. To calculate the equivalent measurement, the numerical method needs to be applied, which is time-consuming. The event-triggered mechanism intermittently and aperiodically processes measurements by judging if the update error has changed drastically. However, its performance is greatly affected by the constant threshold. To solve this problem, a parameter-independent event-triggered implicit unscented Kalman filter (UKF) is proposed and applied to the celestial navigation using time delay measurement. The innovation at the current moment and the updated estimate covariance at the last moment are compared with the previous value instead of the constant threshold. The event is automatically triggered when the accuracy of the state estimate is low. Simulation results indicate that the proposed parameter-independent event-triggered implicit UKF can reduce the running time by reducing unnecessary measurement updates, whose performance will not be affected by any parameter or window size. In a word, the proposed method substitutes the dynamic threshold for the constant threshold, ensuring that its performance will not be affected by any parameter or window size.

This article addresses the multiagent systems consensus control problem via a learning observer-based performance tuning control policy. Specifically, a novel learning observer is presented to reconstruct the compound nonlinear terms and system states simultaneously. Based on the learning observer’s reconstructed information, a novel performance tuning control policy is proposed to deal with the internal nonlinear terms and external disturbances acting on the system while providing prescribed consensus performance. The proposed learning observer can guarantee the uniformly ultimately bounded estimation while saving computing resources, which is beneficial to the multiagent system. The proposed control policy, combined with observer and performance tuning, ensures the robustness to nonideal perturbations and the high accuracy control performance simultaneously. Mathematical simulations verify the effectiveness of the control algorithm.

In practical flight process, the time-varying disturbances are often encountered by the space unmanned systems, and the attitude outputs of the space unmanned systems are required to stay in the predefined intervals. Moreover, the convergence process of the space unmanned systems has to be achieved in finite time, to complete the given tasks. However, most of the existing results are difficult to be applied to give consideration to the finite-time-convergence, the constrained outputs and the suppression for the time-varying disturbances simultaneously. To address this problem, in this article, we propose a novel fuzzy adaptive finite-time anti-disturbance control scheme for the space unmanned systems. The super-twisting disturbance observer, which possess the robust and finite-time disturbance estimation ability, has been utilized to suppress the time-varying disturbances. The nonlinear signal transformation technique has been introduced, transforming the constrained outputs into a novel unconstrained auxiliary variable, and the output constrained control issue becomes an equivalent bounded control problem. To achieve the finite-time convenience of the closed-loop space unmanned system, the fractional control laws have been designed, and an important lemma has been utilized to design the update laws of the adaptive parameters. Moreover, the fuzzy logic systems have been used to improve the robustness respect to the uncertainties. The contrastive simulation results have been provided, the finite-time control ability of the proposed method and the satisfactory estimation performance of the super-twisting disturbance observer can be observed.

This paper addresses the finite time prescribed performance control problem for hypersonic flight vehicle systems with abrupt actuator faults and time-varying uncertainties. The considered actuator faults contains abrupt gain faults and saltatorial bias faults, and the unknown discontinuous variations of the fault parameters are permitted. In virtue of the finite time performance functions and error transformation, the predefined tracking performance including the convergence time, the tracking error and the overshoot for the velocity and altitude can be ensured. By adaptively estimating the lower bonds of the gain faults and the upper bounds of the bias faults, the unwanted effects of the abruptly faulty actuators can be circumvented. Meanwhile, for the purpose of compensating the unknown nonlinearities, the learning-based control strategy has been employed. Finally, a number of simulation results on the hypersonic flight vehicles are conducted to demonstrate the validity of the proposed approach.

In practical, there often exist structural uncertainties, measurement errors, and the aerodynamic perturbations in the flight control systems of the hypersonic vehicles (HSVs). The control performance degradation and loss of stability may be induced if the coupled multisource uncertainties are ignored. To address this problem, this paper develops a novel multivariable robust adaptive sliding mode control structure. Benefiting from the second-order characteristics of the super-twisting controller, the uncertainties from the unknown nonlinearities and the lumped disturbances can be surmounted respectively. Meanwhile, the finite time convergence can be obtained and the continuity of the control signals can be guaranteed. Moreover, to cope with the unknown upper bounds of lumped uncertainties, a novel update law is developed by analyzing the connection between the control gains and the boundaries. Moreover, for the purpose of compensating the mismatched uncertainties in HSVs, a robust disturbance observer is introduced into the sliding surface such that the sliding-mode dynamics are stable with L 2 − L ∞ performance. The closed-loop control system is proved to be globally ultimately stable. Simulation results indicate that the proposed control scheme works well compared with the existing methods.

This paper investigates an adaptive coordinated control problem of spacecraft formation flying (SFF) system subject to uncertain mass and external perturbations. First, a general unified structure is proposed to represent the adjustable performance functions like the exponentially and appointed-time convergent ones. Then, an adaptive coordinated controller is developed based on a two-layer performance envelope to characterize the transient and steady-state formation maneuvering behaviors quantitatively of SFF system. Compared with the existing coordinated control approaches, the prominent advantage of the proposed one is that the preassigned position tracking and consensus tracking performance is guaranteed simultaneously while maintaining the scheduled formation configuration. Meanwhile, the users can configure and adjust the convergence rate (like the exponential or finite-time convergence) of the formation tracking errors by choosing different parameters arbitrarily in the unified structure for the performance functions. Finally, a group of numerical examples are organized to validated the effectiveness of the proposed coordinated control approach.

This work mainly addresses the attitude tracking problem for rigid spacecraft subject to external disturbances, actuator saturation and faults. Firstly, a finite-time prescribed performance function (FTPPF) is defined to limit the tracking errors of original system into the boundary constraints and converge into a small residual set around zero within the preassigned time. An error transformation is employed to transform the constrained control system into an unconstraint one to simplify the relevant controller design. With the aid of the hyperbolic tangent function, the actuator saturation is approximated, and the approximation error is included into the external disturbances to be restrained. Then, a linear extended state observer (LESO) is developed to address the synthetic disturbances mentioned above with fewer tuning parameters. Wherein, the nominal matrix replaces the actual input gains is employed in the LESO design to avoid the singularity in disturbance rejection resulted from saturation approximation. Furthermore, an observer-based fault-tolerant tracking controller is derived to stabilize the attitude tracking error system and realize the disturbance compensation. Compared with the existing methods based on neutral networks (NNs) or fuzzy approximations, the developed scheme can achieve simultaneous estimations of multiple disturbances with simpler design. Meanwhile, the disadvantage that the NNs and fuzzy approximations are only effective on some compact sets is avoided. Finally, numerical simulations and analyses are employed to verify the superiority and robustness of the proposed method.

High-quality control method is of great importance for the attitude determination and maneuvering of spacecraft in the on-orbit servicing technology. Prescribed performance control methodology, as a potential way, has gained considerable attention due to its quantitative description for the transient and steady-state control performance in recent years. Thus, this article constructs a survey for the prescribed performance control methodology along with the detailed analysis of the attitude control methods in the existing reported works. Then, the basic structure of prescribed performance control methodology is discussed in theory, and application to the attitude control of postcapture spacecraft works as an example to further explain the procedure of prescribed performance control methodology. Finally, with consideration of some vital aerospace applications, potential open issues of the prescribed performance control methodology are analyzed.

A robust prescribed performance control (PPC) scheme with practically finite-time stability is proposed for Euler–Lagrange systems with completely unknown dynamics. Firstly, a novel prescribed performance function (PPF) is devised to guarantee the system state to reach its stability region within predefined time. Then, employing the nonsingular terminal sliding mode technique generates an auxiliary manifold, based on which a practically finite-time stable controller is developed without a priori knowledge of the unknown dynamics. Compared with the existing works, the primary contribution is that: not only the prescribed performance is achieved under the proposed PPC scheme, but all state variables are guaranteed to be practically finite-time stable. Finally, three groups of simulations are organized to validate the effectiveness of the proposed scheme.

An event-driven neural network–based fault-tolerant tracking control scheme is investigated for uncertain mechanical systems with performance guaranteed in the presence of unknown actuator faults and external disturbances. Compared with the existing works, the primary advantage is that the detections and identifications of actuator faults are not required, whereas the convergence rate and tracking accuracy can be guaranteed a priori by constructing an adaptive tracking controller with a few aperiodic updates. Moreover, by using the norm-bounding skill, only two adaptive parameters are needed to update online, which dramatically decreases the complexity of the corresponding adaptive schemes. Finally, applications to the attitude stabilization and tracking control of the flexible spacecraft are used to validate the effectiveness of the proposed control scheme.

This paper investigates a novel finite-time saturated deployment control approach for the tethered satellite system in the presence of uncertain dynamics and space perturbations, as well as state constraints. First, an integral Lyapunov function is constructed to remove the hard state constraints characterized by a finite-time convergent performance function. Then, a backstepping finite-time deployment controller is devised via using an extended state observer (ESO) to approach the unknown dynamics, perturbations and saturation deviation. Compared with the existing finite-time control methods, the prominent advantage of the proposed one is that the finite-time saturated deployment control is achieved without violating the state constraints and using fractional state feedback. Finally, an illustrative example is organized to validate the effectiveness of the proposed approach.

A novel control approach is illustrated in this work for uncertain nonlinear systems to guarantee precise tracking behavior with prescribed performance when faced any initial conditions. Wherein, the prescribed performance method is employed so that the output tracking error could attenuate to a vicinity around zero within the appointed-time. Then, a high-gain extended state observer is constructed to acquire the estimations of the unknown system signals. With the aid of observer estimations, a novel output feedback tracking control algorithm is presented, relying upon the backstepping framework. It has been demonstrated that the output tracking error reaches the predesigned performance requirements during the tracking process. Furthermore, the boundedness of all system signals is deduced according to a Lyapunov-based stability synthesis. Finally, applications to a spacecraft attitude system are presented to illustrate the theoretical findings.

This paper investigates an attitude tracking control problem of spacecraft subject to actuator saturation and inertial uncertainties. Aiming at alleviating the negative effects of actuator saturation and parameter uncertainties, an adaptive prescribed performance attitude control scheme is proposed via exploring the reinforcement learning policy. By constructing a critic network and an action network, the approximations of the optimal objective function and the optimal control gains of the spacecraft attitude tracking error system are realized. A group of numerical examples is organized to validate the effectiveness and robustness of the proposed attitude control scheme with respect to actuator saturation and inertia uncertainties.

This paper investigates a novel leader-following attitude control approach for spacecraft formation under the preassigned two-layer performance with consideration of unknown inertial parameters, external disturbance torque, and unmodeled uncertainty. First, two-layer prescribed performance is preselected for both the attitude angular and angular velocity tracking errors. Subsequently, a distributed two-layer performance controller is devised, which can guarantee that all the involved closed-loop signals are uniformly ultimately bounded. In order to tackle the defect of statically two-layer performance controller, learning-based control strategy is introduced to serve as an adaptive supplementary controller based on adaptive dynamic programming technique. This enhances the adaptiveness of the statically two-layer performance controller with respect to unexpected uncertainty dramatically, without any prior knowledge of the inertial information. Furthermore, by employing the robustly positively invariant theory, the input-to-state stability is rigorously proven under the designed learning-based distributed controller. Finally, two groups of simulation examples are organized to validate the feasibility and effectiveness of the proposed distributed control approach.

A novel quasi fixed-time prescribed performance control approach is investigated for mechanical systems with consideration of unknown dynamics and actuator faults. First, a fixed-time stable performance function is developed to quantitatively characterize the transient and steady-state responses. Then, a quasi fixed-time adaptive controller with a pretty simple computational structure is devised based on the developed performance function. Different from the existing finite-time/fixed-time control schemes, the prominent advantages are twofold: (1) no fractional-order state or output information is required, wherein, the order of the states involved in the developed controller just equals to one. (2) the fixed convergence time can be prespecified by the uses instead of relying on several parameters. Finally, applications to a 2-link robotic manipulator are employed to validate the effectiveness of the proposed control approach.

The estimation of the inertia characteristics of a tumbling target before capturing is one of the greatest challenges in on-orbit servicing missions, especially when the time interval between consecutive measurements is large or the rotational speed of the target is very high. In this paper, a two-stage constant state filter (TCSF) is proposed to predict the attitude and estimate the inertia characteristics, including the principal inertia ratios and the principal inertia axes' directions, of a high-speed tumbling target. The man innovation of this paper is to design an Affiliate Filter to estimate the inertia tensor of the target, based on the estimates of angular velocity from an Adaptive Constant State Filter. Compared with the conventional estimation methods like the unscented Kalman filter method, the proposed TCSF shows better stability and accuracy in strongly nonlinear situations, such as that where the time interval between consecutive measurements is 10 s and the angular velocity is more than 14 deg/s. The Monte Carlo simulations have been proposed to demonstrate the performances of the proposed TCSF method.

With consideration of motion control performance and efficient information communication, the synchronization problem on communication connectivity preservation and guaranteed consensus performance for networked mechanical systems has attracted considerable attention in recent years. Different from the existing works, this article investigates a brand-new appointed-time consensus control approach for uncertain networked Euler-Lagrange systems on a directed graph via exploring the prescribed performance control structure. First, a two-layer prescribed performance envelope is formulated via using an appointed-time convergent function for position-related and velocity-related consensus errors, respectively. Then, a simple state-feedback virtual controller with online adaptive performance adjustment is developed to preserve the communication connectivity. Moreover, to guarantee the velocity consensus of the networked systems and improve the position consensus accuracy, an appointed-time adaptive controller is designed by applying the norm inequality to the system uncertainties and external disturbances. Compared to the existing consensus control approaches, the prime advantage of the proposed one is that the constraints generated from the communication ranges are approximated by a time-varying contractive performance envelope, wherein, the appointed-time convergence and steady-state tracking accuracy are preassigned a priori. Meanwhile, no repeated logarithmic error transformations are required in the relevant controller design, which implies that the complexity of the devised control laws has decreased dramatically. Finally, two groups of illustrative examples are organized to validate the effectiveness of the proposed consensus control approach.

In general, the evasion task requires the hypersonic vehicles (HSVs) to quickly complete the attitude maneuver in a short time. Moreover, the rapid variation of the flight modes often induces the structural and parametric uncertainties as well as the highly dynamic disturbances of the HSVs. The peculiar and complex characteristics of the evasion process make it difficult to design the evasion-faced flight control systems. In this work, we investigate the fast adaptive control design problem for the generic HSVs under the evasion task. By introducing several especial nonlinear functional vectors and properly designing the adaptive laws, the high dynamic disturbances and uncertainties can be suppressed. To deal with the completed unknown parts of the structural uncertainties and aerodynamic uncertainties caused by evasion maneuver, two radial basis function neural networks (RBFNNs) are introduced as real-time approximators. Furthermore, to improve the response speed of the flight control system, a super-twisting (STW) algorithm-based predictor is used as a feed-forward term of the controller. Consequently, a novel evasion-faced fast adaptive feed-forward control structure has been established for the HSVs. It has been proven that all the signals of the closed-loop system are bounded with satisfactory tracking velocity. Finally, the simulation experiment has been set up to show the effectiveness and advantages of the proposed control method.

The trajectory tracking control of the quadrotor unmanned aerial vehicle (QUAV) is investigated. The aim of this paper is to develop a novel trajectory tracking control method considering model uncertainties and external disturbances with finite-time stability and prescribed performance of the attitude. Firstly, the trajectory tracking control system of the QUAV is decoupled into two subsystems, i.e., position and attitude subsystems, wherein, backstepping technique is used to design the position tracking controller. Then, to guarantee the tracking performance of the attitude subsystem, a finite-time convergent performance function is developed, and a prescribed performance attitude controller is devised via using integral barrier Lyapunov function thereafter. In the design process of the above two controllers, ESO is used to estimate and compensate multiple uncertainties and disturbances simultaneously. Moreover, the fractional state feedback and discontinuous phenomenon of the control law are directly avoided to achieve the finite-time convergence rate. Finally, an illustrative example is organized to verify the effectiveness of the proposed approach.

This paper investigates a novel semisynchronizing approach for capturing a high-speed tumbling space target with consideration of disturbances and uncertainties. For saving fuel and reducing the re...

This paper investigates an event-triggered neuroadaptive control approach for postcapture flexible spacecraft with guaranteed prespecified tracking performance in the presence of unknown inertial properties, actuator constraints, and external space perturbations. By employing the minimum-learning parameter technique into the neural proportional integral-like controller, only two adaptive parameters are required to update online, which completely avoids the tedious inertial parameter identifications and dramatically reduces the complexity of controller in the meanwhile. Compared with existing works, the primary advantage of the proposed attitude control approach is that the actuator updates are determined by the prescribed event-based conditions in an aperiodic way rather than a periodic one, which greatly reduces the actuator updates. Finally, two groups of illustrative examples are organized to validate the effectiveness of the proposed approach in terms of attitude stabilization and tracking for the postcapture flexible spacecraft.

In this paper, a novel learning-based adaptive attitude takeover control method is investigated for the postcapture space robot-target combination with guaranteed prescribed performance in the presence of unknown inertial properties and external disturbance. First, a new static prescribed performance controller is developed to guarantee that all the involved attitude tracking errors are uniformly ultimately bounded by quantitatively characterizing the transient and steady-state performance of the combination. Then, a learning-based supplementary adaptive strategy based on adaptive dynamic programming is introduced to improve the tracking performance of static controller in terms of robustness and adaptiveness only utilizing the input/output data of the combination. Compared with the existing works, the prominent advantage is that the unknown inertial properties are not required to identify in the development of learning-based adaptive control law, which dramatically decreases the complexity and difficulty of the relevant controller design. Moreover, the transient and steady-state performance is guaranteed a priori by designer-specialized performance functions without resorting to repeated regulations of the controller parameters. Finally, the three groups of illustrative examples are employed to verify the effectiveness of the proposed control method.

This paper focuses on robust adaptive control for a class of uncertain nonlinear systems subject to input saturation and external disturbance with guaranteed predefined tracking performance. To reduce the limitations of classical predefined performance control method in the presence of unknown initial tracking errors, a novel predefined performance function with time-varying design parameters is first proposed. Then, aiming at reducing the complexity of nonlinear approximations, only two least-square-support-vector-machine-based (LS-SVM-based) approximators with two design parameters are required through norm form transformation of the original system. Further, a novel LS-SVM-based adaptive constrained control scheme is developed under the time-vary predefined performance using backstepping technique. Wherein, to avoid the tedious analysis and repeated differentiations of virtual control laws in the backstepping technique, a simple and robust finite-time-convergent differentiator is devised to only extract its first-order derivative at each step in the presence of external disturbance. In this sense, the inherent demerit of backstepping technique–“explosion of terms” brought by the recursive virtual controller design is conquered. Moreover, an auxiliary system is designed to compensate the control saturation. Finally, three groups of numerical simulations are employed to validate the effectiveness of the newly developed differentiator and the proposed adaptive constrained control scheme.

In this paper, the attitude stabilization and tracking control problem is investigated for spacecraft with consideration of unknown dynamics and external disturbance. A low-complexity prescribed performance attitude control scheme is presented to solve this problem. Different from the existing works, there are threefold prominent advantages. The first one is the transient performance and the steady performance of the system is guaranteed by a user-defined function rather than depending on repeated adjustment of controller parameters. The second is that no information of the system and external disturbance is necessary in the developed control scheme, which means the method is model-free. Moreover, the developed low-complexity controller is calculated without any time-consuming iterative operations; thus it's significantly advantageous in engineering applications. It is proved that the state variables converge to the prescribed region at a prescribed exponential rate under the proposed control scheme. Four groups of numerical simulations are organized to validate the effectiveness of the method.

This paper investigates an adaptive model-free constrained prescribed performance control approach for flexible spacecraft with an unknown captured object subject to unknown inertial properties, elastic vibration, actuator saturation, and external disturbance. First, the attitude kinematics and dynamics of the postcapture flexible spacecraft are transformed into a Euler–Lagrange form, based on which a model-free constrained attitude prescribed performance controller comprising a nominal control term and an adaptive compensation control term is developed. Then, by employing norm equalities of the Euler–Lagrange systems, a model-free adaptive scheme is designed to improve the robustness with respect to uncertainty, actuator saturation, and external disturbance just only using the state information. Compared with the existing works, the primary advantage is that the resultant controller and adaptive scheme are computationally very simple without any requirement of unknown inertial information. But the transient and steady-state performance is a priori guaranteed without resorting to repeated regulations of the controller parameters. Finally, the application to attitude stabilization and tracking of postcapture flexible spacecraft along with active vibration suppression is employed to validate the effectiveness of the proposed approach.

In this paper, a low-complexity robust estimation-free decentralized prescribed performance control scheme is proposed and analyzed for nonaffine nonlinear large-scale systems in the presence of unknown nonlinearity and external disturbance. To tackle the high-order dynamics of each tracking error subsystem, a time-varying stable manifold involving the output tracking error and its high-order derivatives is constructed, which is strictly evolved within the envelope of user-specialized prescribed performance. Sequentially, a robust decentralized controller is devised for each manifold, under which the output tracking error and its high-order derivatives are proven to converge asymptotically to a small residual domain with prescribed fast convergence rate. Additionally, no specialized approximation technique, adaptive scheme, and disturbance observer are needed, which alleviates the complexity and difficulty of robust decentralized controller design dramatically. Finally, 3 groups of illustrative examples are used to validate the effectiveness of the proposed low-complexity robust decentralized control scheme for uncertain nonaffine nonlinear large-scale systems.

This paper investigates a low-complexity robust decentralized fault-tolerant prescribed performance control scheme for uncertain larger-scale nonlinear systems with consideration of the unknown nonlinearity, actuator failures, dead-zone input, and external disturbance. Firstly, a new simple finite-time-convergent differentiator is developed to obtain the unmeasurable state variables with arbitrary accuracy. Then, a time-varying sliding manifold involving the output tracking error and its high-order derivatives is constructed to tackle the high-order dynamics of subsystems. Sequentially, a robust decentralized fault-tolerant control scheme is proposed for each sliding manifold with prescribed convergence rate. The prominent advantage of the proposed fault-tolerant control scheme is that any specialized approximation technique, disturbance observer, and recursive procedure of backstepping technique are avoided, which dramatically alleviates the complexity of controller design. Finally, two groups of illustrative examples are employed to demonstrate the effectiveness of the low-complexity decentralized fault-tolerant control scheme under the developed finite-time-convergent differentiator.

Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method. The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids. Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to obtain the true ground data by using the normal vector as a distinctive feature. To improve the performance of noise removal, we propose an elaborate K nearest neighbor (KNN)-based segmentation method via an optimization strategy. Finally, the denoised data of the wall and ground are spliced for further 3D reconstruction. The experimental results show that the proposed method is efficient at noise removal and is superior to several traditional methods in terms of both denoising performance and run speed.

This paper proposes a novel output constrained non-affine control structure by combining backstepping adaptive control with coordinate transformation technique for Generic Hypersonic Vehicles (GHVs) attitude tracking problem suffering from stochastic uncertainties. Firstly, by introducing several specific nonlinear functions, the output constrained control problem is transformed into a stabilization problem of several new variables. Then a novel adaptive control scheme that employee the boundary estimation technique and Nussbaum-type functions to counteract the effect of non-affine aerodynamic characteristics is integrated to address the attitude tracking problem. Meanwhile, two Neural Networks (NNs) are introduced to approximate and compensate the unknown nonlinearities. With the aid of a four-order Lyapunov function, the closed-loop attitude control system is proved to be stochastically stable. As a result, the output tracking errors can be guaranteed to stay in the predefined constraints during the whole control process and finally converge to an acceptable small value. Comparative simulation results are presented to demonstrate the effectiveness and advantages of the proposed control strategy.

Summary This paper presents a systematic method to address the reduction of online computational complexity and infeasibility problem of explicit model predictive control for constrained systems under external disturbance. In feasible state space, in order to avoid the expensive database searching procedure, support vector machine-based approximation is proposed to yield a novel unified explicit optimal control law rather than a piecewise affine one developed by explicit model predictive control. In infeasible state space, through constructing finite maximum control invariant sets around fictitious equilibrium points, a reachable controller is devised to steer the infeasible state asymptotically to the feasible state space without violating the hard constraint. Consequently, global robustness is guaranteed by introducing a minimum robust positively invariant set by means of the tube-based technique, despite the coexistence of external disturbance and training error. Finally, the performance of the presently proposed control law is evaluated through three groups of numerical examples. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, a novel adaptive model-free attitude tracking control method is investigated for rigid spacecraft with consideration of the external disturbance, unknown inertia matrix, and ...

In this paper, a robust inertia-free attitude takeover control scheme with guaranteed prescribed performance is investigated for postcapture combined spacecraft with consideration of unmeasurable states, unknown inertial property and external disturbance torque. Firstly, to estimate the unavailable angular velocity of combination accurately, a novel finite-time-convergent tracking differentiator is developed with a quite computationally achievable structure free from the unknown nonlinear dynamics of combined spacecraft. Then, a robust inertia-free prescribed performance control scheme is proposed, wherein, the transient and steady-state performance of combined spacecraft is first quantitatively studied by stabilizing the filtered attitude tracking errors. Compared with the existing works, the prominent advantage is that no parameter identifications and no neural or fuzzy nonlinear approximations are needed, which decreases the complexity of robust controller design dramatically. Moreover, the prescribed performance of combined spacecraft is guaranteed a priori without resorting to repeated regulations of the controller parameters. Finally, four illustrative examples are employed to validate the effectiveness of the proposed control scheme and tracking differentiator.

This paper investigates a low-complexity prescribed performance control approach for combined spacecraft with an unknown captured object in the presence of external disturbance. First, a prescribed performance function with appropriate parameters is selected for the filtered state variables involving the attitude and angular velocity. Then, a low-complexity robust prescribed performance controller is developed without any priori knowledge of the inertial information of combined spacecraft. This dramatically decreases the complexity of controller design for combined spacecraft with large uncertainty owing to the fact that the tedious identification of inertial property is avoided. Finally, an illustrative example is employed to validate the effectiveness of proposed control approach in terms of stabilizing the combined spacecraft.

This work investigates the attitude tracking control problem of spacecraft under strong external disturbances and parameter uncertainties. A novel appointed-time stable control scheme is proposed with guaranteed transient and steady-state performance. First, an appointed-time reachable performance function (ARPF) is presented, and its reach time can be arbitrarily selected by the users. Then, a double-ARPFs strategy is introduced, that is, by imposing two ARPFs on the attitude and the system output, respectively, all system states will be appointed-time stable. Furthermore, a robust controller with implementable structure is proposed to guarantee the performance functions under strong external disturbances and parameter uncertainties. And the attitude tracking errors as well as the angular velocity errors are proved to be appointed-time stable. Last, three groups of simulations are organized to verify the effectiveness, robustness and appointed-time stability of the proposed control scheme.