Search

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.

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.

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 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.

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...