Learning-Based Adaptive Attitude Control of Spacecraft Formation With Guaranteed Prescribed Performance

Predefined-Time Output-Feedback Leader-Following Consensus of Pure-Feedback Multiagent Systems

This article delves into the predefined-time output-feedback leader-following consensus problem of uncertain pure-feedback nonlinear multiagent systems for the first time. To streamline subsequent design, the original systems in pure-feedback form are first transformed into canonical systems. Following this, a distributed predefined-time extended state observer (ESO) and a local predefined-time ESO are developed to reconstruct the unknown states/lumped disturbance of the transformed leader system and follower systems, respectively. Based on the estimated states and utilizing a bounded regulation function, two nonsingular and nonconservative predefined-time control laws are formulated to achieve consensus tracking. The proposed method showcases the following advantages: 1) the actual convergence time rather than the upper bound of the convergence time (UBCT) of the tracking errors can be explicitly specified a priori regardless of the initial conditions in a bounded region, optimizing control energy usage and 2) the system overshoot could be effectively reduced by selecting appropriate parameters for the regulation function. Finally, numerical examples are conducted to verify the obtained results.

A comprehensive survey of space robotic manipulators for on-orbit servicing

On-Orbit Servicing (OOS) robots are transforming space exploration by enabling vital maintenance and repair of spacecraft directly in space. However, achieving precise and safe manipulation in microgravity necessitates overcoming significant challenges. This survey delves into four crucial areas essential for successful OOS manipulation: object state estimation, motion planning, and feedback control. Techniques from traditional vision to advanced X-ray and neural network methods are explored for object state estimation. Strategies for fuel-optimized trajectories, docking maneuvers, and collision avoidance are examined in motion planning. The survey also explores control methods for various scenarios, including cooperative manipulation and handling uncertainties, in feedback control. Additionally, this survey examines how Machine learning techniques can further propel OOS robots towards more complex and delicate tasks in space.

Reinforcement Learning-Based Fractional-Order Adaptive Fault-Tolerant Formation Control of Networked Fixed-Wing UAVs With Prescribed Performance

This article investigates the fault-tolerant formation control (FTFC) problem for networked fixed-wing unmanned aerial vehicles (UAVs) against faults. To constrain the distributed tracking errors of follower UAVs with respect to neighboring UAVs in the presence of faults, finite-time prescribed performance functions (PPFs) are developed to transform the distributed tracking errors into a new set of errors by incorporating user-specified transient and steady-state requirements. Then, the critic neural networks (NNs) are developed to learn the long-term performance indices, which are used to evaluate the distributed tracking performance. Based on the generated critic NNs, actor NNs are designed to learn the unknown nonlinear terms. Moreover, to compensate for the reinforcement learning errors of actor-critic NNs, nonlinear disturbance observers (DOs) with skillfully constructed auxiliary learning errors are developed to facilitate the FTFC design. Furthermore, by using the Lyapunov stability analysis, it is shown that all follower UAVs can track the leader UAV with predesigned offsets, and the distributed tracking errors are finite-time convergent. Finally, comparative simulation results are presented to show the effectiveness of the proposed control scheme.

Neuroadaptive Fault-Tolerant Control With Guaranteed Performance for Euler-Lagrange Systems Under Dying Power Faults

This article investigates the tracking control problem for Euler-Lagrange (EL) systems subject to output constraints and extreme actuation/propulsion failures. The goal here is to design a neural network (NN)-based controller capable of guaranteeing satisfactory tracking control performance even if some of the actuators completely fail to work. This is achieved by introducing a novel fault function and rate function such that, with which the original tracking control problem is converted into a stabilization one. It is shown that the tracking error is ensured to converge to a pre-specified compact set within a given finite time and the decay rate of the tracking error can be user-designed in advance. The extreme actuation faults and the standby actuator handover time delay are explicitly addressed, and the closed signals are ensured to be globally uniformly ultimately bounded. The effectiveness of the proposed method has been confirmed through both theoretical analysis and numerical simulation.

Reduced-Order Observer-Based Output-Feedback Tracking Control for Nonlinear Time-Delay Systems With Global Prescribed Performance

In this article, the output-feedback tracking control problem is considered for a class of nonlinear time-delay systems in a strict-feedback form. Based on a state observer with reduced order, a novel output-feedback control scheme is proposed using the backstepping approach, which is able to guarantee the system transient and steady-state performance within a prescribed region. Different from existing works on prescribed performance control (PPC), the present method can relax the restriction that the initial value must be given within a predefined region, say, PPC semiglobally. In the case that the upper bound functions for nonlinear time-delay functions are unknown, based on the approximate capacity of fuzzy-logic systems, an adaptive fuzzy approximation control strategy is proposed. When the upper bound functions are known in prior, or in a product form with unknown parameters and known functions, an output-feedback tracking controller is designed, under which the closed-loop signals are globally ultimately uniformly bounded, and tracking control with global prescribed performance can be achieved. Simulation results are given to substantiate our method.

Adaptive neural network prescribed performance control for dual switching nonlinear time-delay system

This paper investigates the adaptive neural network prescribed performance control problem for a class of dual switching nonlinear systems with time-delay. By using the approximation of neural networks (NNs), an adaptive controller is designed to achieve tracking performance. Another research point of this paper is tracking performance constraints which can solve the performance degradation in practical systems. Therefore, an adaptive NNs output feedback tracking scheme is studied by combining the prescribed performance control (PPC) and backstepping method. With the designed controller and the switching rule, all signals of the closed-loop system are bounded, and the tracking performance satisfies the prescribed performance.

Adaptive predefined-time prescribed performance control for spacecraft systems

The high-accuracy attitude maneuvering problem for spacecraft systems is investigated. A prescribed performance function and a shifting function are first employed to ensure the predefined-time stability of attitude errors and eliminate the constraints on tracking errors at the incipient stage. Subsequently, a novel predefined-time control scheme is developed by combining prescribed performance control and backstepping control procedures. Radial basis function neural network and minimum learning parameter techniques are introduced to model the function of lumped uncertainty including inertial uncertainties, actuator faults and virtual control law derivatives. According to the rigorous stability analysis, the preset tracking precision can be achieved within a predefined time and the fixed-time boundedness of all closed-loop signals is established. Finally, the efficacy of the propounded control scheme is manifested through numerical simulation results.

Saturation-Tolerant Prescribed Control for a Class of MIMO Nonlinear Systems

This article proposes a saturation-tolerant prescribed control (SPC) for a class of multiinput and multioutput (MIMO) nonlinear systems simultaneously considering user-specified performance, unmeasurable system states, and actuator faults. To simplify the control design and decrease the conservatism, tunnel prescribed performance (TPP) is proposed not only with concise form but also smaller overshoot performance. By introducing non-negative modified signals into TPP as saturation-tolerant prescribed performance (SPP), we propose SPC to guarantee tracking errors not to violate SPP constraints despite the existence of saturation and actuator faults. Namely, SPP possesses the ability of enlarging or recovering the performance boundaries flexibly when saturations occur or disappear with the help of these non-negative signals. A novel auxiliary system is then constructed for these signals, which bridges the associations between input saturation errors and performance constraints. Considering nonlinearities and uncertainties in systems, a fuzzy state observer is utilized to approximate the unmeasurable system states under saturations and unknown actuator faults. Dynamic surface control is employed to avoid tedious computations incurred by the backstepping procedures. Furthermore, the closed-loop state errors are guaranteed to a small neighborhood around the equilibrium in finite time and evolved within SPP constraints although input saturations and actuator faults occur. Finally, comparative simulations are presented to demonstrate the feasibility and effectiveness of the proposed control scheme.

Event-Driven Connectivity-Preserving Coordinated Control for Multiple Spacecraft Systems With a Distance-Dependent Dynamic Graph

This article considers the connectivity preservation coordinated control problem for multiple spacecraft systems subject to limited communication resources and sensing capability. By constructing a novel bump function, a distance-dependent dynamic communication network model is first presented, which characterizes the interaction strength as a nonlinear smooth function varying with the relative distance of spacecraft continuously. Subsequently, based on an edge-tension potential function, a distributed event-driven coordinated control scheme is proposed to achieve formation consensus, while ensuring that adjacent spacecraft is always within the allowable connectivity range. Meanwhile, to avoid redundant data transmissions, a hybrid dynamic event-triggered mechanism with maximum triggering interval is developed to schedule the communication frequency among spacecraft. It is proven that the onboard communication resources occupation can be reduced significantly and the Zeno phenomenon is strictly excluded. Finally, the efficiency of the proposed method for, as an example, four-spacecraft formation system is substantiated.

Truly Distributed Finite-Time Attitude Formation-Containment Control for Networked Uncertain Rigid Spacecraft

This article addresses the finite-time attitude formation-containment control problem for networked uncertain rigid spacecraft under directed topology. A unified distributed finite-time attitude control framework, based on the sliding-mode control (SMC) principle, is developed. Different from the current state of the art, the proposed attitude control method is suitable for not only the leader spacecraft but also the follower spacecraft, and only the neighbor state information among spacecraft is required, allowing the resulting control scheme to be truly distributed. Furthermore, the proposed method is inherently continuous, which eliminates the undesired chattering problem. Such features are deemed favorable in practical spacecraft applications. In addition, upon using the proposed neuro-adaptive control technique, the attitude formation-containment deployment can be achieved in finite time with sufficient accuracy, despite the involvement of both the uncertain inertia matrices and external disturbances. The effectiveness of the developed control scheme is confirmed by numerical simulations.

Neural-Networks-Based Prescribed Tracking for Nonaffine Switched Nonlinear Time-Delay Systems

In this article, by using the neural-networks (NNs) separation and approximation technique, an adaptive scheme is presented to deliver the prescribed tracking performance for a class of unknown nonaffine switched nonlinear time-delay systems. The nonaffine terms are indifferentiable and the controllability condition is not required for each subsystem, which allows the considered tracking problem to not be efficiently solved by the traditional adaptive control algorithms. To solve the problem, NNs are utilized to separate and approximate the nonaffine functions, and then the dynamic surface control and convex combination method are utilized to construct a controller and a switching strategy. In addition, an adaptive law is considered for each subsystem to reduce the conservativeness. Under the designed controller and switching strategy, all the signals of the resulting closed-loop system are bounded, and the tracking performance is achieved with a prescribed level.

Planar Affine Formation Stabilization via Parameter Estimations

In this article, we study the problem of affine formation stabilization for multiagent systems in the plane. The challenges lie in the limited access to the information of the target formation in the sense that the prescribed values of the formation parameters, that is, the scaling size and rotation angle, are known only by one agent which we call the leader. Motivated by the fact that three agents (say, leaders) can determine the shape of a planar triangular formation using the stress matrix, we propose a class of estimators to guarantee that two agents in the leader set can gain access to the formation parameters. Then, an integrated control scheme is designed such that the target formation can be uniquely stabilized among all its affine transformations. The sufficient condition ensuring the stability of the closed-loop system is also given based on the cyclic-small-gain theorem. Simulations and experiments are carried out to show the effectiveness of the proposed control strategy.

Reliable Control for Flexible Spacecraft Systems With Aperiodic Sampling and Stochastic Actuator Failures

This article addresses the aperiodic sampled-data control problem for flexible spacecraft with stochastic actuator failures. Flexible spacecraft dynamics are approximated by a group of T-S fuzzy models due to strong nonlinearity, and the multi-stochastic failures of spacecraft are depicted by a time-continuous and state-discrete Markov chain. To reduce the design conservativeness, a membership-sampling-dependent Lyapunov-Krasovskii functional (MSDLKF) is introduced to utilize the information of fuzzy membership functions and aperiodic sampling modes. Furthermore, a number of reliable fuzzy controllers are designed to obtain the exponential attitude stabilization under the circumstances of stochastic failures. At the same time, disturbance attenuation is ensured. The solution of the fuzzy controller gains can be obtained by solving a set of linear matrix inequalities (LMIs). In the end, an example of the practical flexible spacecraft system is given to illustrate the feasibility and validity of the proposed fuzzy control methods.

Adaptive Dynamic Programming for Model-Free Global Stabilization of Control Constrained Continuous-Time Systems

This article addresses the problem of global stabilization of continuous-time linear systems subject to control constraints using a model-free approach. We propose a gain-scheduled low-gain feedback scheme that prevents saturation from occurring and achieves global stabilization. The framework of parameterized algebraic Riccati equations (AREs) is employed to design the low-gain feedback control laws. An adaptive dynamic programming (ADP) method is presented to find the solution of the parameterized ARE without requiring the knowledge of the system dynamics. In particular, we present an iterative ADP algorithm that searches for an appropriate value of the low-gain parameter and iteratively solves the parameterized ADP Bellman equation. We present both state feedback and output feedback algorithms. The closed-loop stability and the convergence of the algorithm to the nominal solution of the parameterized ARE are shown. The simulation results validate the effectiveness of the proposed scheme.

Adaptive Sliding Mode Attitude-Tracking Control of Spacecraft with Prescribed Time Performance

In this article, a novel finite-time attitude-tracking control scheme is proposed by using the prescribed performance control (PPC) method for the spacecraft system under the external disturbance and an uncertain inertia matrix. First, a novel polynomial finite-time performance function (FTPF) was used to avoid the complex calculation of exponential function from conventional FTPF. Then, a simpler error transformation was introduced to guarantee that the attitude-tracking error converges to a preselected region in prescribed time. Subsequently, a robust adaptive controller was proposed by using the backstepping method and the sliding mode control (SMC) technique. Unlike the existing attitude-tracking control results, the proposed PPC scheme guarantees the performance of spacecraft system under the static and transient conditions. Meanwhile, the state trajectory of system can be completely drawn into the designed sliding surface. The stability of the control scheme is proven rigorously by the Lyapunov’s theory of stability. Finally, the simulations show that the convergence rate and the convergence accuracy are better for the tracking errors of spacecraft system under the proposed control scheme.

Prescribed performance tracking control for a class of nonlinear system considering input and state constraints

This article develops a new anti-saturation tracking approach for effectively controlling a type of state-constrained systems under actuator failure. To construct a feedback control loop possessed the predetermined indexes, an auxiliary variable incorporated with a performance guider is first introduced into the design process. Then, a robust fault tolerant control law with the variable-gains is devised to guarantee that the tracking errors can be suppressed in the specified range after a predetermined time. In order to dispose of the input constraint problem, an anti-saturation algorithm is designed without compromising the prescribed capability indexes in control process, it is shown that the proposed feedback control loop can efficiently fulfill the fast and accurate requirement of the constraint tasks. Finally, computer simulation related with robot manipulator is taken to evaluate the validity of designed method.

Fixed-Time Leader-Follower Consensus of Networked Nonlinear Systems via Event/Self-Triggered Control

This brief addresses the fixed-time event/self-triggered leader-follower consensus problems for networked multi-agent systems subject to nonlinear dynamics. First, we present an event-triggered control strategy to achieve the fixed-time consensus, and a new measurement error is designed to avoid Zeno behavior. Then, two new self-triggered control strategies are presented to avoid continuous triggering condition monitoring. Moreover, under the proposed self-triggered control strategies, a strictly positive minimal triggering interval of each follower is given to exclude Zeno behavior. Compared with the existing fixed-time event-triggered results, we propose two new self-triggered control strategies, and the nonlinear term is more general. Finally, the performances of the consensus tracking algorithms are illustrated by a simulation example.