Connectivity-Preserving Consensus Tracking of Uncertain Nonlinear Strict-Feedback Multiagent Systems: An Error Transformation Approach

Distributed Adaptive Formation Tracking for a Class of Uncertain Nonlinear Multiagent Systems: Guaranteed Connectivity Under Moving Obstacles

This article explores a guaranteed network connectivity problem during moving obstacle avoidance within a distributed formation tracking framework for uncertain nonlinear multiagent systems with range constraints. We investigate this problem based on a new adaptive distributed design using nonlinear errors and auxiliary signals. Within the detection range, each agent regards other agents and static or dynamic objects as obstacles. The nonlinear error variables for formation tracking and collision avoidance are presented, and the auxiliary signals in formation tracking errors are introduced to maintain network connectivity under the avoidance mechanism. The adaptive formation controllers using command-filtered backstepping are constructed to ensure closed-loop stability with collision avoidance and preserved connectivity. Compared with the previous formation results, the resulting features are as follows: 1) the nonlinear error function for the avoidance mechanism is considered an error variable, and an adaptive tuning mechanism for estimating the dynamic obstacle velocity is derived in a Lyapunov-based control design procedure; 2) network connectivity during dynamic obstacle avoidance is preserved by constructing the auxiliary signals; and 3) owing to neural networks-based compensating variables, the bounding conditions of time derivatives of virtual controllers are not required in the stability analysis.

Fixed-time connectivity-preserving consensus of periodically disturbed nonlinear multi-agent systems with limited communication ranges

This paper concentrates on the fixed-time consensus control problem with preserved connectivity for periodically disturbed nonlinear multi-agent systems with limited communication ranges. The dynamics model of the considered multi-agent systems is general, in which the periodically time-varying disturbance appears in the unknown system function in a nonlinear fashion. The Fourier series expansion-radial basis function neural network-based approximator is incorporated to describe the unknown disturbed functions. In consideration of the limited communication ranges, a unified error transformation is utilized to preserve the initial connectivity. Then, via the backstepping method, a consensus control scheme is recursively constructed to guarantee that the consensus errors fall into a small region around the origin in fixed time, and the connectivity preservation is ensured simultaneously. In simulation part, the consensus trajectories, consensus errors and the results of connectivity preservation are provided. The simulation results demonstrate the capability of the proposed control strategy in achieving connectivity-preserving consensus in fixed time.

Adaptive Output Feedback Fuzzy Fault-Tolerant Control for Nonlinear Full-State-Constrained Switched Systems

In this article, an output feedback adaptive fuzzy tracking control method for a class of switched uncertain nonlinear systems with actuator failures and full-state constraints is proposed under an arbitrary switching signal combining the dynamic surface technique. Since the state variables of the system under study are not measurable, a fuzzy observer is constructed to identify the unmeasured states. The actuator failures are considered in the system. To compensate this failure, a fault-tolerant controller is proposed. Moreover, each state needs to be kept within the constraints, so the tangent Barrier Lyapunov function is selected to solve the full-state constraint problem, and the unknown nonlinear function is approximated by fuzzy-logic systems (FLSs). We also proved that all signals in the closed-loop system are bounded. Furthermore, the states can be kept within the predetermined range even if the actuator fails. Finally, a simulation example is given to verify the effectiveness of the proposed control strategy.

Adaptive-observer-based consensus tracking with fault-tolerant network connectivity of uncertain time-delay nonlinear multiagent systems with actuator and communication faults

In this study, a distributed output-feedback design approach for ensuring fault-tolerant initial network connectivity and preselected-time consensus tracking performance is proposed for a class of uncertain time-delay nonlinear multiagent systems (TDNMSs) with unexpected actuator and communication faults. It is assumed that time-varying state delays and system nonlinearities in TDNMSs are unknown. The main contribution of this study is to provide a delay-independent output-feedback control strategy to address a fault-tolerant initial connectivity preservation problem in the consensus tracking field. A local delay-independent adaptive state observer using neural networks is designed for each follower, and the boundedness of local observation errors is proved by constructing a Lyapunov-Krasovskii functional and adaptive tuning laws. Then, the local nonlinear relative output errors using a time-varying function with a preselected convergence time are derived to design simple local delay-independent trackers. The stability of the proposed consensus tracking system is analyzed, and simulation comparison results demonstrate the validity of the proposed strategy.

Adaptive Appointed-Time Consensus Control of Networked Euler-Lagrange Systems With Connectivity Preservation

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.

Dynamic Learning From Adaptive Neural Control for Discrete-Time Strict-Feedback Systems

This article first investigates the issue on dynamic learning from adaptive neural network (NN) control of discrete-time strict-feedback nonlinear systems. To verify the exponential convergence of estimated NN weights, an extended stability result is presented for a class of discrete-time linear time-varying systems with time delays. Subsequently, by combining the n -step-ahead predictor technology and backstepping, an adaptive NN controller is constructed, which integrates the novel weight updating laws with time delays and without the σ modification. After ensuring the convergence of system output to a recurrent reference signal, the radial basis function (RBF) NN is verified to satisfy the partial persistent excitation condition. By the combination of the extended stability result, the estimated NN weights can be verified to exponentially converge to their ideal values. The convergent weight sequences are comprehensively represented and stored by constructing some elegant learning rules with some novel sequences and the mod function. The stored knowledge is used again to develop a neural learning control scheme. Compared with the traditional adaptive NN control, the proposed scheme can not only accomplish the same or similar tracking tasks but also greatly improve the transient control performance and alleviate the online computation. Finally, the validity of the presented scheme is illustrated by numerical and practical examples.

Distributed Adaptive Fault-Tolerant Time-Varying Formation Control of Unmanned Airships With Limited Communication Ranges Against Input Saturation for Smart City Observation

This article investigates the distributed fault-tolerant time-varying formation control problem for multiple unmanned airships (UAs) against limited communication ranges and input saturation to achieve the safe observation of a smart city. To address the strongly nonlinear functions caused by the time-varying formation flight with limited communication ranges and bias faults, intelligent adaptive learning mechanisms are proposed by incorporating fuzzy neural networks. Moreover, Nussbaum functions are introduced to handle the input saturation and loss-of-effectiveness faults. The distinct features of the proposed control scheme are that time-varying formation flight, actuator faults including bias and loss-of-effectiveness faults, limited communication ranges, and input saturation are simultaneously considered. It is proven by Lyapunov stability analysis that all UAs can achieve a safe formation flight for the smart city observation even in the presence of actuator faults. Hardware-in-the-loop experiments with open-source Pixhawk autopilots are conducted to show the effectiveness of the proposed control scheme.

Predictor-Based Neural Dynamic Surface Control for Bipartite Tracking of a Class of Nonlinear Multiagent Systems

This article is concerned with bipartite tracking for a class of nonlinear multiagent systems under a signed directed graph, where the followers are with unknown virtual control gains. In the predictor-based neural dynamic surface control (NDSC) framework, a bipartite tracking control strategy is proposed by the introduction of predictors and the minimal number of learning parameters (MNLPs) technology along with the graph theory. Different from the traditional NDSC, the predictor-based NDSC utilizes prediction errors to update the neural network for improving system transient performance. The MNLPs technology is employed to avoid the problem of "explosion of learning parameters". It is proved that all closed-loop signals steered by the proposed control strategy are bounded, and the system achieves bipartite consensus. Simulation results verify the efficiency and effectiveness of the strategy.

System Transformation-Based Neural Control for Full-State-Constrained Pure-Feedback Systems via Disturbance Observer

In this article, a novel disturbance observer-based adaptive neural control (ANC) scheme is proposed for full-state-constrained pure-feedback nonlinear systems using a new system transformation method. A nonlinear transformation function in a uniformed design framework is constructed to convert the original states with constrained bounds into the ones without any constraints. By combining an auxiliary first-order filter, an augmented nonlinear system without any state constraint is derived to circumvent the difficulty of the controller design caused by the nonaffine input signal. Based on the augmented nonlinear system, a nonlinear disturbance observer (NDO) is designed to enhance the disturbance rejection ability. Subsequently, the NDO-based ANC scheme is presented by combining the second-order filters with backstepping. The proposed scheme confines all states within the predefined bounds, eliminates the condition on both the known sign and bounds of control gains, improves the robustness of the closed-loop system, and alleviates the computational burden. Two simulation examples are performed to show the validity of the presented scheme.

Cooperative Output Regulation Problem of Nonlinear Multiagent Systems With Proximity Graph via Output Feedback Control

This article considers the cooperative output regulation problem of nonlinear output feedback systems under the communication network modeled by the proximity graph, which is time varying and state dependent. Under the relaxed assumption that the proximity graph is initially connected, based on an improved potential function, we first propose a distributed connectivity-preserving output feedback control law with a linear internal model and distributed observer, which is robust to uncertain parameter and external disturbances in heterogeneous subsystems with strong nonlinearity. Successively, an adaptive design with parameter update law is derived to further tolerate an uncertain parameter in the exosystem, which generates the leader's trajectory and external disturbances.

Distributed Cooperative Learning Control of Uncertain Multiagent Systems With Prescribed Performance and Preserved Connectivity

For an uncertain multiagent system, distributed cooperative learning control exerting the learning capability of the control system in a cooperative way is one of the most important and challenging issues. This article aims to address this issue for an uncertain high-order nonlinear multiagent system with guaranteed transient performance and preserved initial connectivity under an undirected and static communication topology. The considered multiagent system has an identical structure and the uncertain agent dynamics are estimated by localized radial basis function (RBF) neural networks (NNs) in a cooperative way. The NN weight estimates are rigorously proven to converge to small neighborhoods of their common optimal values along the union of all agents' trajectories by a deterministic learning theory. Consequently, the associated uncertain dynamics can be locally accurately identified and can be stored and represented by constant RBF networks. Using the stored knowledge on identified system dynamics, an experience-based distributed controller is proposed to improve the control performance and reduce the computational burden. The theoretical results are demonstrated on an application to the formation control of a group of unmanned surface vehicles.

Adaptive Neural Control for a Class of Nonlinear Multiagent Systems

This article studies the adaptive neural controller design for a class of uncertain multiagent systems described by ordinary differential equations (ODEs) and beams. Three kinds of agent models are considered in this study, i.e., beams, nonlinear ODEs, and coupled ODE and beams. Both beams and ODEs contain completely unknown nonlinearities. Moreover, the control signals are assumed to suffer from a class of generalized backlash nonlinearities. First, neural networks (NNs) are adopted to approximate the completely unknown nonlinearities. New barrier Lyapunov functions are constructed to guarantee the compact set conditions of the NNs. Second, new adaptive neural proportional integral (PI)-type controllers are proposed for the networked ODEs and beams. The parameters of the PI controllers are adaptively tuned by NNs, which can make the system output remain in a prescribed time-varying constraint. Two illustrative examples are presented to demonstrate the advantages of the obtained results.

Neural Learning-Based Fixed-Time Consensus Tracking Control for Nonlinear Multiagent Systems With Directed Communication Networks

This article investigates the problem of fixed-time consensus tracking for nonlinear multiagent systems. Different from the existing studies where the follower systems are linear or pure integrator-type systems, in this article, the follower systems have completely unknown nonlinear functions and time-varying disturbances. Within this framework, a fixed-time observer-based distributed control strategy is proposed to realize the consensus tracking. First, a distributed fixed-time observer is designed for each follower to estimate the leader's state under directed networks. Then, based on the estimate, a fixed-time tracking control protocol is developed where novel approximation and estimation schemes are designed to tackle the nonlinear functions and disturbances. Furthermore, under the proposed control strategy, it is proved that the tracking errors converge into a small set near zero with a fixed-time convergence rate. Finally, the validity of the proposed method is verified by the simulation results.