Robust Consensus Tracking Research Articles

Robust Data-Driven Compensation Iterative Learning Consensus Tracking Control for MIMO Multiagent Systems With Random Packet Dropouts

Robust Data-Driven Compensation Iterative Learning Consensus Tracking Control for MIMO Multiagent Systems With Random Packet Dropouts

Neural Network-Based Distributed Consensus Tracking Control for Nonlinear Multi-Agent Systems with Mismatched and Matched Disturbances

In practice, disturbances, including model uncertainties and unknown external disturbances, are always widely present and have a significant impact on the cooperative control performance of a networked multi-agent system. In this work, the distributed consensus tracking control problem for a class of multi-agent systems subject to matched and mismatched uncertainties is addressed. In particular, the dynamics of the leader agent are modeled with uncertain terms, i.e., the leader’s higher-order information, such as velocity and acceleration, is unknown to all followers. To solve this problem, a robust consensus tracking control scheme that combines a neural network-based distributed observer, a barrier function-based disturbance observer, and a tracking controller based on the back-stepping method was developed in this study. Firstly, a neural network-based distributed observer is designed, which is able to achieve effective estimation of leader information by all followers. Secondly, a tracking controller was designed utilizing the back-stepping technique, and the boundedness of the closed-loop error system was proved using the Lyapunov-like theorem, which enables the followers to effectively track the leader’s trajectory. Meanwhile, a barrier function-based disturbance observer is proposed, which achieves the effective estimation of matched and mismatched uncertainties of followers. Finally, the effectiveness of the robust consensus tracking control method designed in this study was verified through numerical simulations.

Robust consensus tracking of one‐sided Lipschitz nonlinear multi‐agent systems under structured uncertainty and time‐varying delay

AbstractThis article delves into the intricate matter of achieving robust consensus tracking of multi‐agent systems with structured uncertainty, one‐sided Lipschitz nonlinearity and time‐varying delay. It is important to note that the time delay is both time‐varying and bounded. To solve the consensus tracking problem of multi‐agent systems, we transform it into the stability problem of an error system. Then, a Lyapunov–Krasovskii functional, incorporating the free‐weighting matrix technique and integral inequality approach, is designed to investigate the stability of the obtained error system. The Leibnitz–Newton formula and its associated relationships among terms are also taken into account. Then, two distributed control strategyies that respectively utilizes relative state and output information of neighboring agents are designed. This approach enables us to find a feasible solution to the consensus tracking problem. Finally, several numerical examples are presented to illustrate the effectiveness of the proposed theoretical results.

Neural network-based robust consensus tracking for uncertain networked Euler-Lagrange systems with time-varying delays and output constraints

This paper mainly focuses on the cooperative robust consensus tracking problem of uncertain networked Euler-Lagrange systems (NELSs) with time-varying delays and output constraints. By systematically integrating the neural network (NN) adaptive technique and the logarithmic type Barrier Lyapunov Function (BLF) in combination with the additional robust control law, two distributed robust consensus schemes for uncertain NELSs are proposed for two cases of time-varying communication and input delays respectively, which can fully guarantee to constrain the output consensus error within a safety region simultaneously. Furthermore, numerical simulation examples are provided to demonstrate the comparable potential advantages of the proposed robust control law over some existing algorithms, including adaptability, stability, and robustness, as well as delay effects.

Robust Consensus Tracking Strategy of Heterogeneous Nonlinear Multi-Agent Systems With Time-Varying Input Delays

Robust Consensus Tracking Strategy of Heterogeneous Nonlinear Multi-Agent Systems With Time-Varying Input Delays

Robust consensus tracking control for switched multiple Lagrangian systems by UDE-based control method

The robust consensus tracking control of the switched multiple Lagrangian systems is investigated by the uncertainty and disturbance estimator (UDE)-based control method. Firstly, the switching control method based on the average dwell time (ADT) algorithm is utilized for parameters and network communication topology switching of multiple Lagrangian systems to reduce the impact of load variation and enhance robustness. Then, in two parts, a proposed adaptive UDE-based control method for multiple Lagrangian systems with lumped uncertainty and load variation. In the initial stage, a consensus tracking control of the nominal switched multiple Lagrangian systems is realized via an adaptive backstepping tracker. The UDE control method is integrated and used in the second stage for the exact adjustment of model uncertainties and disturbances as well as asymptotic estimates. It is worth noting that by designing different filters for UDE, it is possible to handle several specified external disturbances that are unbounded. The unified analysis framework of the closed-loop switched control system is realized through Lyapunov analysis. Therefore, the proposed consensus tracking algorithm has a simple structure and good robustness in handling load changes, model uncertainties, and external disturbances. Finally, illustrative examples with computer simulation are provided to verify the effectiveness and correctness of the obtained results.

Quantised data-driven iterative learning bipartite consensus control for unknown heterogeneous linear MASs with varying trial lengths

This paper aims to realise the robust output bipartite consensus for unknown heterogeneous linear time-varying multiagent systems (MASs) subject to varying trial lengths, unknown measurement disturbances and data quantisation. To this end, inspired by the idea of quantised control, a quantised data-driven adaptive iterative learning bipartite consensus (AILBC) method is proposed. Specifically, to address the problem of varying trial lengths, a distributed auxiliary output prediction system is constructed based on the agents' input-output (I/O) dynamic relationship. An adaptive update protocol is developed to estimate the measurement disturbances and unknown parameters of I/O dynamic relationship. Subsequently, a quantised distributed data-driven iterative learning control (ILC) approach based on the quantised output information is proposed for MASs to achieve robust bipartite consensus tracking, with an attempt to relax the need of explicit model information. The bipartite consensus tracking errors are ultimately bounded through rigorous analysis, and this result is further extended to switching topologies. Finally, numerical simulations are conducted to verify the validity of the AILBC method.

Neural Network-Based Robust Bipartite Consensus Tracking Control of Multi-Agent System with Compound Uncertainties and Actuator Faults

This paper addresses the challenging problem of bipartite consensus tracking of multi-agent systems that are subject to compound uncertainties and actuator faults. Specifically, the study considers a leader agent with fractional-order nonlinear dynamics unknown to the followers. In addition, both cooperative and competitive interactions among agents are taken into account. To tackle these issues, the proposed approach employs a fully distributed robust bipartite consensus tracking controller, which integrates a neural network approximator to estimate the uncertainties of the leader and the followers. The adaptive laws of neural network parameters are continuously updated online based on the bipartite consensus tracking error. Furthermore, an adaptive control technique is utilized to generate the fault-tolerant component to mitigate the partial loss caused by actuator effectiveness faults. Compared with the existing works on nonlinear multi-agent systems, we consider the compound uncertainties, actuator faults and cooperative–competition interactions simultaneously. By implementing the proposed control scheme, the robustness of the closed-loop system can be significantly improved. Finally, numerical simulations are performed to validate the effectiveness of the control scheme.

Robust Flight-Path Angle Consensus Tracking Control for Non-Minimum Phase Unmanned Fixed-Wing Aircraft Formation in the Presence of Measurement Errors

The robust flight-path angle consensus tracking control problem for multiple unmanned fixed-wing aircrafts is investigated in this paper, where the non-minimum phase properties and the presence of measurement errors are systematically addressed. A three-module control scheme is proposed for each aircraft: a Distributed Observer that obtains the available information from the reference system and the neighbor aircraft to provide the estimates of the reference states; a Casual Stable Inversion that calculates the bounded estimates of the desired input, desired external states, and most importantly, desired internal states to resolve the divergence issues caused by the non-minimum phase properties; and a Local Measurement Error Rejection Controller that includes a measurement error estimator (MEE) to actively compensate for the adverse effect of measurement errors to achieve robust consensus tracking control. Stability, convergence, and robustness of the proposed control are analyzed, showing that (1) the non-minimum phase issue can be systematically resolved by the designed Casual Stable Inversion to ensure aircraft internal stability and flight safety, and (2) the consensus tracking accuracy can be improved by tuning a single MEE parameter, which is favorable in practical applications to large-scale unmanned aircraft formations. Comparative simulation results with classic PID-based consensus control demonstrate the advantage of the proposed control in transient oscillations, steady-state tracking accuracy, and robustness against measurement errors.

Finite-Time Adaptive Consensus Tracking Control Based on Barrier Function and Cascaded High-Gain Observer

This paper studies the consensus tracking control for a class of uncertain high-order nonlinear multi-agent systems under an undirected leader-following architecture. A novel distributed finite-time adaptive control framework is proposed based on the barrier function. The distributed cascaded high-gain observers are introduced to solve the problem of robust consensus tracking with unmeasured intermediate states in multi-agent systems based on the proposed control framework. The proposed control schemes guarantee the finite-time consensus of multi-agent systems, which is proven by the finite-time Lyapunov stability and singular perturbation theory. In conclusion, numerical simulations verify the proposed control protocols’ effectiveness, and their performance advantages are shown by comparing them with another existing method.

Robust Consensus Tracking Control of Uncertain Multi-Agent Systems With Local Disturbance Rejection

In this paper, a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph. It is of two-degree-of-freedom nature. Specifically, a robust distributed controller is designed for consensus tracking, while a local disturbance estimator is designed for each agent without requiring the input channel information of disturbances. The condition for asymptotic disturbance rejection is derived. Moreover, even when the disturbance model is not exactly known, the developed method also provides good disturbance-rejection performance. Then, a robust stabilization condition with less conservativeness is derived for the whole multi-agent system. Further, a design algorithm is given. Finally, comparisons with the conventional one-degree-of-freedom-based distributed disturbance-rejection method for mismatched disturbances and the distributed extended-state observer for matched disturbances validate the developed method. Manuscript received May 13, 2022; accepted June 12, 2022. This work was supported by the National Natural Science Foundation of China (62003010, 61873006, 61673053), the Beijing Postdoctoral Research Foundation (Q6041001202001), the Postdoctoral Research Foundation of Chaoyang District (Q1041001202101), and the National Key Research and Development Project (2018YFC1602704, 2018YFB1702704). Recommended by Associate Editor Xiaohua Ge. (Corresponding author: Pan Yu.)

A Nonlinear Observer-Based Approach to Robust Cooperative Tracking for Heterogeneous Spacecraft Attitude Control and Formation Applications

This article proposes a nonlinear observer-based approach to address the robust cooperative tracking problem and application for heterogeneous spacecraft systems. By introducing appropriate assumptions, a weight average estimation is proposed to construct a class of distributed nonlinear observers for heterogeneous spacecraft systems with a target whose dynamics are available only to a few chasers. By synthesizing the observer dynamics among the neighbors, a novel cooperative tracking controller is developed to address nonidentical parameters both in model uncertainties and nonlinear dynamics. Then, the robust consensus tracking problem can be solved if the cooperative behaviors between the chasers and their observers can be achieved synchronically. A special case of the identical model uncertainty and nonlinear dynamics scenario is also discussed. As applications of the proposed nonlinear observer-based approaches, the controller and observer designs of heterogeneous spacecraft attitude control and formation flying systems are revisited. Numerical simulations with a nonlinear dynamic model of spacecraft attitude control and formation flying systems demonstrate the effectiveness of the proposed approaches.

Observer‐based prescribed‐time consensus control for heterogeneous multi‐agent systems under directed graphs

AbstractThis work studies the prescribed‐time robust consensus tracking problem of heterogeneous multi‐agent systems over directed graphs. Aiming at multi‐agent systems with a static leader and only matched disturbances, a distributed observer‐based consensus algorithm is developed, to ensure that the tracking errors converge to zero accurately in prescribed time. For the case of multi‐agent systems with a dynamic leader and mismatched/matched disturbances, an adaptive strategy based on a distributed observer and a disturbance observer is designed by the dynamic damping reciprocal technology. Such strategy can avoid the numerical singularity, adapt to the situation that the upper bounds of the disturbances are unknown, and assure that the tracking errors converge to an adjustable neighborhood of zero within prescribed time. Finally, simulations are given to verify the effectiveness of the proposed control strategies.

Robust consensus tracking based on hABC algorithm with parameters identification for uncertain nonlinear FOMASs with external disturbances

Distributed coordination of multi-agent systems (MASs) has been investigated for many years, and fractional-order calculus has been proved that it can model the dynamics more accurately in certain circumstances. Hence, in this paper, combining the above two aspects, the distributed coordination of fractional-order MASs (FOMASs) is researched, which is a promising topic. Besides, in this paper, the uncertainty, inherent nonlinearity and external disturbances are taken into consideration, aiming at achieving the robust consensus tracking. In particular, the uncertain parameters will be identified from an optimization perspective using artificial bee colony algorithm (ABC). Firstly, to ameliorate the performance of the standard ABC, a hybrid ABC (hABC) incorporating two groups of searching mechanisms is designed, it facilitates the identification of unknown parameters. After obtaining the identified parameters, an efficient distributed nonlinear controller is raised to fulfill the robust consensus tracking. Finally, experiments prove that the designed parameters identification approach can successfully estimate the uncertain parameters with high accuracy, besides the designed control algorithm can robustly control the FOMASs.

Iterative learning control for consensus of second-order multi-agent systems with interval uncertain topologies

Abstract This paper is devoted to the robust consensus tracking problem of second-order nonlinear multi-agent systems (MASs) with the interval uncertain topologies. For the second-order MASs including one leader agent and multiple follower agents, a control protocol is proposed by combining the iterative learning control scheme with the sliding mode control method. By analyzing the convergence of sliding mode variables, the consensus conditions including the unknown eigenvalues and the undetermined weight coefficient are obtained. In order to deal with the difficulties of weight coefficient design caused by the unknown eigenvalues of graphs, a min-max optimization problem is formulated based on the fastest convergence of the λ-norm of sliding mode variables, then the optimal weight coefficient is obtained by solving the min-max optimization problem. Moreover, for the undirected and directed interval uncertain graphs, two algorithms about the optimal weight coefficients are proposed, respectively. Finally, three numerical simulation examples are presented to demonstrate the effectiveness of the proposed methods.

Neural network based adaptive tracking of nonlinear multi-agent system

In this paper, the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered. An adaptive control strategy is proposed to smooth the agent’s trajectory, and the neural network is constructed to estimate the system’s unknown components. The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties. Then, the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’ models. Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control. Finally, the theoretical results are verified by numerical simulations, and a comparative experiment is conducted, showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster.

Distributed trajectory tracking and formation control without velocity measurements by the notion of prior bounded local neighborhood synchronization error

This paper investigates the robust consensus tracking and formation control problems of multiple second-order systems having exogenous disturbances and no velocity measurements. To account for the input saturation constraint in controller design, a novel notion of local neighborhood synchronization error is proposed, which is obtained using generalized saturation functions and can be regarded as a nonlinear variation of the well-known linear local neighborhood synchronization error. An important property of the notion is proved and then a continuous distributed controller is designed using it. To improve the robustness of the controller with respect to exogenous disturbance, a disturbance estimator–based design and a simple parameter mapping for parameter tuning are proposed. The resulting error system is proven to be small-signal [Formula: see text] stable and input-to-output stable. In particular, the synchronization errors and tracking errors converge asymptotically to zero if the disturbances converge to some constants. By the parameter mapping, the steady-state synchronization errors and tracking errors can be made arbitrarily small. The control scheme is finally modified to adapt to formation control applications by adding the desired position deviation from the leader’s trajectory. The performance of the scheme is demonstrated by the simulation results.

Robust distributed consensus tracking control for high‐order uncertain nonlinear MASs with directed topologies

AbstractThis paper addresses the robust distributed consensus tracking problem for leader‐follower multi‐agent systems (MASs) with nonlinear dynamics and external disturbances under directed graph topologies. The nonlinear uncertainties are not required to be continuous or meet the matching conditions. The backstepping design method combined with signal compensation approach is proposed to design robust controllers in order to guarantee the consensus tracking property between the followers and the one leader. Moreover, the dynamic surface control technique is used to deal with the 'explosion of complexity' problem. It is proven that the robust distributed consensus tracking property of the MASs can be guaranteed. Furthermore, the consensus tracking errors can be designed as small as required and the convergence speed can be chosen arbitrarily. Finally, two examples are presented to show the validity of the proposed consensus tracking control protocols.

Robust Fixed-Time Consensus Tracking Control of High-Order Multiple Nonholonomic Systems

Convergence rate is an important performance index in consensus control. Yet, most of the existing results can only achieve asymptotic consensus, or finite-time consensus whose settling time is contingent on initial conditions. This paper studies the robust fixed-time consensus tracking control problem for high-order nonholonomic chained-form systems subject to unknown parameters and nonlinear uncertainties. By using the modified power integrator method, we propose a new robust fixed-time consensus tracking algorithm, with which the consensus tracking can be achieved in a fixed time independent of initial conditions even in the presence of uncertainties. An observer-based fixed-time consensus tracking algorithm is also developed as an alternative method to overcome the communication loop problem. Lyapunov analysis shows the fixed-time stability of the closed-loop system. An application to multiple wheeled mobile robots is presented.

Distributed low-complexity fault-tolerant consensus tracking of switched uncertain nonlinear pure-feedback multi-agent systems under asynchronous switching

This paper addresses a distributed low-complexity design problem for ensuring preassigned fault-tolerant consensus tracking quality of uncertain switched nonlinear pure-feedback multi-agent systems under arbitrary and asynchronous switching. Switched non-affine nonlinearities and unexpected nonlinear and actuator faults of each system are assumed to be unknown. Compared with existing results in the literature, the main contribution of this paper is to provide a simplified robust control strategy to deal with asynchronously switched nonlinearities and faults among systems in the consensus tracking field. Using nonlinearly transformed error surfaces and the common Lyapunov function method, a common robust consensus tracking design strategy is established to ensure the preassigned fault-tolerant consensus tracking performance and the system reliability on the switched faults, without using any adaptive fault compensation techniques based on neural networks or fuzzy logic systems. It is shown that the distributed consensus tracking errors remain within preselected bounds even at switching and fault occurrence instants and finally converge to a preselected neighborhood of the origin.