Adaptive consensus of multi-agent systems with unknown control coefficients and input saturation

Observer-based adaptive prescribed performance tracking control for nonlinear systems with unknown control direction and input saturation

Safety control using barrier certificates for multiagent systems with input saturation and formation constraints

Practical time-varying formation tracking problems for second-order nonlinear multiagent systems with multiple leaders are investigated using adaptive neural networks (NNs), where the time-varying formation tracking error caused by time-varying external disturbances can be arbitrarily small. Different from the previous work, there exists a predefined time-varying formation formed by the states of the followers and the formation tracks the convex combination of the states of the leaders with unknown control inputs. Besides, the dynamics of each agent has both matched/mismatched heterogeneous nonlinearities and disturbances simultaneously. First, a practical time-varying formation tracking protocol using adaptive NNs is proposed, which is constructed using only local neighboring information. The proposed control protocol can process not only the matched/mismatched heterogeneous nonlinearities and disturbances, but also the unknown control inputs of the leaders. Second, an algorithm with three steps is introduced to design the practical formation tracking protocol, where the parameters of the protocol are determined, and the practical time-varying formation tracking feasibility condition is given. Third, the stability of the closed-loop multiagent system is proven by using the Lyapunov theory. Finally, a simulation example is showed to illustrate the effectiveness of the obtained theoretical results.

This note addresses the adaptive consensus problem of first-order and second-order linearly parameterized multi-agent systems with unknown identical control directions. First, we propose a new Nussbaum-type function based on which a key lemma is established. The lemma plays an important role in analyzing the consensus of the closed-loop multi-agent systems. Second, the Nussbaum-type function is used to design adaptive control laws for first-order and second-order linearly parameterized multi-agent systems so that each agent seeks for the unknown control direction adaptively and cooperatively. Then, under the assumption that the interconnection topology is undirected and connected, it is proved that the first-order and second-order multi-agent systems can achieve consensus by choosing proper design parameters. Two simulation examples are given to illustrate the effectiveness of the proposed control laws.

This paper studies the consensus problem of first-order and second-order multi-agent systems with nonlinear dynamics and quantised interactions. Continuous-time and impulsive control inputs are designed for the multi-agent systems on the logarithmic quantised relative state measurements of agents, respectively. By using nonsmooth analysis tools, we get some sufficient conditions for the consensus of multi-agent systems under the continuous-time inputs. Compared with continuous-time control inputs, impulsive distributed control inputs just use the state variables of the systems at discrete-time instances. Based on impulsive control theory, we prove that the multi-agent systems can reach consensus by choosing proper control gains and impulsive intervals. The simulation results are given to verify the effectiveness of the theoretical results.

This paper focuses on addressing consensus and flocking issues in second-order nonlinear multi-agent systems (MASs) under the false data injection attacks. Novel adaptive control protocols are proposed to effectively mitigate the impact of these attacks by updating adaptive parameters. Based on algebraic graph theory, matrix theory and the Lyapunov approach, various sufficient conditions are established, which show the fixed-time consensus and finite-time flocking can be achieved for the MASs. Moreover, some upper bounds for the settling time are derived in terms of the parameters of the model. Finally, three simulation examples are presented to validate the efficacy of our proposed theoretical results.

Global adaptive neural network control of nonlinear time-varying systems with unknown control coefficients and model uncertainties

The event-based leader-following consensus problem for multi-agent systems with input saturation is studied in this paper. The control input update is event-driven, and the triggering function is linear, under the given trigger function and trigger condition. At first, a sufficient condition of the leader-following consensus for multi-agent systems without input saturation is presented. Furthermore, under the constraint of input saturation and communication time delay, the leader-following consensus control is achieved through designing control protocol and choosing suitable parameters of control input. Lyapunov function in combination with S-procedure method is adopted to prove the consensus stability for multi-agent systems with input saturation and communication time delay. Moreover, the Zeno-behavior is excluded for the triggering time sequence. Finally, simulations are present to illustrate the effectiveness of the proposed consensus control protocols.

Prescribed performance dynamic surface control for trajectory-tracking of unmanned surface vessel with input saturation

Integral sliding-mode fixed-time consensus tracking for second-order non-linear and time delay multi-agent systems

This article deals with the mean square consensus problem for second-order discrete-time multi-agent systems. Both cases of systems with and without time delays in Markov switching topologies are considered. With the introduced control protocols, necessary and sufficient conditions for mean square consensus of second-order multi-agent systems are derived. Under the given control protocols in Markov switching topologies, the second-order multi-agent systems can reach mean square consensus if and only if each union of the graphs corresponding to all the nodes in closed sets has a spanning tree. Finally, a simulation example is provided to illustrate the effectiveness of our theoretical results.

In this paper, a novel event-triggered (ET) adaptive neural tracking control scheme is proposed for uncertain strict-feedback systems subject to asymmetric time-varying full-state constraints, unknown virtual control coefficients and external disturbances. The lower-order time-varying asymmetric barrier Lyapunov function (TVABLF) is constructed to ensure that state constraints are not transgressed. Then, a simple backstepping-based control procedure is developed to remove the existing restrictions on a high power of piecewise TVABLF and high-order differentiability of virtual laws. In particular, a novel adaptive updating law is constructed to co-design the controller and the ET scheme by the combination of the Nussbaum gain technique, thereby solving the ET controller design difficulty resulting from unknown control coefficients. The proposed scheme can guarantee all states within the time-varying constrained areas, achieve the satisfactory tracking ability and save the communication resource. Finally, the effectiveness of the proposed scheme is testified by numerical and practical examples.

This paper studies fixed-time consensus of second-order leader-following multi-agent systems with inherent nonlinear dynamics. Firstly, a new fixed-time consensus protocol is designed to solve the tracking problem of the second-order nonlinear systems. By using Lyapunov theory and graph theory, we choose a different Lyapunov function candidate to verify the theoretical results. Then we consider the second-order multi-agent systems with multiple leaders and propose a new protocol to solve the fixed-time containment control problem. Finally, two numerical examples are proposed to demonstrate the effectiveness of the proposed fixed-time protocols.

In this paper, a robust consensus tracking problem for a class of second-order multi-agent systems has been addressed in the presence of disturbances and unmodeled dynamics. The desired trajectory to be tracked is only provided to a small group of team members. An identifier is developed to estimate the unknown disturbances and unmodeled dynamics. A continuous consensus tracking controller is developed based on this identifier to achieve asymptotic consensus tracking using the local information obtained from neighboring agents. A sufficient condition is derived to ensure consensus tracking and asymptotic stability of the closed-loop system using algebraic graph theory, Lyapunov-based analysis, and an invariance-like theorem. Numerical simulations are provided to demonstrate the effectiveness of the developed robust consensus controller.

In this paper, we study the distributed coordination for second-order nonlinear multi-agent systems with heterogeneous control gains under a general directed graph. The distributed containment problem with multiple dynamic leaders is mainly considered. We first propose control algorithms with heterogeneous constant control gains to ensure all the followers converge to the convex hull spanned by the leaders. It is shown that the constant control gains must be above some positive bounds. We then propose distributed control laws with adaptively updating control gains, which realizes a fully distributed coordination without the need of global information. Furthermore, an adaptive σ-modification scheme is proposed to render smaller control gains and thus requires smaller amplitude on the control input.