Consensus Tracking for Multiagent Systems Under Bounded Unknown External Disturbances Using Sliding-PID Control

This paper is devoted to the study of consensus tracking for multiagent systems under unknown but bounded external disturbances. A consensus tracking protocol which is a combination between the conventional PID controller and sliding mode controller named sliding-PID protocol is proposed. The protocol is applied to the consensus tracking of multiagent system under bounded external disturbances where results showed high effectiveness and robustness

Leader-following Consensus Control of a Distributed Linear Multi-agent System using a Sliding Mode Strategy

A distributed leader-following consensus control framework is proposed for a linear system. The linear system is first transformed into a regular form. Then a linear sliding mode is designed to provide high robustness, and the corresponding consensus protocol is proposed in a fully distributed fashion. When matched disturbances are present, it can be demonstrated that the system states reach the sliding mode in finite time and consensus can be achieved asymptotically using Lyapunov theory and the invariant set theorem. Simulation results validate the effectiveness of the proposed algorithm

Data-Driven Architecture to Increase Resilience In Multi-Agent Coordinated Missions

The rise in the use of Multi-Agent Systems (MASs) in unpredictable and changing environments has created the need for intelligent algorithms to increase their autonomy, safety and performance in the event of disturbances and threats. MASs are attractive for their flexibility, which also makes them prone to threats that may result from hardware failures (actuators, sensors, onboard computer, power source) and operational abnormal conditions (weather, GPS denied location, cyber-attacks). This dissertation presents research on a bio-inspired approach for resilience augmentation in MASs in the presence of disturbances and threats such as communication link and stealthy zero-dynamics attacks. An adaptive bio-inspired architecture is developed for distributed consensus algorithms to increase fault-tolerance in a network of multiple high-order nonlinear systems under directed fixed topologies. In similarity with the natural organisms’ ability to recognize and remember specific pathogens to generate its immunity, the immunity-based architecture consists of a Distributed Model-Reference Adaptive Control (DMRAC) with an Artificial Immune System (AIS) adaptation law integrated within a consensus protocol. Feedback linearization is used to modify the high-order nonlinear model into four decoupled linear subsystems. A stability proof of the adaptation law is conducted using Lyapunov methods and Jordan decomposition. The DMRAC is proven to be stable in the presence of external time-varying bounded disturbances and the tracking error trajectories are shown to be bounded. The effectiveness of the proposed architecture is examined through numerical simulations. The proposed controller successfully ensures that consensus is achieved among all agents while the adaptive law v simultaneously rejects the disturbances in the agent and its neighbors. The architecture also includes a health management system to detect faulty agents within the global network. Further numerical simulations successfully test and show that the Global Health Monitoring (GHM) does effectively detect faults within the network

Leader-following Consensus Control of a Distributed Linear Multi-agent System using a Sliding Mode Strategy

A distributed leader-following consensus control framework is proposed for a linear system. The linear system is first transformed into a regular form. Then a linear sliding mode is designed to provide high robustness, and the corresponding consensus protocol is proposed in a fully distributed fashion. When matched disturbances are present, it can be demonstrated that the system states reach the sliding mode in finite time and consensus can be achieved asymptotically using Lyapunov theory and the invariant set theorem. Simulation results validate the effectiveness of the proposed algorithm

Output feedback consensus control for fractional-order nonlinear multi-agent systems with directed topologies

Abstract(#br)This paper is devoted to the output feedback consensus control problem for a class of nonlinear fractional-order multi-agent systems (MASs) with general directed topologies. It is worth noting that the considered fractional-order MASs including the second-order MASs as special cases. By introducing a distributed filter for each agent, a control algorithm uses only relative position measurements is proposed to guarantee the global leaderless consensus can be achieved. Also the derived results are further extended to consensus tracking problem with a leader whose input is unknown and bounded. Finally, two simulation examples are provided to verify the performance of the control design

Active-passive dynamic consensus filters: Theory and applications

”This dissertation presents a new method for distributively sensing dynamic environments utilizing integral action based system theoretic distributed information fusion methods. Specifically, the main contribution is a new class of dynamic consensus filters, termed active-passive dynamic consensus filters, in which agents are considered to be active, if they are able to sense an exogenous quantity of interest and are considered to be passive, otherwise, where the objective is to drive the states of all agents to the convex hull spanned by the exogenous inputs sensed by active agents. Additionally, we generalize these results to allow agents to locally set their value-of-information, characterizing an agents ability to sense a local quantity of interest, which may change with respect to time. The presented active-passive dynamic consensus filters utilize equations of motion in order to diffuse information across the network, requiring continuous information exchange and requiring agents to exchange their measurement and integral action states. Additionally, agents are assumed to be modeled as having single integrator dynamics. Motivated from this standpoint, we utilize the ideas and results from event-triggering control theory to develop a network of agents which only share their measurement state information as required based on errors exceeding a user-defined threshold. We also develop a static output-feedback controller which drives the outputs of a network of agents with general linear time-invariant dynamics to the average of a set of applied exogenous inputs. Finally, we also present a system state emulator based adaptive controller to guarantee that agents will reach a consensus even in the presence of input disturbances. For each proposed active-passive dynamic consensus filter, a rigorous analysis of the closed-loop system dynamics is performed to demonstrate stability. Finally, numerical examples and experimental studies are included to demonstrate the efficacy of the proposed information fusion filters”--Abstract, page iv