Consensus disturbance rejection for Lipschitz nonlinear multi-agent systems with input delay: a DOBC approach

In this paper, a new predictor-based consensus disturbance rejection method is proposed for high-order multi agent systems with Lipschitz nonlinearity and input delay. First, a distributed disturbance observer for consensus control is developed for each agent to estimate the disturbance under the delay constraint. Based on the conventional predictor feedback approach, a non-ideal predictor based control scheme is constructed for each agent by utilizing the estimate of the disturbance and the prediction of the relative state information. Then, rigorous analysis is carried out to ensure that the extra terms associated with disturbances and nonlinear functions are properly considered. Sufficient conditions for the consensus of the multi-agent systems with disturbance rejection are derived based on the analysis in the framework of Lyapunov-Krasovskii functionals. A simulation example is included to demonstrate the performance of the proposed control scheme. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.National Natural Science Foundation of China [61673034]SCI(E)ARTICLE1,SI298-31535

Event-triggering architectures for adaptive control of uncertain dynamical systems

In this dissertation, new approaches are presented for the design and implementation of networked adaptive control systems to reduce the wireless network utilization while guaranteeing system stability in the presence of system uncertainties. Specifically, the design and analysis of state feedback adaptive control systems over wireless networks using event-triggering control theory is first presented. The state feedback adaptive control results are then generalized to the output feedback case for dynamical systems with unmeasurable state vectors. This event-triggering approach is then adopted for large-scale uncertain dynamical systems. In particular, decentralized and distributed adaptive control methodologies are proposed with reduced wireless network utilization with stability guarantees. In addition, for systems in the absence of uncertainties, a new observer-free output feedback cooperative control architecture is developed. Specifically, the proposed architecture is predicated on a nonminimal state-space realization that generates an expanded set of states only using the filtered input and filtered output and their derivatives for each vehicle, without the need for designing an observer for each vehicle. Building on the results of this new observer-free output feedback cooperative control architecture, an event-triggering methodology is next proposed for the output feedback cooperative control to schedule the exchanged output measurements information between the agents in order to reduce wireless network utilization. Finally, the output feedback cooperative control architecture is generalized to adaptive control for handling exogenous disturbances in the follower vehicles. For each methodology, the closed-loop system stability properties are rigorously analyzed, the effect of the user-defined event-triggering thresholds and the controller design parameters on the overall system performance are characterized, and Zeno behavior is shown not to occur with the proposed algorithms --Abstract, page iv

Adaptive Fuzzy Tracking Control with Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems

Adaptive fuzzy control strategies are established to achieve global prescribed performance with prescribed-time convergence for strict-feedback systems with mismatched uncertainties and unknown nonlinearities. Firstly, to quantify the transient and steady performance constraints of the tracking error, a class of prescribed-time prescribed performance functions are designed, and a novel error transformation function is introduced to remove the initial value constraints and solve the singularity problem in existing works. Secondly, based on dynamic surface control methods, controllers with or without approximating structures are established to guarantee that the tracking error achieves prescribed transient performance and converges into a prescribed bounded set within prescribed time. In particular, the settling time and initial value of the prescribed performance function are completely independent of initial conditions of the tracking error and system parameters, which improves existing results. Moreover, with a novel Lyapunov-like energy function, not only the differential explosion problem frequently occurring in backstepping techniques is solved, but the drawback of the semi-global boundedness of tracking error induced by dynamic surface control can be overcome. The validity and effectiveness of the main results are verified by numerical simulations on practical examples

Robust Distributed Stabilization of Interconnected Multiagent Systems

Many large-scale systems can be modeled as groups of individual dynamics, e.g., multi-vehicle systems, as well as interconnected multiagent systems, power systems and biological networks as a few examples. Due to the high-dimension and complexity in configuration of these infrastructures, only a few internal variables of each agent might be measurable and the exact knowledge of the model might be unavailable for the control design purpose. The collective objectives may range from consensus to decoupling, stabilization, reference tracking, and global performance guarantees. Depending on the objectives, the designer may choose agent-level low-dimension or multiagent system-level high-dimension approaches to develop distributed algorithms. With an inappropriately designed algorithm, the effect of modeling uncertainty may propagate over the communication and coupling topologies and degrade the overall performance of the system. We address this problem by proposing single- and multi-layer structures. The former is used for both individual and interconnected multiagent systems. The latter, inspired by cyber-physical systems, is devoted to the interconnected multiagent systems. We focus on developing a single control-theoretic tool to be used for the relative information-based distributed control design purpose for any combinations of the aforementioned configuration, objective, and approach. This systematic framework guarantees robust stability and performance of the closed-loop multiagent systems. We validate these theoretical results through various simulation studies

Robust and Cooperative Formation Control of Nonlinear Multi-Agent Systems

Compared with the conventional approach of controlling autonomous systems individually, building up a cooperative multi-agent structure is more robust and efficient for both research and industrial purposes. Among the many subbranches of multiagent systems, formation control has been a popular research direction due to its close connection with complex missions such as spacecraft clustering and intelligent transportation. Hence, this thesis focuses on providing new robust formation control algorithms for first-order, second-order and mixed-order nonlinear multi-agent systems to construct and maintain stable system structure in practical scenarios. System uncertainties and external disturbances are commonly seen factors that could negatively affect the formation tracking precision. Among the many popular tools of uncertainty estimation, the implementation of approaches including neural network adaptive estimation and observer-based approximation are discussed in this thesis. Regarding the neural-based approximation process, different neural network structures including Chebyshev neural network, radial basis function neural network, twolayer artificial neural network and three-layer artificial neural network are tested and implemented. The merits and drawbacks of each network design in the field of control is then analysed. Apart from that, this thesis also offers detailed comparison between the cooperative tuning approach and the observer-based tuning approach regarding the neural network structure to find their corresponding applicable scenarios. To ensure the safety of the formation control algorithms, the issues of obstacle avoidance and inter-agent collision avoidance are both considered. Although the method of constructing artificial potential fields is a popular approach in both the field of path planning and motion control, few have discussed the effect of the inter-agent communication on the collision avoidance scheme. For the obstacle avoiding scenarios, the passive correcting behaviour of individual agent is defined and investigated. A new algorithm is then introduced to modify the reference of individual agents to act as the mitigation. The issue of insufficient information accessibility is then discussed for multi-agent systems with a static and uncompleted communication topology. A distance-based communication topology is proposed to create necessary information exchange channel for unconnected agent pairs that are close enough. The actuator saturation issue is also considered for both first-order multi-agent systems and second-order multi-agent systems to increase the practicality of the formation control schemes. Apart from restricting the amplitudes of the control input, the effect of the input coupling phenomenon is investigated. The oscillation of states brought by the coupled and saturated control input is then summarised as the reverse effect. To attenuate the state oscillation, the methods of developing control input regulation algorithms and employing auxiliary compensator are discussed and validated. The last technical problem to discuss is the hierarchical control scheme. The issue of how to decouple the inter-agent communication and the motion dynamics is discussed for both unified-order and mixed-order multi-agent systems. By using a hierarchical formation control structure, the inter-agent communication process is considered based on a group of virtual agents with ideal characteristics, which can significantly reduce the complexity of the system design. Adaptive hierarchical control schemes are then proposed and validated for both unified-order and mixed-order multi-agent systems through the examples of a multi-drone system and a multiple omni-directional robot system, respectively.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 202

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