Recent advances on filtering and control for nonlinear stochastic complex systems with incomplete information: A survey

This Article is provided by the Brunel Open Access Publishing Fund - Copyright @ 2012 Hindawi PublishingSome recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61104125, 61028008, 61174136, 60974030, and 61074129, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Distributed Adaptive Control for a Class of Heterogeneous Nonlinear Multi-Agent Systems with Nonidentical Dimensions

A novel feedback distributed adaptive control strategy based on radial basis neural network (RBFNN) is proposed for the consensus control of a class of leaderless heterogeneous nonlinear multi-agent systems with the same and different dimensions. The distributed control, which consists of a sequence of comparable matrices or vectors, can make that all the states of each agent to attain consensus dynamic behaviors are defined with similar parameters of each agent with nonidentical dimensions. The coupling weight adaptation laws and the feedback management of neural network weights ensure that all signals in the closed-loop system are uniformly ultimately bounded. Finally, two simulation examples are carried out to validate the effectiveness of the suggested control design strategy

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

Distributed Event-triggered Fault-tolerant Consensus Control of Multi-agent Systems under DoS Attacks

This study investigates the distributed fault-tolerant consensus issue of multi-agent systems subject to complicated abrupt and incipient time-varying actuator faults in physical hierarchy and aperiodic denial-of-service (DoS) attacks in networked hierarchy. Decentralized estimators are devised to estimate consecutive system states and actuator faults. A unified framework with an absolute local output-based closed-loop estimator in decentralized fault estimation design and a relative broadcasting state-based open-loop estimator in distributed event-triggered fault-tolerant consensus design is developed. Criteria of exponential consensus of the faulty multi-agent systems under DoS attacks are derived by virtue of average dwelling time and attack frequency technique. Simulations are outlined to confirm the efficacy of the proposed distributed fault-tolerant consensus control algorithm based on an event-triggered mechanism

COOPERATIVE AND CONSENSUS-BASED CONTROL FOR A TEAM OF MULTI-AGENT SYSTEMS

Cooperative control has attracted a noticeable interest in control systems community due to its numerous applications in areas such as formation flying of unmanned aerial vehicles, cooperative attitude control of spacecraft, rendezvous of mobile robots, unmanned underwater vehicles, traffic control, data network congestion control and routing. Generally, in any cooperative control of multi-agent systems one can find a set of locally sensed information, a communication network with limited bandwidth, a decision making algorithm, and a distributed computational capability. The ultimate goal of cooperative systems is to achieve consensus or synchronization throughout the team members while meeting all communication and computational constraints. The consensus problem involves convergence of outputs or states of all agents to a common value and it is more challenging when the agents are subjected to disturbances, measurement noise, model uncertainties or they are faulty. This dissertation deals with the above mentioned challenges and has developed methods to design distributed cooperative control and fault recovery strategies in multi-agent systems. Towards this end, we first proposed a transformation for Linear Time Invariant (LTI) multi-agent systems that facilitates a systematic control design procedure and make it possible to use powerful Lyapunov stability analysis tool to guarantee its consensus achievement. Moreover, Lyapunov stability analysis techniques for switched systems are investigated and a novel method is introduced which is well suited for designing consensus algorithms for switching topology multi-agent systems. This method also makes it possible to deal with disturbances with limited root mean square (RMS) intensities. In order to decrease controller design complexity, a iii method is presented which uses algebraic connectivity of the communication network to decouple augmented dynamics of the team into lower dimensional parts, which allows one to design the consensus algorithm based on the solution to an algebraic Riccati equation with the same order as that of agent. Although our proposed decoupling method is a powerful approach to reduce the complexity of the controller design, it is possible to apply classical pole placement methods to the transformed dynamics of the team to develop and obtain controller gains. The effects of actuator faults in consensus achievement of multi-agent systems is investigated. We proposed a framework to quantitatively study actuator loss-of-effectiveness effects in multi-agent systems. A fault index is defined based on information on fault severities of agents and communication network topology, and sufficient conditions for consensus achievement of the team are derived. It is shown that the stability of the cooperative controller is linked to the fault index. An optimization problem is formulated to minimize the team fault index that leads to improvements in the performance of the team. A numerical optimization algorithm is used to obtain the solutions to the optimal problem and based on the solutions a fault recovery strategy is proposed for both actuator saturation and loss-of-effectiveness fault types. Finally, to make our proposed methodology more suitable for real life scenarios, the consensus achievement of a multi-agent team in presence of measurement noise and model uncertainties is investigated. Towards this end, first a team of LTI agents with measurement noise is considered and an observer based consensus algorithm is proposed and shown that the team can achieve H∞ output consensus in presence of both bounded RMS disturbance input and measurement noise. In the next step a multi-agent team with both linear and Lipschitz nonlinearity uncertainties is studied and a cooperative control algorithm is developed. An observer based approach is also developed to tackle consensus achievement problem in presence of both measurement noise and model uncertainties

Event-triggered resilient consensus control of multiple unmanned systems against periodic DoS attacks based on state predictor

This paper develops an event-triggered resilient consensus control method for the nonlinear multiple unmanned systems with a data-based autoregressive integrated moving average (ARIMA) agent state prediction mechanism against periodic denial-of-service (DoS) attacks. The state predictor is used to predict the state of neighbor agents during periodic DoS attacks and maintain consistent control of multiple unmanned systems under DoS attacks. Considering the existing prediction error between the actual state and the predicted state, the estimated error is regarded as the uncertainty system disturbance, which is dealt with by the designed disturbance observer. The estimated result is used in the design of the consistent controller to compensate for the system uncertainty error term. Furthermore, this paper investigates dynamic event-triggered consensus controllers to improve resilience and consensus under periodic DoS attacks and reduce the frequency of actuator output changes. It is proved that the Zeno behavior can be excluded. Finally, the resilience and consensus capability of the proposed controller and the superiority of introducing a state predictor are demonstrated through numerical simulations

Fault-tolerant consensus of nonlinear agents considering switching topology in the presence of communication noise

In this paper, the consensus of nonlinear multi-agent systems (MASs) is discussed, considering actuator fault and switching topology in the presence of communication noise. The actuator fault and communication noise are both considered to be random. The switching of the topologies is considered random as well. These issues are handled by Distributed Nonlinear Dynamic Inversion (DNDI), which is designed for Multi-Agent Systems (MASs) operation. The convergence proof with actuator fault is provided, which shows the robustness of the controller. The simulation results show that DNDI successfully dealt with the actuator fault and communication events simultaneously

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

Macroscopic Noisy Bounded Confidence Models with Distributed Radical Opinions

In this article, we study the nonlinear Fokker-Planck (FP) equation that arises as a mean-field (macroscopic) approximation of bounded confidence opinion dynamics, where opinions are influenced by environmental noises and opinions of radicals (stubborn individuals). The distribution of radical opinions serves as an infinite-dimensional exogenous input to the FP equation, visibly influencing the steady opinion profile. We establish mathematical properties of the FP equation. In particular, we (i) show the well-posedness of the dynamic equation, (ii) provide existence result accompanied by a quantitative global estimate for the corresponding stationary solution, and (iii) establish an explicit lower bound on the noise level that guarantees exponential convergence of the dynamics to stationary state. Combining the results in (ii) and (iii) readily yields the input-output stability of the system for sufficiently large noises. Next, using Fourier analysis, the structure of opinion clusters under the uniform initial distribution is examined. Specifically, two numerical schemes for identification of order-disorder transition and characterization of initial clustering behavior are provided. The results of analysis are validated through several numerical simulations of the continuum-agent model (partial differential equation) and the corresponding discrete-agent model (interacting stochastic differential equations) for a particular distribution of radicals