Narrow Results

In this paper, we consider a nonlinear control problem for one-dimensional viscous Burgers’ equation associated with a controlled linear heat equation by means of the Hopf–Cole transformation. The control is carried out by the time-dependent intensity of a distributed heat source influencing the heat equation. The set of admissible controls consists of compactly supported $L^{\infty }$ functions. Using the Green’s function approach, we analyze the possibilities of exact and approximate establishment of a given terminal state for the associated nonlinear Burgers’ equation within a desired amount of time. It is shown that the exact controllability of the associated Burgers’ equation and the heat equation are equivalent. Furthermore, sufficient conditions for the approximate controllability are derived. The set of resolving controls is constructed in both cases. The determination of the resolving controls providing exact controllability is reduced to an infinite-dimensional system of linear algebraic equations. By means of the heuristic method of resolving control determination, parametric hierarchies of solutions providing approximate controllability are constructed. The results of a numerical simulation supporting the theoretical derivations are discussed.

This work explores a distributed problem solving (DPS) approach, namely the AM/AG (Amplification/Aggregation) model. The AM/AG model is a hierarchic social system metaphor for DPS based on Mintzberg’s model of organizations. At the core of the model are information flow mechanisms, namely, amplification and aggregation. Amplification is a process of decomposing a given task, called an agenda, into a set of subtasks with magnified degree of specificity and distributing them to multiple processing units downward in the hierarchy. Aggregation is a process of combining the results reported from multiple processing units into a unified view, called a resolution, and promoting the conclusion upward in the hierarchy.

Amplification is discussed in detail. A set of generative rules is introduced. Each rule specifies a set of actions for transforming an input agenda into other forms with higher specificity. The proposed model can be used to account for the memory recall process which makes associations between vast amounts of related concepts, sorts out the combined results, and promotes the most plausible ones. An example of memory recall is used to illustrate the model.

Intelligent and Cooperative Information Systems (ICIS) will have large numbers of distributed, heterogeneous agents interacting and cooperating to solve problems regardless of location, original mission, or platform. The agents in an ICIS will adapt to new and possibly surprising situations, preferably without human intervention. These systems will not only control a domain, but also will improve their own performance over time, that is, they will learn.

This paper describes five heterogeneous learning agents and how they are integrated into an Integrated Learning System (ILS) where some of the agents cooperate to improve performance. The issues involve coordinating distributed, cooperating, heterogeneous problem-solvers, combining various learning paradigms, and integrating different reasoning techniques. ILS also includes a central controller, called The Learning Coordinator (TLC), that manages the control of flow and communication among the agents, using a high-level communication protocol. In order to demonstrate the generality of the ILS architecture, we implemented an application which, through its own experience, learns how to control the traffic in a telephone network, and show the results for one set of experiments. Options for enhancements of the ILS architecture are also discussed.

Energy Management Systems have become an imperative aspect of smart grids, owing to the enormous challenges imposed due to real-time pricing, distributed generation and integration of intermittent renewables. Due to the uncertainty associated with renewable sources and prominent fluctuations in the load demand, it is extremely important to maintain the overall energy balance in such grids. In this paper, the distributed energy management is achieved using a Multi-agent System which provides a flexible and reliable solution to control and manage smart grids. Adaptive fuzzy systems are designed to instill intelligent decision making capability in the agents of multi-agent system. When renewable sources are inadequate, the sustainability of the system is not guaranteed and multi-agent system is capable of deciding the mode of operation such that the system reliability and performance is not compromised. The proposed algorithm maintains power balance in the system and also sustains desired values for the State of Charge of storage units in order to guarantee extended battery life. The Energy management system also implements a cost optimization algorithm based on the Particle Swarm Optimization technique, to minimize operating costs and maximize profits earned by the grid. The proposed energy management algorithm is tested and validated on a practical test system which inherits most of the features of a small-scale smart grid.

We consider the problem of designing (perhaps massively distributed) collectives of computational processes to maximize a provided "world utility" function. We consider this problem when the behavior of each process in the collective can be cast as striving to maximize its own payoff utility function. For such cases the central design issue is how to initialize/update those payoff utility functions of the individual processes so as to induce behavior of the entire collective having good values of the world utility. Traditional "team game" approaches to this problem simply assign to each process the world utility as its payoff utility function. In previous work we used the "Collective Intelligence" (COIN) framework to derive a better choice of payoff utility functions, one that results in world utility performance up to orders of magnitude superior to that ensuing from the use of the team game utility. In this paper, we extend these results using a novel mathematical framework. Under that new framework we review the derivation of the general class of payoff utility functions that both (i) are easy for the individual processes to try to maximize, and (ii) have the property that if good values of them are achieved, then we are assured a high value of world utility. These are the "Aristocrat Utility" and a new variant of the "Wonderful Life Utility" that was introduced in the previous COIN work. We demonstrate experimentally that using these new utility functions can result in significantly improved performance over that of previously investigated COIN payoff utilities, over and above those previous utilities' superiority to the conventional team game utility. These results also illustrate the substantial superiority of these payoff functions to perhaps the most natural version of the economics technique of "endogenizing externalities."

Recent work has shown how information theory extends conventional full-rationality game theory to allow bounded rational agents. The associated mathematical framework can be used to solve distributed optimization and control problems. This is done by translating the distributed problem into an iterated game, where each agent's mixed strategy (i.e. its stochastically determined move) sets a different variable of the problem. So the expected value of the objective function of the distributed problem is determined by the joint probability distribution across the moves of the agents. The mixed strategies of the agents are updated from one game iteration to the next so as to converge on a joint distribution that optimizes that expected value of the objective function. Here, a set of new techniques for this updating is presented. These and older techniques are then extended to apply to uncountable move spaces. We also present an extension of the approach to include (in)equality constraints over the underlying variables. Another contribution is that we show how to extend the Monte Carlo version of the approach to cases where some agents have no Monte Carlo samples for some of their moves, and derive an "automatic annealing schedule".

This paper, proposes a complex Laplacian-based distributed control scheme for convergence in the multi-agent network. The proposed scheme has been designated as cascade formulation. The proposed technique exploits the traditional method of organizing large scattered networks into smaller interconnected clusters to optimize information flow within the network. The complex Laplacian-based approach results in a hierarchical structure, with the formation of a meta-cluster leading other clusters in the network. The proposed formulation enables flexibility to constrain the eigenspectra of the overall closed-loop dynamics, ensuring desired convergence rate and control input intensity. The sufficient conditions ensuring globally stable formation for the proposed formulation are also asserted. Robustness of the proposed formulation to uncertainties like loss in communication links and actuator failure have also been discussed. The effectiveness of the proposed approach is illustrated by simulating a finitely large network of 30 vehicles.

A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This paper describes a unique method to control network communications through distributed task allocation to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. This work builds upon a distributed algorithm previously developed by the authors, CBBA with Relays, which uses task assignment information, including task location and proposed execution time, to predict the network topology and plan support using relays. By explicitly coupling task assignment and relay creation processes, the team is able to optimize the use of agents to address the needs of dynamic complex missions. In this work, the algorithm is extended to explicitly consider realistic network communication dynamics, including path loss, stochastic fading, and information routing. Simulation and flight test results validate the proposed approach, demonstrating that the algorithm ensures both data-rate and interconnectivity bit-error-rate requirements during task execution.

In this paper, distributed optimization control for a group of autonomous Lagrangian systems is studied to achieve an optimization task with local cost functions. To solve the problem, two continuous-time distributed optimization algorithms are designed for multiple heterogeneous Lagrangian agents with uncertain parameters. The proposed algorithms are proved to be effective for those heterogeneous nonlinear agents to achieve the optimization solution in the semi-global sense, even with the exponential convergence rate. Moreover, simulation adequately illustrates the effectiveness of our optimization algorithms.

This paper studies the distributed nonlinear control of multi-agent systems with switching topologies for output agreement. A novel cyclic-small-gain approach is proposed. The crucial idea is to introduce a new dynamic mechanism to process the exchanged information between the agents, and to transform the distributed control problem into a stabilization problem for a dynamic network composed of input-to-output stable (IOS) subsystems. The desired distributed controller is designed based on IOS and cyclic-small-gain techniques. More interestingly, it is shown that the proposed method can be extended to distributed control design in the presence of disturbances.

A quantum network may be realized by the entanglement of particles communicated by qubits between quantum computers, where the entangled photons of light are transferred for communication purposes. This technology has been proven to be feasible experimentally through free-space distribution of entangled photon pairs. Sending photons of light through nonlinear crystals produces correlated photon pairs, by splitting each photon into two half particles with each particle having the same level of energy, which results in entangled pairs. This entanglement is represented by photons, having both either horizontal or vertical polarization. This paper investigates collaborative robotic tasks of unmanned systems in a network where the agents are entangled. For instance, a leader robot sends two identical photons (e.g. with vertical polarization) to two follower robots/autonomous vehicles to communicate information about various tasks such as swarm, formation, trajectory tracking, path following and collaborative tasks. The potential advantages of quantum cooperation of robotic agents is the speed of the process, the ability to achieve security with immunity against cyberattacks, and fault tolerance, through entanglement. If a Quantum Network is implemented in a robotic application, it would present an effective solution; for example, for a group of unmanned systems working securely together. An analytical basis of such systems is investigated in this paper, and the formulation of quantum cooperation of unmanned systems is presented and discussed. The concept of experimental quantum entanglement, as well as quantum cryptography (QC), for robotics applications is presented.

This paper proposes a low complexity distributed multi-agent coordination algorithm for agents to reach their target positions in dense traffic under limited communication. Each single-integrator agent is limited to communicating with only one other agent at a time in consideration of limited bandwidth. We adapt the Velocity Obstacle collision avoidance method from literature to the limited communication problem by incorporating Voronoi Cells and repulsion in our hybrid algorithm. We also introduce a priority system for distributed coordination to avoid deadlocks and livelocks by having agent pairs make mutual decisions based on each agent’s conditional priority. An event trigger-based communication protocol is designed to determine when and to whom to communicate. Our method’s effectiveness is demonstrated in simulations including 100 randomized scenarios of 50 agents. The simulations show that our proposed algorithm enables agents to reach their assigned target positions without deadlock and collision while requiring an average communication rate that is significantly lower than the control frequency.

In this paper, we study the leader-following formation tracking problem for multiple quadrotor helicopters via the distributed observer approach. In contrast with existing results in the literature, our approach offers the following features. First, our results apply to jointly connected switching communication networks, which are more general than static communication networks. Second, our control law is fully distributed in the sense that we do not assume that every vehicle can access the information of the desired formation trajectory. Third, with the virtual leader system being modeled by an exosystem, our control law can accomplish the formation tracking for a large class of leader’s trajectories. Two numerical examples are used to illustrate our design.

Multi-agent formation control is an important part of distributed perception and cooperation, which is convenient to complete various complex tasks and would be a key research direction in the future. This paper reviews the corresponding problems of formation control and the existing centralized and distributed formation control strategies. In particular, we discuss four types of distributed formation control methods based on position and displacement in the global coordinate system and distance and bearing in the nonglobal coordinate system, respectively. Moreover, this paper analyzes affine formation which does not require the global coordinate system. Combined with the current practical applications of multi-agent systems, the latest research for the formation control of the unmanned aerial vehicle (UAV), unmanned ground vehicle (UGV), unmanned surface vehicle (USV) and autonomous underwater vehicle (AUV) is given. Finally, the challenges and opportunities in this burgeoning field are discussed.

Collaborative coverage for target search using a group of unmanned aerial vehicles (UAVs) has received increasing attention in recent years. However, the design of distributed control strategy and coordination mechanisms remains a challenge. This paper presents a distributed and online heuristic strategy to solve the problem of multi-UAV collaborative coverage. As a basis, each UAV maintains a probability grid map in the form of a locally stored matrix, without shared memory. Then we design two evaluation functions and related technical strategies to enable UAVs to make state transfer or area transfer decisions in an online self-organizing way. The simulation results show that the algorithm integrates geometric features such as parallel search and internal spiral search, and is not interfered by factors such as sudden failure of UAVs, changes in detection range, and target movement. Compared with other commonly used methods for target search, our strategy has high search efficiency, good robustness, and fault tolerance.

In this paper, a novel formation control strategy is proposed to address the target tracking and circumnavigating problem of multi-UAV formation. First, two sets of definitions, space angle definition and space vector definition, are presented in order to describe the flight state and construct the desired relative velocity. Then, the relative kinematic model between the UAV and the moving target is established. The distributed control law is constructed by using dynamic feedback linearization so as to realize the tracking and circumnavigating control with the desired velocity, circling radius and relative angular spacing. Next, the exponential stability of the closed-loop system is further guaranteed by properly choosing some corresponding parameters based on the Lyapunov method. Finally, the numerical simulation is carried out to verify the effectiveness of the proposed control method.

To guide multi-agent systems (MASs) through a cluttered environment, this paper proposes a distributed control method based on a novel fractional-order extended state observer (NFO-ESO). First, unlike the traditional multi-agent double-integral model, the agent model constructed in this paper contains external wind disturbances (mismatched disturbances) and internal unmodeled dynamics (matched disturbances), and these disturbances are treated as matched disturbance equivalent. Then, the designed disturbance observer is added to the robust distributed controller to coordinate the disturbance, which is used to accomplish the curve virtual tube crossing task in the perturbed case. The disturbance observer is NFO-ESO, which is implemented in this paper by constructing a neural network based on fractional-order discrete theory. It improves the weakness of the traditional ESO that only observes low-frequency slow time-varying disturbances and can be more suitable for nonlinear fast time-varying disturbance estimation. Finally, to demonstrate the advantages of the proposed controller, we show an example of MASs with wind disturbance and unmodeled dynamic through numerical simulation experiment. The instance shows the effectiveness of the proposed distributed control method under external disturbances.

Aiming at the problems of large data, high communication cost, vulnerable controller and poor expansibility which cause extra energy loss in the current centralized control of micro-grid, a novel topology of smart micro-grid for home applications is proposed. Moreover, a distributed optimization operation strategy based on Gossip algorithm is proposed to optimize the operation of micro-grid. In this strategy, the optimized operation of the micro-grid can be achieved only by exchanging information between adjacent controllers, while the central controller is not required. Therefore, the problems existing in centralized control can be effectively solved, and the control performance of the system is improved, which is beneficial to the micro-grid implementation of plug and play. Finally, the feasibility of the proposed micro-grid structure, model and operation method is verifed by the Matlab/Simulink simulation platform, simulation results are consistent with the consistent with the analysis. This paper aims to design a novel micro-grid for home applications to help achieve the goal of reducing carbon peaking at the micro level.

We consider the problem of designing (perhaps massively distributed) collectives of computational processes to maximize a provided “world utility” function. We consider this problem when the behavior of each process in the collective can be cast as striving to maximize its own payoff utility function. For such cases the central design issue is how to initialize/update those payoff utility functions of the individual processes so as to induce behavior of the entire collective having good values of the world utility. Traditional “team game” approaches to this problem simply assign to each process the world utility as its payoff utility function. In previous work we used the “Collective Intelligence” (COIN) framework to derive a better choice of payoff utility functions, one that results in world utility performance up to orders of magnitude superior to that ensuing from the use of the team game utility. In this paper, we extend these results using a novel mathematical framework. Under that new framework we review the derivation of the general class of payoff utility functions that both (i) are easy for the individual processes to try to maximize, and (ii) have the property that if good values of them are achieved, then we are assured a high value of world utility. These are the “Aristocrat Utility” and a new variant of the “Wonderful Life Utility” that was introduced in the previous COIN work. We demonstrate experimentally that using these new utility functions can result in significantly improved performance over that of previously investigated COIN payoff utilities, over and above those previous utilities’ superiority to the conventional team game utility. These results also illustrate the substantial superiority of these payoff functions to perhaps the most natural version of the economics technique of “endogenizing externalities.”

This work explores a distributed problem solving (DPS) approach, namely the AM/AG (Amplification/Aggregation) model. The AM/AG model is a hierarchic social system metaphor for DPS based on Mintzberg's model of organizations. At the core of the model are information flow mechanisms, namely, amplification and aggregation. Amplification is a process of decomposing a given task, called an agenda, into a set of subtasks with magnified degree of specificity and distributing them to multiple processing units downward in the hierarchy. Aggregation is a process of combining the results reported from multiple processing units into a unified view, called a resolution, and promoting the conclusion upward in the hierarchy.

Amplification is discussed in detail. A set of generative rules is introduced. Each rule specifies a set of actions for transforming an input agenda into other forms with higher specificity. The proposed model can be used to account for the memory recall process which makes associations between vast amounts of related concepts, sorts out the combined results, and promotes the most plausible ones. An example of memory recall is used to illustrate the model.