Optimal control approaches for consensus and path planning in multi-agent systems

Optimal control is one of the most powerful, important and advantageous topics in control engineering. The two challenges in every optimal control problem are defining the proper cost function and obtaining the best method to minimize it. In this study, innovative optimal control approaches are developed to solve the two problems of consensus and path planning in multi-agent systems (MASs). The consensus problem for general Linear-Time Invariant systems is solved by implementing an inverse optimal control approach which enables us to start by deriving a control law based on the stability and optimality condition and then according to the derived control define the cost function. We will see that this method in which the cost function is not specified a priori as the conventional optimal control design has the benefit that the resulting control law is guaranteed to be both stabilizing and optimal. Three new theorems in related linear algebra are developed to enable us to use the algorithm for all the general LTI systems. The designed optimal control is distributed and only needs local neighbor-to-neighbor information based on the communication topology to make the agents achieve consensus and track a desired trajectory. Path planning problem is solved for a group are Unmanned Aerial Vehicles (UAVs) that are assigned to track the fronts of a fires in a process of wildfire management. We use Partially Observable Markov Decision Process (POMDP) in order to minimize the cost function that is defined according to the tracking error. Here the challenge is designing the algorithm such that (1) the UAVs are able to make decisions autonomously on which fire front to track and (2) they are able to track the fire fronts which evolve over time in random directions. We will see that by defining proper models, the designed algorithms provides real-time calculation of control variables which enables the UAVs to track the fronts and find their way autonomously. Furthermore, by implementing Nominal Belief-state Optimization (NBO) method, the dynamic constraints of the UAVs is considered and challenges such as collision avoidance is addressed completely in the context of POMDP

Robust Observation and Control of Complex Networks

The problem of understanding when individual actions of interacting agents display to a coordinated collective behavior has receiving a considerable attention in many research fields. Especially in control engineering, distributed applications in cooperative environments are achieving resounding success, due to the large number of relevant applications, such as formation control, attitude synchronization tasks and cooperative applications in large-scale systems. Although those problems have been extensively studied in Literature, themost of classic approaches use to consider the unrealistic scenario in which networks always consist of identical, linear, time-invariant entities. It’s clear that this assumption strongly approximates the effective behavior of a network. In fact agents can be subjected to parameter uncertainties, unmodeled dynamics or simply characterized by proper nonlinear dynamics. Therefore, motivated by those practical problems, the present Thesis proposes various approaches for dealing with the problem of observation and control in both the framework of multi-agents and complex interconnected systems. The main contributions of this Thesis consist on the development of several algorithms based on concepts of discontinuous slidingmode control. This techniques can be employed for solving in finite-time problems of robust state estimation and consensus-based synchronization in network of heterogenous nonlinear systems subjected to unknown but bounded disturbances and sudden topological changes. Both directed and undirected topologies have been taken into account. It is worth to mention also the extension of the consensus problem to networks of agents governed by a class parabolic partial differential equation, for which, for the first time, a boundary-based robust local interaction protocol has been presented

Robust Observation and Control of Complex Networks

The problem of understanding when individual actions of interacting agents display to a coordinated collective behavior has receiving a considerable attention in many research fields. Especially in control engineering, distributed applications in cooperative environments are achieving resounding success, due to the large number of relevant applications, such as formation control, attitude synchronization tasks and cooperative applications in large-scale systems. Although those problems have been extensively studied in Literature, themost of classic approaches use to consider the unrealistic scenario in which networks always consist of identical, linear, time-invariant entities. It’s clear that this assumption strongly approximates the effective behavior of a network. In fact agents can be subjected to parameter uncertainties, unmodeled dynamics or simply characterized by proper nonlinear dynamics. Therefore, motivated by those practical problems, the present Thesis proposes various approaches for dealing with the problem of observation and control in both the framework of multi-agents and complex interconnected systems. The main contributions of this Thesis consist on the development of several algorithms based on concepts of discontinuous slidingmode control. This techniques can be employed for solving in finite-time problems of robust state estimation and consensus-based synchronization in network of heterogenous nonlinear systems subjected to unknown but bounded disturbances and sudden topological changes. Both directed and undirected topologies have been taken into account. It is worth to mention also the extension of the consensus problem to networks of agents governed by a class parabolic partial differential equation, for which, for the first time, a boundary-based robust local interaction protocol has been presented

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin