This thesis presents a control algorithm to be used by a team of ECAVs (Electronic Combat Air Vehicle) to deceive a network of radars through the generation of a phantom track. Each ECAV has the electronic capability of intercepting and introducing an appropriate time delay to a transmitted pulse of a radar before transmitting it back to the radar, thereby deceiving the radar into seeing a phantom target at a range beyond that of the ECAV. A radar network correlates targets and target tracks to detect range delay based deception. A team of cooperating ECAVs, however, precisely plans their trajectories in a way all the radars in the radar network are deceived into seeing the same phantom. Since each radar in the network confirms the target track of the other, the phantom track is considered valid. An important feature of the algorithm achieving this is that it translates kinematic constraints on the ECAV dynamic system into constraints on the phantom point. The phantom track between two specified way points then evolves without violating any of the system constraints. The evolving phantom track in turn generates the actual controls on the ECAVs so that ECAVs have flyable trajectories. The algorithms give feasible but suboptimal solutions. The main objectives are algorithm development for phantom track generation through a team of cooperating ECAVs, development of the algorithms to be finite dimensional searches and determining necessary conditions for feasible solutions in the immediate horizon of the searches of the algorithm. Feasibility of the algorithm in deceiving a radar network through phantom track generation is demonstrated through simulation results.

This thesis considers the design of distributed state and output feedback control algorithms for linear multi-agent systems with performance guarantees in the presence of delays. The multi-agent systems considered are assumed to exchange relative information over an information network. As a first contribution, a novel distributed state feedback control design method with a sub-optimal LQR performance is developed for a network of multiple agents. For the control design process, it is assumed that the exchange of relative information is instantaneous. A stability analysis of the proposed control law is performed by incorporating delays in relative information to ascertain the maximum possible delay that can be accommodated by the communication network. Subsequently, the assumption of the exchange of instantaneous relative information in the control design process is relaxed and the relative information is assumed to be delayed. The system is then represented as a time-delay system. Distributed state feedback control synthesis methods are then developed for the system with a certain level of LQR performance. In the above contributions, the time delay analysis and the development of delay based control methods, it is implicitly assumed that delays are detrimental to achieving cooperative tasks for a multi-agent system. Subsequently, positive effects of delays in communication of relative information are explored. For this a network of vehicles described by double integrator dynamics, which cannot be stabilized by static output feedback without delays, is considered. A novel control design method to achieve exponential stabilization of such a multi-agent system by static output feedback using delayed relative information is developed. Conclusions are drawn from the results of the research presented in this thesis and a few directions for future work are identified.

The major objective of this dissertation is extending the capabilities of game theoretic distributed control to more general settings. In particular, we are interested in drifting environments and/or constrained communications. The first part of the dissertation concerns slowly varying dynamics, i.e., drifting environments. A standard assumption in game theoretic learning is a stationary environment, e.g., the game is fixed. We investigate the case of slow variations and show that for sufficiently slow time variations, the limiting behavior “tracks” the stochastically stable states. Since the analysis is regarding Markov processes, the results could be applied to various game theoretic learning rules. In this research, the results were applied to log-linear learning. A mobile sensor coverage example was tested in both simulation and laboratory experiments. The second part considers a problem of coordinating team players' actions without any communications in team-based zero-sum games. Generally, some global signalling devices are required for common randomness between players, but communications are very limited or impossible in many practical applications. Instead of learning a one-shot strategy, we let players coordinate a periodic sequence of deterministic actions and put an assumption on opponent's rationality. Since team players' action sequences are periodic and deterministic, common randomness is no longer required to coordinate players. It is proved that if a length of a periodic action sequence is long enough, then opponents with limited rationality cannot recognize its pattern. Because the opponents cannot recognize that the players are playing deterministic actions, the players' behavior looks like a correlated and randomized joint strategy with empirical distribution of their action sequences. Consequently players can coordinate their action sequences without any communications or global signals, and the resulting action sequences have correlated behavior. Moreover, the notion of micro-players are introduced for efficient learning of long action sequences. Micro-player matching approach provides a new framework that converts the original team-based zero-sum game to a game between micro-players. By introducing a de Bruijn sequence to micro-player matching, we successfully separate the level of opponent's rationality and the size of the game of micro-players. The simulation results are shown to demonstrate the performance of micro-player matching methods. Lastly, the results of the previous two topics are combined by considering a problem of coordinating actions without communications in drifting environments. More specifically, it is assumed that the opponent player in the team-based zero-sum games tries to adjust its strategy in the set of bounded recall strategies. Then the time-varying opponent's strategy can be considered as a dynamic environment parameter in a coordination game between the team players. Additionally, we develop a human testbed program for further study regarding a human as an adaptive opponent in the team-based zero-sum games. The developed human testbed program can be a starting point for studying game theoretic correlated behavior learning against a human.

The cooperative control of large-scale multi-agent systems has gained a significant interest in recent years from the robotics and control communities for multi-vehicle control. One motivator for the growing interest is the application of spatially and temporally distributed multiple unmanned aerial vehicle (UAV) systems for distributed sensing and collaborative operations. In this research, the multi-vehicle control problem is addressed using a decentralised control system. The work aims to provide a decentralised control framework that synthesises the self-organised and coordinated behaviour of natural swarming systems into cooperative UAV systems. The control system design framework is generalised for application into various other multi-agent systems including cellular robotics, ad-hoc communication networks, and modular smart-structures. The approach involves identifying suitable relationships that describe the behaviour of the UAVs within the swarm and the interactions of these behaviours to produce purposeful high-level actions for system operators. A major focus concerning the research involves the development of suitable analytical tools that decomposes the general swarm behaviours to the local vehicle level. The control problem is approached using two-levels of abstraction; the supervisory level, and the local vehicle level. Geometric control techniques based on differential geometry are used at the supervisory level to reduce the control problem to a small set of permutation and size invariant abstract descriptors. The abstract descriptors provide an open-loop optimal state and control trajectory for the collective swarm and are used to describe the intentions of the vehicles. Decentralised optimal control is implemented at the local vehicle level to synthesise self-organised and cooperative behaviour. A deliberative control scheme is implemented at the local vehicle level that demonstrates autonomous, cooperative and optimal behaviour whilst the preserv ing precision and reliability at the local vehicle level.

No description available.

Multiple robot manipulators cooperating in a common manipulation task can accomplish complex tasks that a single manipulator would be unable to complete. To achieve physical cooperation with multiple manipulators working on a common object, interaction forces need to be controlled throughout the motion. The aim of this research is to develop an inverse dynamics model-based cooperative force and position control scheme for multiple robot manipulators. An extended definition of motion is proposed to include force demands based on a constrained Lagrangian dynamics and Lagrangian multipliers formulation. This allows the direct calculation of the inverse dynamics with both motion and force demands. A feedforward controller based on the proposed method is built to realise the cooperative control of two robots sharing a common load, with both motion and force demands. Furthermore, this thesis develops a method to design an optimal excitation trajectory for robot dynamic parameter estimation utilising the Schroeder Phased Harmonic Sequence. This method yields more precise and accurate inverse dynamics models, which result in better control. The proposed controller is then tested in an experimental set-up consisting of two robot manipulators and a common load. Results show that in general the proposed controller performs noticeably better position and force tracking, especially for higher speed motions, when compared to traditional hybrid position/force controllers.

This dissertation addresses the design and stability analysis of decentralized, cooperative control laws for multivehicle systems. Advances in communication, navigation, and surveillance systems have enabled greater autonomy in multivehicle systems, and there is a shift toward decentralized, cooperative systems for computational efficiency and robustness. In a decentralized control scheme, control inputs are determined onboard each vehicle; therefore, decentralized controllers are more efficient for large numbers of vehicles, and the system is more robust to communication failures and reconfiguration. The design of decentralized, cooperative control laws is explored for a nonlinear vehicle model that can be represented in a double-integrator form. Cooperative controllers are functions of spacing errors with respect to other vehicles in the system, where the communication structure defines the information that is available to each vehicle. Control inputs are selected to achieve internal stability, or zero steady-state spacing errors, between vehicles in the system. Closed-loop equations of motion for the cooperative system can be written in a structural form, where damping and stiffness matrices contain control gains acting on the velocity and positions of the vehicles, respectively. The form of the stiffness matrix is determined by the communication structure, where different communication structures yield different control forms. Communication structures are compared using two structural analysis tools: modal cost and frequency-response functions, which evaluate the response of the multivehicle systems to disturbances. The frequency-response information is shown to reveal the string stability of different cooperative control forms. The effects of time delays in the feedback states of the cooperative control laws on system stability are also investigated. Closed-loop equations of motion are modeled as delay differential equations, and two stability notions are presented: delay-independent and delay-dependent stability. Lastly, two additional cooperative control forms are investigated. The first control form spaces vehicles along an arbitrary path, where distances between vehicles are constant for a given spacing parameter. This control form shows advantages over spacing vehicles using control laws designed in an inertial frame. The second control form employs a time-based spacing scheme, which spaces vehicles at constant-time intervals at a desired endpoint. The stability of these control forms is presented.

Pursuit and evasion games have garnered much research attention since the class of problems was first posed over a half century ago. With wide applicability to both civilian and military problems, the study of pursuit and evasion games showed much early promise. Early work generally focused on analytical solutions to games involving a single pursuer and a single evader. These solutions generally assumed simple system dynamics to facilitate convergence to a solution. More recently, numerical techniques have been utilized to solve more difficult problems. While many sophisticated numerical tools exist for standard optimization and optimal control problems, developing a more complete set of numerical tools for pursuit and evasion games is still a developing topic of research. This thesis extends the current body of numeric solution tools in two ways. First, an existing approach that modifies sophisticated optimization tools to solve two player pursuer and evasion games is extended to incorporate a class of state inequality constraints. Several classical problems are solved to illustrate the e±cacy of the new approach. Second, a new cooperation metric is introduced into the system objective function for multi-player pursuit and evasion games. This new cooperation metric encourages multiple pursuers to surround and then proceed to capture an evader. Several examples are provided to demonstrate this new cooperation metric.

The Poincare equations, also known as Lagrange's equations in quasi coordinates, are revisited with special attention focused on a diagonal form. The diagonal form stems from a special choice of quasi velocities that were first introduced by Georg Hamel nearly a century ago. The form has been largely ignored because the quasi velocities create so-called Hamel coefficients that appear in the governing equations and are based on the partial derivative of the mass matrix factorization. Consequently, closed-form expressions for the Hamel coefficients can be difficult to obtain and relying on finite-dimensional, numerical methods are unattractive. In this thesis we use a newly developed operator overloading technique to automatically generate the Hamel coefficients through exact partial differentiation together with numerical evaluation. The equations can then be numerically integrated for system simulation. These special Poincare equations are called the Hamel Form and their usefulness in dynamic modeling and control is investigated. Coordinated control algorithms for an automatic retargeting system are developed in an attempt to protect an area against direct assaults. The scenario is for a few weapon systems to suddenly be faced with many hostile targets appearing together. The weapon systems must decide which weapon system will attack which target and in whatever order deemed sufficient to defend the protected area. This must be performed in a real-time environment, where every second is crucial. Four different control methods in this thesis are developed. They are tested against each other in computer simulations to determine the survivability and thought process of the control algorithms. An auction based control algorithm finding targets of opportunity achieved the best results.

With the advancement of communication technology, more and more control processes happen in networked environment. This makes it possible for us to deploy multiple systems in a spatially distributed way such that they could finish certain tasks collaboratively. While it brings about numerous advantages over conventional control, challenges arise in the mean time due to the imperfection of communication. This thesis is aimed to solve some problems in cooperative control involving multiple agents in the presence of communication constraints. Overall, it is comprised of two main parts: Distributed consensus in multi-agent systems and bilateral teleoperation. Chapter 2 to Chapter 4 deal with the consensus problem in multi-agent systems. Our goal is to design appropriate control protocols such that the states of a group of agents will converge to a common value eventually. The robustness of multi-agent systems against various adverse factors in communication is our central concern. Chapter 5 copes with bilateral teleoperation with time delays. The task is to design control laws such that synchronization is reached between the master plant and slave plant. Meanwhile, transparency should be maintained within an acceptable level. Chapter 2 investigates the consensus problem in a multi-agent system with directed communication topology. The time delays are modeled as a Markov chain, thus more characteristics of delays are taken into account. A delay-dependent approach has been proposed to design the Laplacian matrix such that the system is robust against stochastic delays. The consensus problem is converted into stabilization of its equivalent error dynamics, and the mean square stability is employed to characterize its convergence property. One feature of Chapter 2 is redesign of the adjacency matrix, which makes it possible to adjust communication weights dynamically. In Chapter 3, average consensus in single-integrator agents with time-varying delays and random data losses is studied. The interaction topology is assumed to be undirected. The communication constraints lie in two aspects: 1) time-varying delays that are non-uniform and bounded; 2) data losses governed by Bernoulli processes with non-uniform probabilities. By considering the upper bounds of delays and probabilities of packet dropouts, sufficient conditions are developed to guarantee that the multi-agent system will achieve consensus. Chapter 4 is concerned with the consensus problem with double-integrator dynamics and non-uniform sampling. The communication topology is assumed to be fixed and directed. With the adoption of time-varying control gains and the theory on stochastic matrices, we prove that when the graph has a directed spanning tree and the control gains are properly selected, consensus will be reached. Chapter 5 deals with bilateral teleoperation with probabilistic time delays. The delays are from a finite set and each element in the set has a probability of occurrence. After defining the tracking error between the master and slave, the input-to-state stability is used to characterize the system performance. By taking into account the probabilistic information in time delays and using the pole placement technique, the teleoperation system has achieved better position tracking and enhanced transparency. / Graduate