Download Free Suboptimization Of Decentralized Control Systems Book in PDF and EPUB Free Download. You can read online Suboptimization Of Decentralized Control Systems and write the review.

Starting with a graph-theoretic framework for structural modeling of complex systems, this text presents results related to robust stabilization via decentralized state feedback. Subsequent chapters explore optimization, output feedback, the manipulative power of graphs, overlapping decompositions and the underlying inclusion principle, and reliability design. An appendix provides efficient graph algorithms. 1991 edition.
Decentralized Control of Complex Systems
This book is the record of papers presented at the Conference on Directions in Decentralized Control, Many-Person Optimization, and Large-Scale Systems held at the Colonial Hilton Inn, Wakefield, Massachusetts from September 1-3, 1975. Our motivation for organizing such a conference was two fold. Firstly, the last few years have seen a great deal of activity in the field of Large-Scale Systems Theory and it has been certainly one of the dominant themes of research in the disciplines of Systems and Control Theory. It therefore seemed appropriate to try and take stock of what had been accomplished and also try to "invent"l the future directions of research in this field. Secondly, the 6th World IFAC Conference was being held in Cambridge, Massachusetts the week earlier and it provided an ideal opportunity for taking advantage of the presence of a large number of specialists from all parts of the world to organize a small conference where a free exchange of ideas could take place. It is left to the readers of this volume to judge to what extent we have been successful in our above mentioned goals. There is no accepted definition of what constitutes a "large scale system" nor what large-scale system theory is. While this diversity does suggest that the field {whatever it may turn out to be} is in a state of flux, it does not necessarily imply chaos.
Application to a freeway corridor.
A large-scale system is composed of several interconnected subsystems. For such a system it is often desired to have some form of decentralization in the control structure, since it is typically not realistic to assume that all output measurements can be transmitted to every local control station. Problems of this kind can appear in electric power systems, communication networks, large space structures, robotic systems, economic systems, and traffic networks, to name only a few. Typical large-scale control systems have several local control stations which observe only local outputs and control only local inputs. All controllers are involved, however, in the control operation of the overall system. The focus of this book is on the efficient control of interconnected systems, and it presents systems analysis and controller synthesis techniques using a variety of methods. A systematic study of multi-input, multi-output systems is carried out and illustrative examples are given to clarify the ideas.
Decentralized control problems naturally arise in the control of large-scale networked systems. Such systems are regulated by a collection of local controllers in a decentralized manner, in the sense that each local controller is required to specify its control input based on its locally accessible sensor measurements. In this dissertation, we consider the decentralized control of discrete-time, linear systems subject to exogenous disturbances and polyhedral constraints on the state and input trajectories. The underlying system is composed of a finite collection of dynamically coupled subsystems, each of which is assumed to have a dedicated local controller. The decentralization of information is expressed according to sparsity constraints on the sensor measurements that each local controller has access to. In its most general form, the decentralized control problem amounts to an infinite-dimensional nonconvex program that is, in general, computationally intractable. The primary difficulty of the decentralized control problem stems from the potential informational coupling between the controllers. Specifically, in problems with nonclassical information structures, the actions taken by one controller can affect the information acquired by other controllers acting on the system. This gives rise to an incentive for controllers to communicate with each other via the actions that they undertake--the so-called signaling incentive. To complicate matters further, there may be hard constraints coupling the actions and local states being regulated by different controllers that must be jointly enforced with limited communication between the local controllers. In this dissertation, we abandon the search for the optimal decentralized control policy, and resort to approximation methods that enable the tractable calculation of feasible decentralized control policies. We first provide methods for the tractable calculation of decentralized control policies that are affinely parameterized in their measurement history. For problems with partially nested information structures, we show that the optimization over such a policy space admits an equivalent reformulation as a semi-infinite convex program. The optimal solution to these semi-inifinite programs can be calculated through the solution of a finite-dimensional conic program. For problems with nonclassical information structures, however, the optimization over such a policy space amounts to a semi-infinite nonconvex program. With the objective of alleviating the nonconvexity in such problems, we propose an approach to decentralized control design in which the information-coupling states are effectively treated as disturbances whose trajectories are constrained to take values in ellipsoidal "contract" sets whose location, scale, and orientation are jointly optimized with the affine decentralized control policy being used to control the system. The resulting problem is a semidefinite program, whose feasible solutions are guaranteed to be feasible for the original decentralized control design problem. Decentralized control policies that are computed according to such convex optimization methods are, in general, suboptimal. We, therefore, provide a method of bounding the suboptimality of feasible decentralized control policies through an information-based convex relaxation. Specifically, we characterize an expansion of the given information structure, which maximizes the optimal value of the decentralized control design problem associated with the expanded information structure, while guaranteeing that the expanded information structure be partially nested. The resulting decentralized control design problem admits an equivalent reformulation as an infinite-dimensional convex program. We construct a further constraint relaxation of this problem via its partial dualization and a restriction to affine dual control policies, which yields a finite-dimensional conic program whose optimal value is a provable lower bound on the minimum cost of the original decentralized control design problem. Finally, we apply our convexd programming approach to control design to the decentralized control of distributed energy resources in radial power distribution systems. We investigate the problem of designing a fully decentralized disturbance-feedback controller that minimizes the expected cost of serving demand, while guaranteeing the satisfaction of individual resource and distribution system voltage constraints. A direct application of our aforementioned control design methods enables both the calculation of affine controllers and the bounding of their suboptimality through the solution of finite-dimensional conic programs. A case study demonstrates that the decentralized affine controller we compute can perform close to optimal.
Decentralized Estimation and Control for Multisensor Systems explores the problem of developing scalable, decentralized estimation and control algorithms for linear and nonlinear multisensor systems. Such algorithms have extensive applications in modular robotics and complex or large scale systems, including the Mars Rover, the Mir station, and Space Shuttle Columbia. Most existing algorithms use some form of hierarchical or centralized structure for data gathering and processing. In contrast, in a fully decentralized system, all information is processed locally. A decentralized data fusion system includes a network of sensor nodes - each with its own processing facility, which together do not require any central processing or central communication facility. Only node-to-node communication and local system knowledge are permitted. Algorithms for decentralized data fusion systems based on the linear information filter have been developed, obtaining decentrally the same results as those in a conventional centralized data fusion system. However, these algorithms are limited, indicating that existing decentralized data fusion algorithms have limited scalability and are wasteful of communications and computation resources. Decentralized Estimation and Control for Multisensor Systems aims to remove current limitations in decentralized data fusion algorithms and to extend the decentralized principle to problems involving local control and actuation. The text discusses: Generalizing the linear Information filter to the problem of estimation for nonlinear systems Developing a decentralized form of the algorithm Solving the problem of fully connected topologies by using generalized model distribution where the nodal system involves only locally relevant states Reducing computational requirements by using smaller local model sizes Defining internodal communication Developing estima
Decentralized control has been one of the important problems in systems and control engineering. Computing an optimal decentralized controller for general linear systems, however, is known to be a very challenging task. In particular, designing an optimal decentralized controller in the standard framework of a linear system with quadratic cost and Gaussian noise is well known to be extremely hard even in very simple and small sized problems. Because of this fact, previous work has focused on characterizing several different classes of problems for which an optimal decentralized controller may be efficiently computed. The set of quadratically invariant problems is one of the largest known class of such problems. This dissertation provides a novel, general, and powerful framework for addressing decentralized control by introducing the idea of using rational elimination theory of algebraic geometry. We show that, in certain cases, this approach reduces the set of closed-loop maps of decentralized control to the solution set of a collection of linear equations. We show how to use these linear equations to find an optimal decentralized controller. We also prove that if a system is quadratically invariant then under an appropriate technical condition the resulting elimination set is affine. We further illustrate that our approach can be well applied to a strictly larger class of decentralized control problem than the quadratically invariant one by presenting a simple example: the example shows that there are problems which are not quadratically invariant but for which the resulting elimination description is affine.
This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural inverse optimal control for stabilization. The fourth decentralized neural inverse optimal control is designed for trajectory tracking. This comprehensive work on decentralized control of robot manipulators and mobile robots is intended for professors, students and professionals wanting to understand and apply advanced knowledge in their field of work.