40 research outputs found
Optimal Subharmonic Entrainment
For many natural and engineered systems, a central function or design goal is
the synchronization of one or more rhythmic or oscillating processes to an
external forcing signal, which may be periodic on a different time-scale from
the actuated process. Such subharmonic synchrony, which is dynamically
established when N control cycles occur for every M cycles of a forced
oscillator, is referred to as N:M entrainment. In many applications,
entrainment must be established in an optimal manner, for example by minimizing
control energy or the transient time to phase locking. We present a theory for
deriving inputs that establish subharmonic N:M entrainment of general nonlinear
oscillators, or of collections of rhythmic dynamical units, while optimizing
such objectives. Ordinary differential equation models of oscillating systems
are reduced to phase variable representations, each of which consists of a
natural frequency and phase response curve. Formal averaging and the calculus
of variations are then applied to such reduced models in order to derive
optimal subharmonic entrainment waveforms. The optimal entrainment of a
canonical model for a spiking neuron is used to illustrate this approach, which
is readily extended to arbitrary oscillating systems
Optimal Control and Synchronization of Dynamic Ensemble Systems
Ensemble control involves the manipulation of an uncountably infinite collection of structurally identical or similar dynamical systems, which are indexed by a parameter set, by applying a common control without using feedback. This subject is motivated by compelling problems in quantum control, sensorless robotic manipulation, and neural engineering, which involve ensembles of linear, bilinear, or nonlinear oscillating systems, for which analytical control laws are infeasible or absent. The focus of this dissertation is on novel analytical paradigms and constructive control design methods for practical ensemble control problems. The first result is a computational method %based on the singular value decomposition (SVD) for the synthesis of minimum-norm ensemble controls for time-varying linear systems. This method is extended to iterative techniques to accommodate bounds on the control amplitude, and to synthesize ensemble controls for bilinear systems. Example ensemble systems include harmonic oscillators, quantum transport, and quantum spin transfers on the Bloch system. To move towards the control of complex ensembles of nonlinear oscillators, which occur in neuroscience, circadian biology, electrochemistry, and many other fields, ideas from synchronization engineering are incorporated. The focus is placed on the phenomenon of entrainment, which refers to the dynamic synchronization of an oscillating system to a periodic input. Phase coordinate transformation, formal averaging, and the calculus of variations are used to derive minimum energy and minimum mean time controls that entrain ensembles of non-interacting oscillators to a harmonic or subharmonic target frequency. In addition, a novel technique for taking advantage of nonlinearity and heterogeneity to establish desired dynamical structures in collections of inhomogeneous rhythmic systems is derived
Optimal Control of Transient Flow in Natural Gas Networks
We outline a new control system model for the distributed dynamics of
compressible gas flow through large-scale pipeline networks with time-varying
injections, withdrawals, and control actions of compressors and regulators. The
gas dynamics PDE equations over the pipelines, together with boundary
conditions at junctions, are reduced using lumped elements to a sparse
nonlinear ODE system expressed in vector-matrix form using graph theoretic
notation. This system, which we call the reduced network flow (RNF) model, is a
consistent discretization of the PDE equations for gas flow. The RNF forms the
dynamic constraints for optimal control problems for pipeline systems with
known time-varying withdrawals and injections and gas pressure limits
throughout the network. The objectives include economic transient compression
(ETC) and minimum load shedding (MLS), which involve minimizing compression
costs or, if that is infeasible, minimizing the unfulfilled deliveries,
respectively. These continuous functional optimization problems are
approximated using the Legendre-Gauss-Lobatto (LGL) pseudospectral collocation
scheme to yield a family of nonlinear programs, whose solutions approach the
optima with finer discretization. Simulation and optimization of time-varying
scenarios on an example natural gas transmission network demonstrate the gains
in security and efficiency over methods that assume steady-state behavior
Locational Marginal Pricing of Natural Gas subject to Engineering Constraints
We derive a price formation mechanism to maximize social welfare for a pipeline network that delivers natural gas from suppliers to consumers. The system is modeled as a metric graph subject to physical balance laws for steady-state hydraulic flow on edges and mass balance at nodes. The pricing mechanism incorporates engineering constraints on local pressures and energy applied by gas compressors. Optimality conditions yield expressions for locational marginal prices for gas (gLMPs) and a decomposition of gLMPs into components corresponding to energy, compression, and two types of congestion. We demonstrate that price and pressure differentials between nodes have the opposite sign, so that price cannot decline in the direction of flow, and prove that the pricing mechanism is revenue adequate. We also present computational examples of congestion pricing for a small test network and a large-scale case study