Invasive species management in two-patch environments: Agricultural damage control in the raccoon (procyon lotor) problem, Hokkaido, Japan

We develop discrete-time models for analyzing the long run equilibrium outcomes on invasive species management in two-patch environments with migration. In particular, the focus is upon a situation where removal operations for invasive species are implemented only in one patch (controlled patch). The new features of the model are that (i) asymmetry in density dependent migration is considered, which may originate from impact of harvesting as well as heterogeneous habitat conditions, and (ii) the effect of density-dependent catchability is well-taken to account for the nature that required effort level to remove one individual may rise as the existing population decreases. The model is applied for agricultural damage control in the raccoon problem that has occurred in Hokkaido, Japan. Numerical illustration demonstrates that the long run equilibrium outcomes highly depend on the degree of asymmetry in migration as well as the sensitivity of catchability in response to a change in the population size of invasive species. Furthermore, we characterize the conditions under which the economically optimal effort levels are qualitatively affected by the above two factors and aiming at local extermination of invasive species in controlled patch is justified.catchability, meta-population, local extermination, removal effort, density dependent migration

Decentralized multi-agent coordinated secondary voltage control of power systems

In this thesis, two different approaches toward Secondary Voltage Control of large scale power systems are presented. In the first approach, for each area of the power grid, a Model Predictive Controller which modifies the set-points of reactive power compensators participating in Coordinated Secondary Voltage Control algorithm is designed. The proposed controller takes into account reactive power limits of these compensation devices. The novelty of the method lies in the consideration of measured reactive power deviation on tie-lines between neighboring areas as measured disturbance and compensation of the disturbance by regional MPC controllers. As another contribution of this work, the validation of the proposed algorithm is done in real-time simulation environment in which the decentralized MPC controllers are run in parallel on separate computational cores. The stability and robustness of the presented algorithm is validated for a large scale realistic transmission network with 5000 buses considering standard communication protocols to send and receive the data. Simulation results show that the proposed method can regulate the voltages on the pilot buses at the desired values in presence of load variations and communication delays. The computational burden of the proposed method is also evaluated in real-time. For the networks facing large disturbances, an alternative model based centralized controller is presented next which considers the nonlinearities of the power system while taking into account both discrete and continuous type compensators. In this regard, sensitivity analysis is used to first find the most sensitive buses of the network called pilot nodes and second to locate the control buses in which discrete type or continuous type controllers are installed. The CSVC controller is then designed based on the notion of nonlinear sensitivity model which relates reactive power injection/absorption or change of reference voltage of controllers to the voltage variation at pilot buses at different operating points of the network. The non-linear sensitivity model is identified using Neural Networks approach which is then used by Simulated Annealing optimization algorithm to solve a mixed discrete-continuous type optimization problem and find the suboptimal control input. The proposed algorithm is tested in real-time against coordinated secondary voltage control method based on linear sensitivity models and also traditional capacitor/inductor banks’ control method which is based on local measurements. Finally, the same methodology as nonlinear sensitivity based optimal controller is adapted to a decentralized architecture considering consensus between regional controllers overlapping in some buses with a connected reactive power compensator. The consensus is reached in two iterations and does not require any communication link between regional controllers. Moreover the proposed method gives the flexibility to the shared compensators as agents to decide on their degree of participation in SVC algorithm of each neighbor based on their own performance objectives

Optimal Control of a Rigid Body using Geometrically Exact Computations on SE(3)

Optimal control problems are formulated and efficient computational procedures are proposed for combined orbital and rotational maneuvers of a rigid body in three dimensions. The rigid body is assumed to act under the influence of forces and moments that arise from a potential and from control forces and moments. The key features of this paper are its use of computational procedures that are guaranteed to preserve the geometry of the optimal solutions. The theoretical basis for the computational procedures is summarized, and examples of optimal spacecraft maneuvers are presented.Comment: IEEE Conference on Decision and Control, 2006. 6 pages, 19 figure

A Data-driven Approach to Robust Control of Multivariable Systems by Convex Optimization

The frequency-domain data of a multivariable system in different operating points is used to design a robust controller with respect to the measurement noise and multimodel uncertainty. The controller is fully parametrized in terms of matrix polynomial functions and can be formulated as a centralized, decentralized or distributed controller. All standard performance specifications like $H_2$, $H_\infty$ and loop shaping are considered in a unified framework for continuous- and discrete-time systems. The control problem is formulated as a convex-concave optimization problem and then convexified by linearization of the concave part around an initial controller. The performance criterion converges monotonically to a local optimal solution in an iterative algorithm. The effectiveness of the method is compared with fixed-structure controllers using non-smooth optimization and with full-order optimal controllers via simulation examples. Finally, the experimental data of a gyroscope is used to design a data-driven controller that is successfully applied on the real system

Generation of Explicit Knowledge from Empirical Data through Pruning of Trainable Neural Networks

This paper presents a generalized technology of extraction of explicit knowledge from data. The main ideas are 1) maximal reduction of network complexity (not only removal of neurons or synapses, but removal all the unnecessary elements and signals and reduction of the complexity of elements), 2) using of adjustable and flexible pruning process (the pruning sequence shouldn't be predetermined - the user should have a possibility to prune network on his own way in order to achieve a desired network structure for the purpose of extraction of rules of desired type and form), and 3) extraction of rules not in predetermined but any desired form. Some considerations and notes about network architecture and training process and applicability of currently developed pruning techniques and rule extraction algorithms are discussed. This technology, being developed by us for more than 10 years, allowed us to create dozens of knowledge-based expert systems. In this paper we present a generalized three-step technology of extraction of explicit knowledge from empirical data.Comment: 9 pages, The talk was given at the IJCNN '99 (Washington DC, July 1999

Local Improvements to Reduced-Order Approximations of Optimal Control Problems Governed by Diffusion-Convection-Reaction Equation

We consider the optimal control problem governed by diffusion convection reaction equation without control constraints. The proper orthogonal decomposition(POD) method is used to reduce the dimension of the problem. The POD method may be lack of accuracy if the POD basis depending on a set of parameters is used to approximate the problem depending on a different set of parameters. We are interested in the perturbation of diffusion term. To increase the accuracy and robustness of the basis, we compute three bases additional to the baseline POD. The first two of them use the sensitivity information to extrapolate and expand the POD basis. The other one is based on the subspace angle interpolation method. We compare these different bases in terms of accuracy and complexity and investigate the advantages and main drawbacks of them.Comment: 19 pages, 5 figures, 2 table