Modeling and Control of Renewable Resources
This area of research focuses on modeling and control of uncertain bio-economic (bionomic) dynamical systems and processes associated with the renewal (growth) and consumption of renewable (biological) resources, the institutions that manage the resources, and the human-institution interactions that impact the management of the resource.
Relevant application areas include:
- Management of Fisheries
- Eutrophication of Lakes
- Global Warming and Greenhouse Effect
Relevant Control Challenges
Relevant control challenges include:
The above includes modeling and analysis of the institutions that are instrumental in setting policy and responsible for resource management. Of particular focus is understanding fundamental sensitivity-vulnerability tradeoffs as well as developing robust control methods to achieve appropriate tradeoffs between economic and biological factors.
- uncertain nonlinearities (e.g. variable constraints),
- uncertain high frequency dynamics (i.e. unmodeled differential equations),
- parametric uncertainty,
- uncertain institutional dynamics,
- uncertain human-institution interactions,
- multivariable coupling/interaction,
- centralized versus decentralized control architectures,
- multiple time-scale dynamics; e.g. multiple measurement/actuation rates,
- selection of weighting function parameters for dynamical optimization,
- assessment of fundamental performance limitations and tradeoffs,
- following of varying (typically low frequency) reference commands,
- attenuation of (stochastic, typically low frequency) disturbances,
- attenuation of (stochastic, typically high frequency) measurement noise,
- state estimation,
- parameter and uncertainty estimation (system identification).
Objectives and Goals
The main objective of this research is to contribute to the development of effective public policy making, decision making, resource management.
Nonlinear ordinary and partial differential equations, concepts from classical and modern robust control, sensitivity analysis.
Collaborators and Sponsors
This work has been supported by the National Science Foundation (NSF).
- ASU Professors John Marty Anderies, Marcus Janssen, Charles Perrings, Ann Kinzig - School of Human Evolution and Social Change and Global Institute of Sustainability
- Professor Elinor Ostrom (Indiana University, Political Science).