lab-control-avanzado

The main objective of this laboratory is to enable the implementation of advanced strategies of adaptive robust control, predictive control, estimation of state, and fault detection in nonlinear and non-Gaussian systems.

Among the methods studied, the use of neural networks, fuzzy logic, passivity, flatness, backstepping, fractional control, and Bayesian estimation algorithms are highlighted. The laboratory is oriented mainly towards research purposes, although it is also used in (post)graduate teaching.

The laboratory equipment includes instruments, sensors, a temperature-modified chamber, Ion-Lithium battery chargers and programmable DC (Direct Current) loads , all of the above used in the design and execution of controlled cell loading/unloading tests at controlled temperatures around 0 ° C and 40 ° C. There is also a self-balancing “segway” vehicle and an engine-generator platform to test advanced control strategies.

  • FONDECYT Project 1110070: “Risk-Sensitive Particle Filtering Framework for Failure Prognosis and Uncertainty Representation in Nonlinear Systems with High-Impact/Low-Likelihood Events”. PI: Marcos Orchard (U.Ch). Faculty of Mathematics and Physical Sciences. Department of Electrical Engineering. University of Chile (2011-2013).
  • Innova-Chile CORFO Project 11IDL1-10409: “Model-Based Probabilistic Approach for Online State-of-Health/Load-State Estimation and Profile Characterization of Lithium-Ion Battery Utilization”. PI: Marcos Orchard (U.Ch.). Faculty of Mathematics and Physical Sciences. Department of Electrical Engineering. University of Chile (2012).
  • “Real-time estimation module and prediction of health status (SOH) and state of charge (SOC) of lithium-ion batteries”. PI: Marcos Orchard (U.Ch.). Center of Innovation of  Lithium (Center of Energy). Department of Electrical Engineering, Faculty of Physical Sciences and Mathematics, University of Chile (03/2012 – 09/2012).
  • FONDECYT Project 11070022: “Sequential Monte Carlo Methods and Feedback Concepts Applied to Fault Diagnosis and Failure Prognosis in Nonlinear, Non-Gaussian Dynamic Systems”. PI: Marcos Orchard (U.Ch). Faculty of Mathematics and Physical Sciences. Department of Electrical Engineering. University of Chile (2007-2009).
  • FONDECYT Project 1120453: “Improvements of Adaptive Systems Performance by Using Fractional Order Observers and Particle Swarm Optimization”. PI: Manuel Duarte (U.Ch). Faculty of Mathematics and Physical Sciences. Department of Electrical Engineering. University of Chile (2012-2014).
  • FONDECYT Project 1090208: “Design of a Fractional Order Adaptive Controller with Applications”. PI: Manuel Duarte (U.Ch). Faculty of Mathematics and Physical Sciences. Department of Electrical Engineering. University of Chile (2009-2011).
Responsible Academics
Manuel Duarte

Manuel Duarte

Ph.D. Yale University, USA.

Marcos Orchard

Marcos Orchard

Ph.D. Georgia Institute of Technology, USA.