A biologically-inspired approach for adaptive control of advanced energy systems. (2nd September 2018) - Mirlekar, Gaurav; Al-Sinbol, Ghassan; Perhinschi, Mario; Lima, Fernando V.
Highlights: An integrated biomimetic control approach is introduced for advanced energy systems. In this approach, BIO-CS is employed to generate the baseline control laws. A neural network-based adaptive component is integrated into the BIO–CS framework. The integrated approach is illustrated via an IGCC process with carbon capture. Abstract: In this article, a novel approach is proposed for integrating a Biologically-Inspired Optimal Control Strategy (BIOCS) with an Artificial Neural Network (ANN)-based adaptive component for advanced energy systems applications. Specifically, BIOCS employs gradient-based optimal control solvers in a biologically-inspired manner, following the rule of pursuit for ants, to simultaneously control multiple process outputs at their desired setpoints. Also, the ANN component captures the mismatch between the controller and the plant models by using a single-hidden-layer technique with online learning capabilities to augment the baseline BIOCS control laws. The resulting approach is a unique combination of biomimetic control and data-driven methods that provides optimal solutions for dynamic systems. The applicability of the proposed framework is illustrated via an Integrated Gasification Combined Cycle (IGCC) process with carbon capture as an advanced energy system example. In particular, a multivariable control structure associated with a subsystem of the IGCC plant simulation in DYNSIM ® is addressed. The proposed control laws are derived in MATLAB ®, while the plant models are built in DYNSIM ®, and a previously developed MATLAB ® -DYNSIM ® link is employed for implementation purposes. The proposed integrated approach improves the overall performance of the process up to 85% in terms of reducing the output tracking error when compared to stand-alone BIOCS and Proportional-Integral (PI) controller implementations, resulting in faster setpoint tracking. The proposed framework thus provides a promising alternative for advanced control of energy systems of the future.