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.

在本文中，提出了一种新颖的方法，用于将生物启发的最优控制策略（BIO CS）与基于人工神经网络（ANN）的自适应组件相集成，以用于高级能源系统应用。具体来说，BIO CS遵循蚂蚁追随的规则，以生物学启发的方式采用基于梯度的最优控制求解器，以同时将多个过程输出控制在所需的设定点。而且，ANN组件通过使用具有在线学习功能的单隐藏层技术来增加控制器的基本BIO，从而捕获控制器和工厂模型之间的不匹配。CS控制法则。最终的方法是仿生控制和数据驱动方法的独特结合，为动态系统提供了最佳解决方案。通过集成气化联合循环（IGCC）工艺以及碳捕集作为先进的能源系统示例，说明了所提出框架的适用性。特别是，在DYNSIM IGCC设备仿真的一个子系统相关联的多变量控制结构^®被寻址。所提出的控制律导出MATLAB ^®，而植物模型是建立在DYNSIM ^®，和以前开发的MATLAB ^® -DYNSIM ^®链接用于实现目的。与独立的BIO CS和比例积分（PI）控制器实现相比，所提出的集成方法在减少输出跟踪误差方面将过程的整体性能提高了85％，从而实现了更快的设定值跟踪。因此，提出的框架为未来的能源系统的高级控制提供了一个有希望的替代方案。