Zhang Shu, Bentsman Joseph, Lou Xinsheng, Neuschaefer Carl, Lee Yongseok, El-Kebir Hamza
Department. of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, 1206 W Green St., Urbana, IL 61801, USA.
Alstom Thermal Power, Windsor, CT 06095, USA.
Energies (Basel). 2020 Apr;13(7). doi: 10.3390/en13071759. Epub 2020 Apr 7.
Chemical looping is a near-zero emission process for generating power from coal. It is based on a multi-phase gas-solid flow and has extremely challenging nonlinear, multi-scale dynamics with jumps, producing large dynamic model uncertainty, which renders traditional robust control techniques, such as linear parameter varying design, largely inapplicable. This process complexity is addressed in the present work through the temporal and the spatiotemporal multiresolution modeling along with the corresponding model-based control laws. Namely, the nonlinear autoregressive with exogenous input model structure, nonlinear in the wavelet basis, but linear in parameters, is used to identify the dominant temporal chemical looping process dynamics. The control inputs and the wavelet model parameters are calculated by optimizing a quadratic cost function using a gradient descent method. The respective identification and tracking error convergence of the proposed self-tuning identification and control schemes, the latter using the unconstrained generalized predictive control structure, is separately ascertained through the Lyapunov stability theorem. The rate constraint on the control signal in the temporal control law is then imposed and the control topology is augmented by an additional control loop with self-tuning deadbeat controller which uses the spatiotemporal wavelet riser dynamics representation. The novelty of this work is three-fold: (1) developing the self-tuning controller design methodology that consists in embedding the real-time tunable temporal highly nonlinear, but linearly parametrizable, multiresolution system representations into the classical rate-constrained generalized predictive quadratic optimal control structure, (2) augmenting the temporal multiresolution loop by a more complex spatiotemporal multiresolution self-tuning deadbeat control loop, and (3) demonstrating the effectiveness of the proposed methodology in producing fast recursive real-time algorithms for controlling highly uncertain nonlinear multiscale processes. The latter is shown through the data from the implemented temporal and augmented spatiotemporal solutions of a difficult chemical looping cold flow tracking control problem.
化学链燃烧是一种用于煤炭发电的近零排放过程。它基于多相气固流动，具有极具挑战性的非线性、多尺度动态特性且伴有跳跃，会产生很大的动态模型不确定性，这使得传统的鲁棒控制技术，如线性参数变化设计，在很大程度上不适用。在本工作中，通过时间和时空多分辨率建模以及相应的基于模型的控制律来解决这一过程复杂性问题。具体而言，采用具有外部输入的非线性自回归模型结构，其在小波基中是非线性的，但在参数上是线性的，来识别化学链燃烧过程中主要的时间动态特性。通过使用梯度下降法优化二次成本函数来计算控制输入和小波模型参数。所提出的自整定识别和控制方案（后者使用无约束广义预测控制结构）各自的识别和跟踪误差收敛性，通过李雅普诺夫稳定性定理分别确定。然后对时间控制律中的控制信号施加速率约束，并通过一个带有自整定无差拍控制器的附加控制回路来增强控制拓扑结构，该控制器使用时空小波提升管动态表示。这项工作的新颖之处有三点：（1）开发自整定控制器设计方法，该方法包括将实时可调的时间高度非线性但线性可参数化的多分辨率系统表示嵌入到经典的速率约束广义预测二次最优控制结构中；（2）通过一个更复杂的时空多分辨率自整定无差拍控制回路增强时间多分辨率回路；（3）证明所提出的方法在为控制高度不确定的非线性多尺度过程生成快速递归实时算法方面的有效性。通过一个具有挑战性的化学链冷态流动跟踪控制问题的已实现时间和增强时空解的数据展示了后者。
