An online adaptive deep neural network (DNN) scheme has been introduced for the tracking control of a nonlinear bioprocess with uncertain internal dynamics. First, a detailed controllability analysis is conducted for the Lutein bioprocess to represent the bioprocess as a nonlinear system in affine form. Next, a controller consisting of a DNN-based function approximator is designed for the nonlinear Lutein production bioprocess. It is demonstrated that closed-loop tracking control of a bioprocess for a desired yield profile is possible only with two inputs. The set point trajectory to yield maximum Lutein production is shown by the proposed online adaptive deep NN controller. The proposed controller exhibits self-learning capability under closed loop condition, due to the online learning phase. In other words, no explicit offline learning phase is required and online learning is preferred due to lack of a priori training data for approximating complex nonlinear functions. Simulation results are provided to confirm the performance of the proposed approach.

在线自适应深度神经网络（DNN）方案已被引入，用于具有不确定内部动力学的非线性生物过程的跟踪控制。首先，对叶黄素生物过程进行了详细的可控性分析，以仿射形式将生物过程表示为非线性系统。接下来，针对非线性叶黄素生产生物过程设计了由基于DNN的函数逼近器组成的控制器。已经证明，只有两个输入才可能对所需的产量曲线进行生物过程的闭环跟踪控制。提出的在线自适应深度神经网络控制器显示了产生最大叶黄素产量的设定点轨迹。由于在线学习阶段，所提出的控制器在闭环条件下具有自学习能力。换一种说法，不需要明确的离线学习阶段，并且由于缺少用于逼近复杂非线性函数的先验训练数据，因此首选在线学习。提供仿真结果以确认所提出方法的性能。