The control of constrained nonlinear liquid level systems is a problem of fundamental importance in pharmaceutical, chemical, food-processing, oil refining and natural liquid gas separation industries. This paper proposes a novel control strategy for the control of such constrained high-dimensional interacting liquid level systems. The nonlinear liquid level regulation problem is formulated as a path planning problem in high-dimensional state space where constraint satisfaction is viewed as obstacle avoidance. An approximate control policy to steer the system to the goal state while satisfying numerous level and flow-rate constraints is computed using the famous RRT path planning algorithm which can efficiently explore non-convex spaces. To further improve performance a neural network was trained to generalize the approximate control policy computed by the RRT to unexplored states and provide smooth control. The generalized control policy learnt by the neural network is then used to achieve large changes in state and bring the system close to the goal state after which computationally cheap linear control is used to keep the system close to the goal state. The effectiveness of the proposed ANN-RRT control approach is demonstrated by applying it to the control of constrained high dimensional 5, 10, 20, 30 and 50 interacting tank systems. Experimental results for a highly interacting quadruple tank system indicate that the ANN-RRT algorithm significantly outperforms alternate approaches like PID, Fuzzy control, MPC, IMC and SMC from recent literature.

约束非线性液位系统的控制是制药，化学，食品加工，炼油和天然气液分离行业中极为重要的问题。本文提出了一种新颖的控制策略来控制这种受限的高维相互作用液位系统。非线性液位调节问题被表述为高维状态空间中的路径规划问题，其中约束满足被视为避障。使用著名的RRT路径规划算法，可以有效地探索非凸空间，从而计算出一种近似控制策略，以在满足众多级别和流量约束的同时将系统引导至目标状态。为了进一步提高性能，对神经网络进行了训练，以将RRT计算出的近似控制策略推广到未探索状态并提供平滑控制。然后，通过神经网络学习的广义控制策略可用于实现状态的大变化，并使系统接近目标状态，然后使用计算成本低廉的线性控制来使系统接近目标状态。通过将其应用于受约束的高尺寸5、10、20、30和50相互作用罐系统的控制，可以证明所提出的ANN-RRT控制方法的有效性。高度交互的四缸系统的实验结果表明，ANN-RRT算法的性能明显优于最新文献中的PID，模糊控制，MPC，IMC和SMC等替代方法。