Series Canal Water Level Control Using Fuzzy Neural Prediction
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Solution Overview
Problem
Existing canal water conveyance systems face challenges in accurately controlling water levels due to subjective manual operations, limitations of classic control algorithms, and simplified linear control models that fail to account for nonlinear dynamics and varying conditions, especially in multi-series canal pools.
Innovation Solution
A method utilizing a fuzzy neural network to predict and control water levels by establishing a multi-input single-output fuzzy neural network, constructing an upstream water level controller with a coupled predictive control algorithm, and optimizing control rates through a gradient optimization algorithm to generate a control strategy based on measured water level changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual operation based on operator experience is used for water level control, then operational flexibility is maintained, but control accuracy deteriorates due to subjective factors
Solution Approach 1:
The patent replaces manual mechanical operation with an automated control system comprising a fuzzy neural network model and predictive control algorithm. The system automatically determines sluice opening adjustments based on real-time water level data, eliminating subjective human factors while maintaining operational flexibility through adaptive control strategies.
Solution Approach 2:
The control system performs self-adjustment by automatically processing sensor data through the fuzzy neural network model and executing control decisions without continuous human intervention. The system serves itself by autonomously monitoring water levels, predicting future states, and adjusting sluice openings to maintain target water levels.
2Measurement precision
If the Saint-Venant equations are used as the control model, then simulation accuracy is improved, but calculation complexity increases making real-time control difficult
Solution Approach 1:
The patent extracts the essential nonlinear hydraulic relationships from the complex Saint-Venant equations and embeds them into a pre-trained fuzzy neural network model. During real-time operation, the system uses the trained model for rapid prediction without solving the full differential equations, thus extracting only the necessary control insights while avoiding computational complexity.
Solution Approach 2:
The fuzzy neural network model is trained offline using historical data and Saint-Venant equation simulations to learn the nonlinear hydraulic relationships in advance. This preliminary training phase captures the complex dynamics, allowing the system to perform fast real-time predictions without solving the equations during actual control operations.
3Productivity
If the simplified linear control model is used, then calculation speed is improved, but control accuracy deteriorates due to the steady linear relationship assumption
Solution Approach 1:
The patent implements a dynamic control model using a fuzzy neural network that adapts to changing hydraulic conditions. The model captures nonlinear relationships and time-varying characteristics of the canal system, allowing accurate predictions under varying flow conditions while maintaining real-time calculation speed through the trained network structure.
Solution Approach 2:
The system uses parameter adaptation within the fuzzy neural network to reflect changing hydraulic conditions. The model parameters are trained to represent different operating scenarios, enabling the system to accurately predict water level changes across various flow conditions without requiring complex real-time calculations.
4Device complexity
If research focuses on single canal pool control models, then model simplicity is maintained, but applicability to multi-series canal projects is limited
Solution Approach 1:
The patent develops a universal fuzzy neural network control model that can be applied to multi-series canal systems. The model incorporates upstream water level coupling effects and can handle multiple canal pools simultaneously, making it versatile for complex canal networks while maintaining the computational efficiency of neural network-based approaches.
Solution Approach 2:
The control model nests the single-pool control logic within a multi-pool framework by incorporating upstream water level coupling. Each canal pool's control is handled by the fuzzy neural network while accounting for the influence of upstream pools, creating a hierarchical structure that scales from individual to multiple pools.
Data Source
AI summary
A method for predicting and controlling a water level of a series water conveyance canal on the basis of a fuzzy neural network is disclosed. The method includes: performing the relationship between a sluice opening degree and an open canal control water level by means of a fuzzy neural network, and constructing an upstream water level controller of a coupled predictive control algorithm; solving an optimal control rate of the upstream water level controller using a gradient optimization algorithm on the basis of a control target of the upstream water level controller; and generating a control strategy by collecting actually measured water level change information and multiplying the actually measured water level change information by the optimal control rate on the basis of the solved optimal control rate, thereby fulfilling the object of predicting and controlling the water level.