Adjustment and improvement of environmental parameters for fruit and vegetable fresh-keeping based on BPNN-PID control strategy
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摘要:目的
开发新的控制策略，用于解决传统控制方法果蔬保鲜环境参数因时变性、非线性、滞后性强和惯性大等特点导致的控制精度低、鲁棒性弱等问题。
方法将传统比例−积分−微分（Proportional-integral-derivative，PID）和BP神经网络（Back-propagation neural network，BPNN）算法相结合，开发一种基于BPNN-PID的控制策略，通过自主搭建的果蔬保鲜环境调控试验平台和自主设计的控制系统，研究不同控制策略对保鲜环境参数调控效果的影响。
结果基于BPNN-PID控制策略的果蔬保鲜环境控制系统，环境温度超调量为1.7 ℃、稳定时间为80 min、稳态误差为±0.2 ℃，环境相对湿度超调量为2.8%、稳定时间为55 min，相对湿度稳定维持在80%~90%范围内。与传统PID控制策略相比，BPNN-PID控制策略环境温度超调量减小了2.1 ℃、稳态误差减小了0.3 ℃、稳定时间缩短了25 min，环境相对湿度超调量减小了2.2%、稳定时间缩短了25 min，环境参数波动幅度均有所降低。
结论本文开发的果蔬保鲜环境控制系统呈现出良好的动态调整能力，具有较强的鲁棒性，控制性能明显提升，实现了保鲜环境参数的精准控制，满足果蔬保鲜贮藏要求。研究结果为果蔬保鲜环境参数调控提供了参考。
Abstract:ObjectiveThe environmental parameters of fruit and vegetable preservation are characterized by time-varying, non-linear, strong hysteresis and large inertia, which leads to the problems of low control accuracy and weak robustness of traditional control methods. The goal was to develop a new control strategy to address these problems.
MethodWe combined conventional proportional-integral-derivative (PID) and back-propagation neural network (BPNN) algorithms to develop a control strategy based on BPNN-PID. We studied the effect of using different control strategies on the regulation of freshness environment parameters through an independently built test platform for environmental regulation in fruit and vegetable preservation and an independently designed control system.
ResultThe experimental results showed that the temperature overshoot of the environment control system based on BPNN-PID control strategy was 1.7 ℃, the stable time was 80 min, and the steady-state error was 0.2 ℃. The overshoot of environmental relative humidity was 2.8%, the stable time was 55 min, and it was stable in the range of 80%–90%. Compared with conventional PID control strategy, the ambient temperature overshoot of BPNN-PID control strategy was reduced by 2.1℃, the steady-state error was reduced by 0.3 ℃, and the steady-state time was shortened by 25 min. The environmental relative humidity overshoot was reduced by 2.2%, the stabilization time was shortened by 25 min, and the fluctuation ranges of environmental parameters were reduced.
ConclusionThe system shows good dynamic adjustment ability, strong robustness and obvious improvement in control performance, which enables accurate control of environmental parameters of fruit and vegetable preservation and meets the requirements of fruit and vegetable preservation and storage. The research results can provide references for the regulation of environmental parameters of fruit and vegetable preservation.
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Keywords:
- PID /
- BPNN /
- Control system /
- Fruit and vegetable fresh-keeping /
- Environmental parameter /
- Overshoot
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图 1 果蔬保鲜运输车箱体结构示意图
1：冷凝器；2：冷凝风机；3：蒸发风机；4：回风道；5：保鲜室；6： 传感器盒；7：货物；8：支撑架；9：气流轨道；10：排水管；11：三通接头；12：积水槽；13：温度传感器；14：超声波雾化器；15：开孔隔板；16：加湿风机；17：蒸发器；18：补水箱；19：排水阀；20：变频器；21：变频压缩机；22：制冷管路；23：压力室；24：控制器；25：触摸屏；26：电子计算机
Figure 1. Schematic diagram of the box structure of the transport vehicle for fruit and vegetable fresh-keeping
1: Condenser; 2: Condensing fan; 3: Evaporating fan; 4: Return air duct; 5: Keep-freshing room; 6: Sensor box; 7: Cargo; 8: Support frame; 9: Air flow track; 10: Drain pipe; 11: Tee connector; 12: Standing water tank; 13: Temperature sensor; 14: Ultrasonic atomizer; 15: Open partition; 16: Humidifying fan; 17: Evaporator; 18: Water replenishment tank; 19: Drain valve; 20: Inverter; 21: Inverter compressor; 22: Refrigeration line; 23: Pressure chamber; 24: Controller; 25: Touch screen; 26: Electronic computer
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图 2 果蔬保鲜环境控制系统结构框架图
Figure 2. Structural framework diagram of the environmental control system for fruit and vegetable fresh-keeping
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图 3 果蔬保鲜环境控制系统的电源电路
Figure 3. Power supply circuit of the environmental control system for fruit and vegetable fresh-keeping
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图 7 基于BPNN-PID的果蔬保鲜环境控制策略
Figure 7. Environmental control strategy of fruit and vegetable fresh-keeping based on BPNN-PID
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图 8 果蔬保鲜环境温度BPNN-PID控制器
C：外部偏置常量；r(k) ：系统期望值；y(k) ：系统实际值；e(k) ：系统偏差；u(k)：计算机输出值；KP：比例系数；KI ：积分系数；KD：微分系数
Figure 8. Ambient temperature BPNN-PID controller for fruit and vegetable fresh-keeping
C: External bias constant; r(k): System expected value; y(k): Actual value of the system; e(k): System deviation; u(k):Computer output; KP: Proportional coefficient; KI: Integration coefficient; KD: Differential coefficient
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图 9 BP神经网络结构
x1：目标温度；x2：实际温度；x3：温度误差；x4：外部偏置常量；l：输入层神经元序号；m隐含层神经元序号；n：输出层神经元序号；KP ：比例系数；KI：积分系数；KD：微分系数
Figure 9. BP neural network structure
x1: Target temperature; x2: Actual temperature; x3: Temperature error; x4: External bias constant; l: Input layer neuron number; m: Hidden layer neuron number; n: Output layer neuron number; KP: Proportional coefficient; KI: Integration coefficient; KD: Differential coefficient
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图 10 基于常规PID控制策略的空载保鲜环境参数变化曲线
Figure 10. Variation curve of no-load fresh-keeping environment parameters based on conventional PID control strategy
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图 11 基于BPNN-PID控制策略的空载保鲜环境参数变化曲线
Figure 11. Variation curve of no-load fresh-keeping environment parameters based on BPNN-PID control strategy
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图 13 基于常规PID控制策略的实载保鲜环境参数变化曲线
Figure 13. Variation curves of real-load fresh-keeping environment parameters based on conventional PID control strategy
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图 14 基于BPNN-PID控制策略的实载保鲜环境参数变化曲线
Figure 14. Variation curves of real-load fresh-keeping environment parameters based on BPNN-PID control strategy
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