Design of multi-parameter model-free adaptive control algorithm for pig house environment
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摘要:目的
针对猪舍环境控制中场景复杂、多参数控制难以及系统呈现耦合特性的问题，基于无模型自适应控制(Model-free adaptive control, MFAC)算法，设计了适用于猪舍的多参数无模型自适应控制(Multi-parameter model-free adaptive control, MMFAC)算法。
方法通过将MFAC算法与用于尺度伸缩变换的权值矩阵相融合，设计MMFAC算法。该算法利用紧格式动态线性化(Compact form dynamic linearization, CFDL)技术和最优化数学方法，进行伪雅可比矩阵(Pseudo-jacobian matrix, PJM)的参数辨识和调控装置的控制量计算。
结果仿真试验结果表明，MMFAC算法具备对多参数进行自适应控制的能力，并可通过调整权重矩阵来降低关键参数的误差。真实猪舍环境控制试验结果表明，该算法可根据实时环境参数测量值计算风机控制量，并根据该控制量驱动风机，以保持环境参数在舒适区间范围内的稳定控制。
结论MMFAC算法展现出良好的多参数自适应控制能力，能够适应复杂的猪舍环境，有效解决多参数控制难以及系统耦合的问题，在猪舍环境控制中具有较好的应用前景。
Abstract:ObjectiveAiming at the problems of complex scenarios, difficult multi-parameter control and the coupling characteristics of the system in the environmental control of pig house, based on the model-free adaptive control (MFAC) algorithm, a multi-parameter model-free adaptive control (MMFAC) algorithm applicable to pig house was designed.
MethodThe MMFAC algorithm was designed by integrating the MFAC algorithm with the weight matrix used for scale scaling transformation. The algorithm utilized the compact form dynamic linearization (CFDL) technique and the optimization mathematical method to identify the parameters of the pseudo-jacobian matrix (PJM) and calculate the control volume of the control device.
ResultThe simulation experiment results showed that the MMFAC algorithm had the ability of adaptive control of multiple parameters, and could reduce the error of key parameters by adjusting the weight matrix. The results of real pig house environment control experiment showed that the algorithm could calculate the fan control quantity according to the real-time environmental parameter measurements, and drive the fan according to this control quantity to keep the environmental parameters in the range of the comfort zone for stable control.
ConclusionThe MMFAC algorithm shows good multi-parameter adaptive control ability, can adapt to the complex pig house environment, and effectively solves the problems of difficult multi-parameter control as well as system coupling, which has a good application prospect in pig house environment control.
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Keywords:
- Pig house environment /
- Multi-parameter control /
- Adaptive control /
- Coupled system /
- MFAC
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图 2 MMFAC和PID算法对恒定期望信号的跟踪能力
Figure 2. The tracking capability of the MMFAC and PID algorithms for constant desired signals
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图 3 MMFAC对耦合系统的控制能力
Figure 3. The control capability of the MMFAC algorithm for coupled system
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图 4 权重矩阵对耦合系统控制性能的影响
在a1和a2中，输出y1分配了高权重；在b1和b2中，输出y2分配了高权重
Figure 4. The effect of the weight matrix on the control performance of coupled system
In a1 and a2, high weights were assigned to output y1; In b1 and b2, high weights were assigned to output y2
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