The Active Disturbance Rejection Control (ADRC) has been employed in many industrial applications in recent year. One of the key issues when applying ADRC to industrial process control is determining controller parameters. This paper presents a new robust optimal tuning rule for first order reduced ADRC for the industrial processes which can be approximated to First Order Plus Dead Time (FOPDT) model. The tuning rule is derived to achieve a good control performance and robustness of the closed loop system by minimizing the Integrated Absolute Error (IAE) or Integrated Time-weighted Absolute Error (ITAE) under robustness constraints. It is formulated as an optimization problem with the strong nonlinear inequality constraints such as stability margin and maximum sensitivity function constraint. It is difficult to solve these optimization problems analytically. From this, the optimization problem is effectively solved by using the Particle Swarm Optimization - Differential Evolution (PSO-DE) hybrid intelligence algorithm, which is one of the swarm intelligent optimization techniques. The validity of the proposed tuning rule is verified via simulations for the benchmark processes and the temperature control for yeast culture process.

中文翻译：

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基于一阶加死区时间模型近似的减少型自扰抑制控制器的鲁棒优化调节

近年来，有源抗扰控制（ADRC）已在许多工业应用中得到采用。将 ADRC 应用到工业过程控制时的关键问题之一是确定控制器参数。本文提出了一种新的工业过程一阶简化 ADRC 稳健优化调整规则，可近似为一阶加死区时间 (FOPDT) 模型。推导出调节规则，通过在鲁棒性约束下最小化积分绝对误差（IAE）或积分时间加权绝对误差（ITAE）来实现闭环系统的良好控制性能和鲁棒性。它被表述为具有强非线性不等式约束（例如稳定裕度和最大灵敏度函数约束）的优化问题。解析地解决这些优化问题是很困难的。由此可见，采用群体智能优化技术之一的粒子群优化-差分进化（PSO-DE）混合智能算法有效地解决了优化问题。通过对基准过程和酵母培养过程的温度控制的模拟，验证了所提出的调整规则的有效性。