Interactive Multi‐objective Dynamic Optimization of Bioreactors under Parametric Uncertainty

Model‐based optimization techniques play a key role in achieving a sustainable operation of biochemical processes. Models are an approximation of the real process under study, hence, uncertainty is inherently present and for a sustainable process operation this uncertainty should be accounted for. In practice, optimality with respect to different conflicting objectives is required and multi‐objective optimization is a valuable tool. In this article the sigma point approach is applied to account for parametric uncertainty in the frame of interactive multi‐objective bioprocess optimization.     

An analysis of adaptive planning capacity: The case of chilean water utilities

New long-term planning approaches capable of coping with uncertainties such as climate change, rapid urbanization, and changing societal values, have been put forward as a way of producing more robust and sustainable plans for the future. But is the planning practice ready for their adoption? This paper takes four key propositions from the adaptive planning literature and tests the existing capacity for adopting those propositions in the context of Chilean water utilities. We will then propose how existing capacities could be enhanced, and propose alternatives for current planning practices, highlighting the importance of implementation through experimentation.     

Robustness of strong stability of discrete semigroups

In this paper we study the robustness of strong stability of a discrete semigroup on a Hilbert space under bounded perturbations. As the main result we present classes of perturbations preserving the strong stability of the semigroup.     

A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells

Proton exchange membrane fuel cell (PEMFC) long-term prognostic facilitates reducing the time/cost of the durability tests and is a critical starting point for control/maintenance suggestions. Long short-term memory (LSTM) recurrent neural networks have excellent time series processing capabilities and are proved to be useful for the short-term prognostic of PEMFC. However, LSTM prognostic models usually suffer from accumulated errors and model recognition uncertainties, which make it difficult to break the historical degradation data limitations, resulting in unsatisfactory long-term prediction performance. To tackle the problem, this paper proposes a novel model named navigation sequence driven LSTM (NSD-LSTM) for long-term prognostic. In the strategy, a navigation sequence is firstly generated by using an autoregressive integrated moving average model with exogenous variables. The sequence is then fed iteratively into LSTM in the implementation stage to achieve long-term perdition. The proposed strategy is evaluated using the aging experimental data of two types of PEMFC under different operating conditions. The long-term prognostic performance of the proposed model and other two state-of-the-art prognostic models, namely, nonlinear autoregressive exogenous and echo state network, are evaluated through comparison experiments. The simulation and experimental results show that the proposed prognostic strategy has better long-term degradation trend prediction consistency and remaining useful life estimation robustness.     

基于RBF最小参数学习法的正流量变量泵滑模自适应控制

为了提高正流量变量泵的性能,提出基于RBF最小参数学习法的正流量变量泵滑模自适应控制方法。分析正流量变量泵电液伺服系统的动力学特性,并进行系统辨识实验获得较为精确的系统数学函数模型;基于RBF最小参数学习法设计滑模控制器,在系统参数不确定性、摩擦力干扰和系统泄漏等非线性因素的情况下实现对目标流量的跟踪响应和自适应控制;最后利用MATLAB/Simulink对正流量变量泵的控制系统性能进行仿真实验,并和传统的PID控制器和模糊PID控制器进行比较。仿真实验结果验证了所设计控制方法的可行性和有效性。     

基于模糊PID算法的挤出机机筒温度控制系统设计

针对传统塑料挤出机机筒温度控制系统存在的收敛时间长、超调量大、温度控制精度低的现象,对塑料挤出机机筒的温度控制采取模糊PID控制算法,可以使系统既具有PID控制的可靠性和快速性,又具有模糊控制的高鲁棒性和强适应性,并能消除传统温控系统中普遍存在的温度控制不确定时滞,从而确保系统具有良好的动、静态特性,保证精确控温。开发了基于西门子S7-300 PLC平台的模糊PID温度控制系统,经过现场测试,稳定时间较开关控制法缩短30%左右,超调量确保在2℃以内,达到良好的控制效果。     

贝叶斯估计器先验模型参数的迭代感知方法

充分利用先验信息是提高统计推断性能的有效途径之一.贝叶斯估计的先验信息模型参数必须在设计阶段确定下来,与待探测环境模型参数之间必然存在不一致性,从而有可能导致估计质量的下降.该文首先给出了基于估计性能的先验模型参数感知的一般性框架.基于该框架,针对白高斯噪声中直流信号的贝叶斯估计器,分析了先验失配条件下的估计性能,给出了一种先验模型参数迭代感知的算法.利用计算机仿真分析了该估计器性能对先验模型参数的敏感性和稳健性,分析了不同条件下的迭代感知过程.计算机仿真结果表明,该文给出的迭代感知方法建立了从估计性能到先验模型参数的反馈,通过估计器与待探测场景的多次交互,可以使得先验模型与当前场景模型匹配.     

Frequency frame approach on loop shaping of first order plus time delay systems using fractional order PI controller

This study proposes an analytical design method of fractional order proportional integral (FOPI) controllers for first order plus time delay (FOPTD) systems. Suggested technique obtains the general computation equations of controllers for such systems. These equations are used to tune controller parameters to meet specified frequency and phase properties to satisfy the stability of whole system. It is found that the designed controllers not only make the system stable, but also have positive effect on the performance and robustness of the system. Main contribution of the paper lays on this thought. There proposed a concept, “frequency frame” which encloses the curves between phase and gain crossover frequencies in Bode plot. Robustness of the control system can be improved by expanding or constricting the edges of this frame and flattening the curves inside the frame. Thus, any case that leads the system to instability can be avoided. Analytically derived equations are tested with proper examples and the results are shown illustratively. Advantages and disadvantages of the method are comparatively given.