基于web的遥操作系统时延分析

采用预测控制算法给出了一种带有时延补偿器的新的控制结构,分别在前向通道和反馈通道设计补偿器对网络时延进行补偿.实验结果表明:带有预测器及补偿器的新的控制结构可以改善系统的动态性能,并且能够保证系统在具有时延和数据丢失的环境下的稳定性.

非线性机电系统的鲁棒及预测控制

近年来，机器人、数控机床等机电系统在国民生产及生活中得到了越来越广泛的应用，与之相对应的，机电系统的控制无形中也逐渐成为机电一体化和自动控制的研究热点。另一方面，非线性特性是任何实际系统普遍存在的现象，在机电系统中尤其如此。本文从控制器设计的角度研究非线性机电系统的两个典型问题：系统闭环优化性能的改善；控制器鲁棒性的增强。这也是当前自动控制研究领域的两个重点及热点问题。 最优性是闭环系统性能最实用的评价指标之一。最优控制及预测控制是试图实现控制性能优化的两种典型方法。但前者因本身不构成闭环而严重缺乏鲁棒性，在实践中很少能得到应用；后者作为前者理念的推广，在实践中已经得到了较为广泛地应用，并取得了不错的效果，但在实现某种程度的鲁棒性时依然面临困难。除最优性外，闭环控制的鲁棒性也是非线性系统控制中亟待解决的问题之一，这是由于实际的系统几乎不可能避免模型不确定性。 现有的非线性预测控制及鲁棒控制方法无论在方法的广泛适用性还是在可行性方面都还远非完备。据此，本论文沿鲁棒性和最优性两条主线，以典型的机电系统（无人直升机模型）为研究对象，分别进行了深入的理论和实验研究，并最终形成一种同时兼顾最优性和鲁棒性的控制器设计框架，以期在一定程度上解决现有方法中存在的问题，并为以后更深入的研究工作奠定基础。 鉴于此，本论文分别针对鲁棒控制和预测控制展开讨论，其中前者主要解决基于加速度反馈实现鲁棒控制的方法，内容为第二章和第三章；后者则旨在解决基于控制Lyapunov函数方法实现实时稳定预测控制，主要内容为第四章和第五章。本论文的具体内容安排如下： 论文的第一章综述了控制理论在鲁棒性与最优性两个方向的发展概况（主要针对非线性系统），包括其发展历史，现存方法的局限性等。从而引出本论文的研究内容及研究意义。 第二章，研究了基于加速度反馈的控制器鲁棒增强方法。在深入分析常规加速度反馈控制方法的基础上，指出其存在的三方面主要问题：代数环问题；高增益实现问题和不能用于欠驱动非线性系统等。并针对两种典型的非线性系统（以无人直升机模型为代表）将新的加速度反馈控制方法与H∞控制相结合，得到了一种能够保证输入输出稳定的扰动抑制方法。大量的仿真结果验证了方法的可行性及有效性。 随后，在第三章研究了加速度的估计问题。基于加速度反馈的鲁棒控制器增强技术得以实现的前提是加速度信号的获取，本章在分析了现有加速度估计方法存在严重的滞后问题的同时，提出了将Kalman滤波方法同牛顿预测方法相结合以改善相位滞后问题的方法。实验及仿真结果验证了方法的有效性。 第四章提出了基于控制Lyapunov函数的稳定闭环控制器设计框架。本章利用集值分析理论研究了控制Lyapunov函数具有的一些性质及其在控制器设计中的应用。随后，介绍了两种典型的根据控制Lyapunov函数设计控制器的方法。接着，将引导函数的概念引入到Freeman的逐点最小范数控制方法中，形成了一种新的利用控制Lyapunov函数设计非线性控制器的方法—广义逐点最小范数控制器。最后指出，在这种框架下，鲁棒控制器设计也可以实现，并针对三种不同的不确定性系统给出了鲁棒广义逐点最小范数控制器设计方法。 最后，在第五章，将前面提出的广义逐点最小范数控制引入到非线性预测控制中去，以期利用控制Lyapunov函数保证闭环稳定性，同时利用控制器中的参数化变量作为优化对象以减轻预测控制算法的计算负担，从而达到实时稳定预测控制的目的。另外，在这一章我们还在第二章和第四章的基础上，结合加速度反馈思想和鲁棒控制Lyapunov函数的概念，提出了一种用于扰动抑制的鲁棒实时预测控制算法。同样，仿真实验验证了方法的有效性和可行性。

AUV系统辨识与广义预测控制研究

本论文的研究内容分为两方面：AUV的建模和控制。 建模方面，主要对当前用于AUV的建模方法进行了分类及对比，给出了水动力机理建模、水动力辨识、面向目标的系统辨识三类方法的优缺点。 根据可辨识性理论，对AUV闭环系统进行分析，给出了AUV闭环系统可辨识的充分条件。为了提高辨识算法的实时性，解决辨识过程中的“数据饱和”问题，给出了改进的变步长增广卡尔曼滤波辨识算法。利用小型AUV湖上试验数据辨识出航向回路、深度回路的系统模型，通过不同的试验数据与模型预测值的相关性验证模型，试验结果表明了该算法应用于AUV闭环系统建模的可行性。 控制方面，在传统PID控制、S面控制方法基础上，借鉴单神经元PID控制思想，将积分环节加入S面控制中来简化S面PID控制算法，并通过仿真验证了算法的可行性。上述方法参数调节依赖工程经验，而广义预测控制具有对模型要求低、算法鲁棒性强、参数调节简单等优点。因此，本文对输入输出约束的广义预测控制快速算法应用于AUV系统进行仿真，通过小型AUV水池试验验证了算法的有效性。

Evaluating the impact of network density, hidden nodes and capture effect for throughput guarantee in multi-hop wireless networks

To optimize the performance of wireless networks, one needs to consider the impact of key factors such as interference from hidden nodes, the capture effect, the network density and network conditions (saturated versus non-saturated). In this research, our goal is to quantify the impact of these factors and to propose effective mechanisms and algorithms for throughput guarantees in multi-hop wireless networks. For this purpose, we have developed a model that takes into account all these key factors, based on which an admission control algorithm and an end-to-end available bandwidth estimation algorithm are proposed. Given the necessary network information and traffic demands as inputs, these algorithms are able to provide predictive control via an iterative approach. Evaluations using analytical comparison with simulations as well as existing research show that the proposed model and algorithms are accurate and effective.

State Estimation for Anaerobic Digesters using the ADM1

The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model No.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible plant operating regions. Results show that the operating state vector of the modelled anaerobic digestion process can be predicted with an overall accuracy of about 90%. This facilitates the application of state-based optimization and control algorithms on full-scale biogas plants and therefore fosters the production of eco-friendly energy from biomass.

Distributed MPC for Frequency Regulation in Multi-Terminal HVDC Grids

High Voltage Direct Current (HVDC) lines allow large quantities of power to be
transferred between two points in an electrical power system. A Multi-Terminal HVDC (MTDC) grid consists of a meshed network of HVDC lines, and this allows energy reserves to be shared between a number of AC areas in an efficient manner. Secondary Frequency Control (SFC) algorithms return the frequencies in areas connected by AC or DC lines to their original setpoints after Primary Frequency Controllers have been called following a contingency. Where multiple
TSOs are responsible for different parts of a MTDC grid it may not be possible to implement SFC from a centralised location. Thus, in this paper a simple gain based distributed Model Predictive Control strategy is proposed for Secondary Frequency Control of MTDC grids which allows TSOs to cooperatively perform SFC without the need for centralised coordination.

Energy Saving - Another Perspective for Parameter Optimization of P and PI Controllers

Modern control methods like optimal control and model predictive control (MPC) provide a framework for simultaneous regulation of the tracking performance and limiting the control energy, thus have been widely deployed in industrial applications. Yet, due to its simplicity and robustness, the conventional P (Proportional) and PI (Proportional–Integral) control are still the most common methods used in many engineering systems, such as electric power systems, automotive, and Heating, Ventilation and Air Conditioning (HVAC) for buildings, where energy efficiency and energy saving are the critical issues to be addressed. Yet, little has been done so far to explore the effect of its parameter tuning on both the system performance and control energy consumption, and how these two objectives are correlated within the P and PI control framework. In this paper, the P and PI controllers are designed with a simultaneous consideration of these two aspects. Two case studies are investigated in detail, including the control of Voltage Source Converters (VSCs) for transmitting offshore wind power to onshore AC grid through High Voltage DC links, and the control of HVAC systems. Results reveal that there exists a better trade-off between the tracking performance and the control energy through a proper choice of the P and PI controller parameters.

Optimization of Controller Parameters for Energy Saving

Among various technologies to tackle the twin challenges of sustainable energy supply and climate change, energy saving through advanced control plays a crucial role in decarbonizing the whole energy system. Modern control technologies, such as optimal control and model predictive control do provide a framework to simultaneously regulate the system performance and limit control energy. However, few have been done so far to exploit the full potential of controller design in reducing the energy consumption while maintaining desirable system performance. This paper investigates the correlations between control energy consumption and system performance using two popular control approaches widely used in the industry, namely the PI control and subspace model predictive control. Our investigation shows that the controller design is a delicate synthesis procedure in achieving better trade-o between system performance and energy saving, and proper choice of values for the control parameters may potentially save a significant amount of energy

Automatic parallelization of matricial algorithms

The introduction of parallel processing architectures allowed the real time impelemtation of more sophisticated control algorithms with tighter specifications in terms of sampling time. However, to take advantage of the processing power of these architectures the control engeneer, due to the lack of appropriate tools, must spend a considerable amount of time in the parallelizaton of the control algorithm.

Controlo clássico vs. preditivo na regulação de frequência em sistemas de potência

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo Automação e Electrónica Industrial

Controlo preditivo distribuído em edifícios para conforto térmico e alocação de cargas

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Automação e Electrónica Industrial

Controlo preditivo distribuído para conforto térmico em edifícios

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Automação e Electrónica Industrial

Retificador monofásico com fator de potência quase unitário

Dissertação para a obtenção do grau de Mestre em Engenharia Eletrotécnica Ramo de Automação e Eletrónica Industrial

Nonlinear dynamics of the patient’s response to drug effect during general anesthesia

In today’s healthcare paradigm, optimal sedation during anesthesia plays an important role both in patient welfare and in the socio-economic context. For the closed-loop control of general anesthesia, two drugs have proven to have stable, rapid onset times: propofol and remifentanil. These drugs are related to their effect in the bispectral index, a measure of EEG signal. In this paper wavelet time–frequency analysis is used to extract useful information from the clinical signals, since they are time-varying and mark important changes in patient’s response to drug dose. Model based predictive control algorithms are employed to regulate the depth of sedation by manipulating these two drugs. The results of identification from real data and the simulation of the closed loop control performance suggest that the proposed approach can bring an improvement of 9% in overall robustness and may be suitable for clinical practice.

Nonlinear dynamics of the patient’s response to drug effect during general anesthesia

In today’s healthcare paradigm, optimal sedation during anesthesia plays an important role both in patient welfare and in the socio-economic context. For the closed-loop control of general anesthesia, two drugs have proven to have stable, rapid onset times: propofol and remifentanil. These drugs are related to their effect in the bispectral index, a measure of EEG signal. In this paper wavelet time–frequency analysis is used to extract useful information from the clinical signals, since they are time-varying and mark important changes in patient’s response to drug dose. Model based predictive control algorithms are employed to regulate the depth of sedation by manipulating these two drugs. The results of identification from real data and the simulation of the closed loop control performance suggest that the proposed approach can bring an improvement of 9% in overall robustness and may be suitable for clinical practice.