模糊辨识在隧道纵向式通风控制中的应用

纵向式通风是利用射流风机的推力将新鲜空气从隧道一端送入,使污染物从另一端排出,以此保证隧道内的环境质量。但由此产生的能耗使得隧道运营成本巨大,所以需要一种合适的控制策略来控制射流风机的开启台数,使得在隧道内污染物浓度不超过规定值的前提下,开启的风机数量最少,从而达到节能的目的。将模糊辨识应用到隧道通风控制中,基于模糊C均值聚类和递推最小二乘法的原理,采用T-S模型模糊辨识方法对隧道通风系统进行了辨识;采用已辨识系统来预测隧道下一采样时刻的污染物浓度;优化开启的风机台数并将其作为控制量。仿真结果表明了该方法的有效性。     

引射式跨声速风洞流场控制软件设计

目前提出的引射式跨声速风洞流场控制软件抽气端压力过大,导致排气阀气流排气速度变化过于剧烈;设计了一种新的引射式跨声速风洞流场控制软件,在风洞的控制程序中引入了马赫数和雷诺数,对控制质量进行试验检测,以实现风洞系统能够达到更精准快速的控制水平;在风洞流场控制系统中引入了解耦系统,对风洞测试各部分参数进行解耦筛选,提高各参数的准确度,有利于控制系统实现精准控制;实验结果表明,设计的引射式跨声速风洞流场控制软件能有效降低引射式跨声速风洞流场控制软件抽气端压力,使排气阀气流排气速度处于稳定状态。     

An algorithm for non-linear space—Time nuclear reactor control

A novel multivariable control algorithm for non-linear space-time nuclear reactor dynamics is proposed in this paper. The multivariable control algorithm is based on a mathematical model of the nuclear reactor which includes: a single energy group of neutrons, delayed neutron precursors, iodine, xenon and thermal-hydraulic feedback. The multivariable control algorithm is composed of non-linear time-varying feedforward and feedback control signals, a reference model of the nuclear reactor and a dynamic observer. The non-linear proportional plus integral feedback controller forces the nuclear reactor to follow the response of the reference model. The dynamic observer estimates the unmeasurable state variables. The feedforward and feedback control signals are determined in a novel approach by specifying the form of the closed-loop response of the neutron density variables. By virtue of the multivariable control algorithm the closed-loop differential equations are linear and time-varying. A linear stability analysis for base-load and load-cycle operation indicates that the closed-loop system is stable provided that the thermal-hydraulic subsystem is inherently stable. The simulated dynamic response indicates that the multivariable control algorithm provides excellent response characteristics.     

A systematic approach for airflow velocity control design in road tunnels

This paper introduces a systematic approach to design and tune the airflow velocity control system for use during fire situations in road tunnels. The proposed approach is focused on road tunnels with a complex structure; long tunnels with connected ramps (entrances and exits), where the controller design can be challenging and time consuming. Such tunnels usually have many sections where a fire can be localized, and this makes the control task difficult. Our approach is based on a simplified one-dimensional simulation model of a tunnel, which includes all the important factors influencing the airflow dynamics of a tunnel. The proportional–integral (PI) controllers are tuned based on the Skogestad Internal Model Control (SIMC) method, which requires a simple model for the process dynamics. The case study is the airflow velocity control in the Blanka tunnel complex in Prague, Czech Republic, which is the largest city tunnel in Central Europe. The results of the paper show how to improve the control algorithm in real operation and how to use the proposed systematic approach for future tunnels.     

模糊复合控制方法在焦炉控制系统中的应用研究

针对焦炉温度的大惯性、纯滞后、非线性和时变性等特点。提出了一种新的模糊复合控制方法．它将常规的前馈控制、反馈控制与具有人工智能的模糊控制相结合。吸取了前馈控制改善系统动态响应特性、反馈控制消除稳态误差以及模糊控制能够较好地解决系统难以建立精确数学模型的优点。解决了焦炉温度控制问题．通过模糊复合控制的理论分析和仿真试验。证明了该控制方法的可行性和有效性．     

考虑能见度影响的公路隧道照明动态优化与智能控制

为解决不同能见度影响下公路隧道实际路面亮度变化过大以及由此引起的行车安全与能源虚耗问题, 本文提出了一种能够改善公路隧道照明环境的动态优化与智能控制方法. 首先, 通过对不同时空条件下的公路隧道进行现场试验和数据分析, 得到了隧道内能见度的变化规律; 其次, 在公路隧道传统照明设计的基础上考虑能见度对照明环境的影响, 建立了基于隧道内能见度、交通量、车速、路面亮度和照明亮度的按需照明与动态优化模型;随后, 以不同地区公路隧道的实测数据为样本, 结合划分出的公路隧道典型照明场景和模糊径向基神经网络算法构建了公路隧道照明智能控制模型, 最后, 通过仿真实验验证了所构建模型的有效性, 其结果表明, 本文所提出的优化控制方法能够在保证隧道照明安全性的前提下兼顾节能性.     

Two adaptive control structures of robot manipulators

This paper proposes two simple adaptive control schemes of robot manipulators. The first one is the state feedback control which consists of feedforward from the desired position trajectory, PD feedback from the actual trajectory, and an auxiliary input. The second one is the feedforward/feedback control which consists of a feedforward term from the desired position, velocity, and acceleration trajectory based on the inverse of robot dynamics. The feedforward, feedback, and auxiliary gains are adapted using simple equations derived from the decentralized adaptive control theory based on Lyapunov's direct method, and using only the local information of the corresponding joint. The proposed control schemes are computationally fast and do not require a priori knowledge of the detail parameters of the manipulator or the payload. Simulation results are presented in support of the proposed schemes. The results demonstrate that both controllers perform well with bounded adaptive gains.     

A switching control strategy applied to a solar collector field

This work presents a new switching control procedure that has been chosen to deal with the changes in plant dynamics. Several control systems composed of IMC-based PID controllers and feedforward compensators are designed for each operation region and a continuous switching mechanism for the overall control system is defined. Experimental tests which have been performed in a distributed collector field of a solar air cooling system, are presented showing promising results for the proposed strategy.     

Robust control of separated shear flows in simulation and experiment

Closed-loop flow control is gaining more and more interest in the last few years. Whereas most of the published results are based on simulation studies, this work explores the synthesis of closed-loop controllers for separated wall-bounded shear flows in experiments. A methodology and first results are presented for the robust control of a flow over a well established benchmark system, namely a backward-facing step. This system can be seen as a simple representation of the situation in a burner or behind a flame holder. Based on numerical solutions of the Navier–Stokes equations and on experimental wind tunnel results, possible candidates for an online determination of an appropriate output variable are proposed. For our purpose, a substitute for the reattachment length is taken as the control variable. Simple black-box models are derived to describe the behaviour of the system in the vicinity of set-points. Based on these, different robust controllers are synthesised. As expected, these controllers show superior behaviour over the open-loop strategies mainly proposed in the flow control literature.     

Feedforward control of glass mold colling

In the manufacture of glass containers, good temperature regulation of the forming molds is essential for a high production yield. However daily and hourly temperature fluctuations of the cooling air, drawn from outside the glass plant, upsets the temperature stability. A 1°F change in the cooling air results in a 1.4°F change in the formed glass if air flow is unchanged. Since continuous mold or glass temperature measurements are impractical under production conditions, a feedforward control system, based upon a mathematical model of mold cooling and supported by experimentation, was designed to modulate the air flow to maintain mold cooling conditions. The design is implemented using a pressure controller manipulating a valve (or blower louver), with the controller setpoint computed from the air temperature. If the computed pressure exceeds a preset limit, due to temperature extremes, an override control function changes the glass feeder temperature controller setpoint and/or the machine speed instead of pressure. In the first installation, the feedforward control system paid for itself in several days. Since then, two years of operation at one plant has shown that two containers with production yields previously at 80–82% increased an average 7.6% and six containers with production yields at 90–92% increased an average 2.3%.     

基于PLC的隧道通风监控系统

提出了一种基于PLC的隧道通风监控系统,介绍了系统的组成与功能、通信的实现,并对通风控制算法进行了详细说明。     

Thermal comfort control using a non-linear MPC strategy: A real case of study in a bioclimatic building

The aim to maintain thermal comfort conditions in confined environments may require complex regulation procedures and the proper management of an HVAC (heating, ventilation and air conditioning) system. This problem is being widely analyzed, since it has a direct effect on users’ productivity, and an indirect effect on energy saving. This paper presents a hierarchical thermal comfort control system with two layers. The upper layer includes a non-linear model predictive controller that allows to obtain a high thermal comfort level by optimizing the use of an HVAC system in order to reduce, as much as possible, the energy consumption. On the other hand, the lower layer is formed by a PID (proportional, integrative and derivative) controller with anti-windup function which is in charge of reach the setpoints calculated by the non-linear model predictive controller. In order to probe the effectiveness of the proposed control system, suitable real results obtained in a bioclimatic building are included and commented.     

Model-based motion control for multi-axis servohydraulic shaking tables

The shaking table is an essential testing tool in the development of earthquake resistant buildings and infrastructure, so improving its performance is an important contribution to saving lives. Currently the bandwidth and accuracy of shaking tables is such that earthquake motion often cannot be replicated with the desired fidelity. A new model-based motion control method is presented for multi-axis shaking tables. The ability of this method to decouple the control axes is demonstrated. A linear parameter varying modal control approach is used – i.e. the modes of vibration of the system are controlled individually, with the modal decomposition repeated at each time step to account for parameter variations. For each mode, a partial non-linear dynamic inversion is performed in the control loop. Feedback is based on a combination of position and acceleration measurements. A command feedforward method is proposed to increase the tracking bandwidth, thus the controller has a two degree-of-freedom structure. Experimental and simulation results are presented for a large (43 t total) six degree-of-freedom shaking table. The simulation results are based on a detailed, validated model of the table. Experimental results show that the controller gives exceptional performance compared a conventional proportional controller: for example the horizontal acceleration bandwidth is six-times higher at over 100 Hz, which is also many times higher than the hydraulic resonant frequency. These results will allow a step change in earthquake simulation accuracy.     

Two air path observers for turbocharged SI engines with VCT

Nowadays, downsizing is a major way to reduce fuel consumption and pollutant emissions of spark ignition (SI) engines. In downsized engines, new air path management systems such as turbocharging or variable camshaft timing (VCT) are included, and an efficient control of the air actuators is required for engine torque control. Two non-linear estimators are proposed to estimate non-measured variables of the air path. The first one is an in-cylinder air mass observer that combines feedforward neural static models and a linear parameter varying (LPV) polytopic observer. The second one is a neural estimator of the burned gas and scavenged air masses. Test bench results on a turbocharged SI engine with VCT show the real time applicability and good performance of the proposed estimators. Finally, a strategy for developing the engine supervisor is presented.     

Self-tuning of 2 DOF control based on evolving fuzzy model

In this paper we present a self-tuning of two degrees-of-freedom control algorithm that is designed for use on a non-linear single-input single-output system. The control algorithm is developed based on the Takagi-Sugeno fuzzy model, and it consists of two loops: a feedforward loop and feedback loop. The feedforward part of the controller should drive the system output to the vicinity of the reference signal. It is developed from the inversion of the T-S fuzzy model. To achieve accurate error-free reference tracking a feedback part of the controller is added. A time-varying error-model predictive controller is used in the feedback loop. The error-model is obtained from the T-S fuzzy model. The T-S fuzzy model of the system, required in the controller, is obtained with evolving fuzzy modelling, which is based on recursive Gustafson-Kessel clustering algorithm and recursive fuzzy least squares. It employs evolving mechanisms for adding, removing, merging and splitting the clusters.The presented control approach was experimentally validated on a non-linear second-order SISO system helio-crane in simulation and real environment. Several criteria functions were defined to evaluate the reference-tracking and disturbance rejection performance of the control algorithm. The presented control approach was compared to another fuzzy control algorithm. The experimental results confirm the applicability of the approach.     

城市固废焚烧过程风量智能优化设定方法

城市固体废物焚烧(municipal solid wastes incineration,MSWI)技术由于其高效的减容效果逐渐成为了生活垃圾处理的主要方式.MSWI过程产生的氮氧化物(nitrogen oxides,NOx)是大气中的主要污染物之一.为了在控制NOx排放的同时保证燃烧效率,提出一种基于多目标粒子群算法的MSWI过程风量智能优化设定方法.首先,结合最大相关最小冗余算法及前馈神经网络,建立燃烧效率和氮氧化物排放浓度预测模型;然后,提出分阶段多目标粒子群优化算法(staged multi-objective particle swarm optimization,SMOPSO),获得一次风流量和二次风流量的Pareto优化解集;此外,设计效用函数,确定一次风流量和二次风流量的最优设定值;最后,基于国内某城市固废焚烧厂的实际运行数据,验证所提方法的有效性.     

Distributed adaptive output consensus control of second-order systems containing unknown non-linear control gains

In this paper, we address the output consensus problem of tracking a desired trajectory for a group of second-order agents on a directed graph with a fixed topology. Each agent is modelled by a second-order non-linear system with unknown non-linear dynamics and unknown non-linear control gains. Only a subset of the agents is given access to the desired trajectory information directly. A distributed adaptive consensus protocol driving all agents to track the desired trajectory is presented using the backstepping technique and approximation technique of Fourier series (FSs). The FS structure is taken not only for tracking the non-linear dynamics but also the unknown portion in the controller design procedure, which can avoid virtual controllers containing the uncertain terms. Stability analysis and parameter convergence of the proposed algorithm are conducted based on the Lyapunov theory and the algebraic graph theory. It is also demonstrated that arbitrary small tracking errors can be achieved by appropriately choosing design parameters. Though the proposed work is applicable for second-order non-linear systems containing unknown non-linear control gains, the proposed controller design can be easily extended to higher-order non-linear systems containing unknown non-linear control gains. Simulation results show the effectiveness of the proposed schemes.     

Precise piston trajectory control for a free piston engine

A free piston engine removes the mechanical constraint on the piston motion by eliminating the crankshaft. The extra degree of freedom offers many advantages for reducing fuel consumption and emissions. Nevertheless, stability and robustness of the engine operation has been affected in the meantime. To ensure smooth engine operation, an active motion controller, which utilizes robust repetitive control, was developed previously to regulate the piston motion of a hydraulic free piston engine to track pre-defined trajectories. However, the long piston stroke length, high operating frequency and system nonlinearity impose challenges to precise piston motion control. Therefore, feedforward controllers are investigated in this paper to complement the repetitive control to further improve the tracking performance. The first feedforward design involves the inversion of a linear plant model that describes the dynamics of the engine operation, and the second design is based on the flatness approach, which involves the inversion of a nonlinear model of the system. The two feedforward controllers are designed and implemented on the free piston engine. The experimental and simulation results demonstrate the effectiveness of the proposed control under various operating conditions and reference piston trajectories.     

Multi-objective model-based control for an automotive catalyst

A model-based feedforward/feedback air fuel ratio controller that optimizes the oxygen storage capacity of the three-way catalyst in automotive emission control systems is presented. This work incorporates a simplified dynamic catalyst model that describes the physical behavior of oxygen chemisorption and reversible deactivation in the catalyst system. A novel aspect of this work is the use of the oxygen storage capability of the catalyst not only to minimize vehicle emissions but also to optimize engine performance and fuel economy during transient engine demand. The feedback/feedforward controller is a nonlinear model predictive controller that incorporates catalyst, engine air fuel ratio controller, and fuel system models to determine the optimal air fuel ratio target trajectory. Feedback is provided by a nonlinear moving horizon estimation strategy for the determination of the oxygen storage level of the catalyst based on air fuel ratio sensors.     

On the model-based approach to nonlinear networked control systems

The problem of model-based stabilization of a nonlinear system based on its approximate discrete-time model is addressed, under the assumption that both the feedforward and the feedback paths are subject to network-induced constraints. These constraints include irregularity of the transfer intervals, time-varying communication delays, and the possibility of packet losses. A communication protocol that copes with these constraints is proposed. A “Stability+performance recovery” result for the nonlinear model-based networked control system (NCS) is formulated and proven.Simulation results presented confirm that the proposed method improves the maximum allowable transfer interval.