运用人工神经网络预测净水厂混凝投药的研究

运用前馈式人工神经网络,通过建立网络模型并进行训练、测试,对华北地区某水厂的混凝投药量进行了预测,达到了较好的预测效果。     

浅析水厂“单因子混凝投药自控技术”应用

结合实例,分析了单因子混凝投药自控技术在水厂中的应用。     

基于MATLAB的前馈控制系统仿真与应用

在过程控制系统课程中将MATLAB仿真软件应于课程的辅助教学,比较了静态前馈和动态前馈的区别,围绕前馈控制设计了开环前馈控制系统、前馈反馈和前馈串级控制系统三种仿真实例,通过仿真实验,学生可清楚地理解前馈控制的特点及参数整定过程,加深学生对前馈过程控制系统设计及系统性能分析等理论知识的理解。     

应用FPGA控制直流电机伺服系统

文章介绍了将FPGA作为整个系统的核心处理器应用到直流电机伺服控制系统中的方法。算法采用前馈——反馈型控制算法,反馈算法采用模糊PID;对位置环、速度环、电流环的三环控制,使系统的动态性能和静态品质都得到提高;利用DSP Builder完成前馈和PID算法,并在Matlab Simulink中对系统算法进行了仿真;最后通过QuartusII完成整个系统的分析、综合、仿真以及配置。     

基于人工神经网络的盲源分离研究

本文介绍了盲源分离的神经网络模型,讨论了基于前馈神经网络和递归神经网络的最常用盲源分离算法的方法和思路。     

怎样抓好“学情”的前馈和反馈

教学过程是教与学之间的信息传递和反馈的控制过程。备课时注意运用前馈信息,了解学生掌握的旧知情况,在教学过程中适时运用即时反馈,灵活引导学生探究新知。     

饮用水安全的保护者

于水利1962年10月出生,教授,博士生导师,现任哈尔滨工业大学市政工程系主任。1983年毕业于哈尔滨建筑大学,博士后。2001-2002年作为高级访问学者应邀前往日本东京大学从事水的膜分离技术的合作研究。长期致力于水处理工程领域的研究工作,在混凝及其投药控制研究方面取得了重大、创造性的成就。1997年被建设部授予"有突出贡献的中青年专家"的称号。     

知识型员工反馈源异质性与创新绩效——基于工作环境互动的影响

利用行为的动机-机会-能力理论作为总体框架,以37家中小型新兴企业主管与327名知识型员工为研究对象,采用层级回归法检验模型,探究了反馈源异质性对知识型员工创新绩效的影响机制。研究结果表明：反馈源异质性会直接正向影响知识型员工的创新绩效;绩效氛围和开放度正向调节反馈源异质性与创新绩效的关系。研究结果强调了反馈源异质性与创新绩效的关系受到寻求反馈所感知到的工作环境的影响。     

新型还原性重金属捕集剂WY5的制备及其含铬废水处理效果

制备了重金属捕集剂WY5并用于含铬废水的处理,研究了该药剂对不同Cr6 浓度原水的还原螯合沉淀效果以及投药量、pH值对处理效果的影响。实验表明WY5具有较强的还原性,对Cr6 具有还原螯合作用,可以用简单的投药混凝的方法对含铬废水进行有效的处理,省去了采用常规深度处理含铬废水所需增加的设备和费用,具有较好的经济性和实用性。     

热电厂除氧器温度自动控制系统

氧的浓度,PH值的影响,水的温度,水中离子及水的流速都会造成锅炉的氧腐蚀,影响锅炉系统的正常运作,而氧的浓度是最主要的因素。所以除氧器在热力发电厂中起到了很重要的作用。热力除氧是应用最广的方法。为了保证除氧器能达到很好的除氧效果,就需要应用自动化控制技术来控制。本论文以热电厂的除氧器为控制对象,以除氧器的温度为被调量,选择蒸汽的流量作为被控变量,除盐水量为干扰量,设计了该前馈—反馈控制系统,并结合生产实际对各仪表进行了选型。     

Tracking control of a piezo-hydraulic actuator using input–output linearization and a Cascaded Extended Kalman Filter structure

In this paper, an innovative piezo-hydraulic actuator (PHA) is considered that is intended to realize a fully variable valve control in camless combustion engines. A nonlinear model of the hydraulic system part is presented along with linear models of the remaining system parts. Accurate tracking of desired valve trajectories as well as soft landing despite disturbance forces and measurement noise is achieved using a combined control strategy. It consists of an input–output linearization of the nonlinear part as well as feedforward and linear quadratic integral (LQI) feedback control of the linear system part. Given measurements of the valve spool and engine valve positions, a Cascaded Extended Kalman Filter (CEKF) structure provides estimates for the immeasurable states. Simulation results confirm the effectiveness of the proposed approach.     

Frequency domain precision analysis and design of sliding mode observers

Estimation precision and bandwidth of sliding mode (SM) observers are analyzed in the frequency domain for different settings of the observer design parameters. It was shown previously that the SM observer could be analyzed as a relay feedback-feedforward system. It is feedback with respect to the measured variable of the system being observed, and feedforward with respect to the control applied to the system being observed. This approach is now further extended to analysis of effects of design parameter change on observer performance. An example of SM observer design for estimation of DC motor speed from the measurements of armature current is considered in the paper. The input-output properties of observer dynamics are analyzed with the use of the locus of a perturbed relay system (LPRS) method.     

Optimal synchronization controller design for complex dynamical networks with unknown system dynamics

In this paper, the optimal synchronization controller design problem for complex dynamical networks with unknown system internal dynamics is studied. A necessary and sufficient condition on the existence of the optimal control minimizing a quadratic performance index is given. The optimal control law consists of a feedback control and a compensated feedforward control, and the feedback control gain can be obtained by solving the well-known Algebraic Riccati Equation (ARE). Especially, in the presence of unknown system dynamics, a novel adaptive iterative algorithm using the information of system states and inputs is proposed to solve the ARE to get the optimal feedback control gain. Finally, a simulation example shows the effectiveness of the theoretical results.     

Predictor-based optimal robust guaranteed cost control for uncertain nonlinear systems with completely tracking errors constraint

In this paper, a novel composite controller is proposed to achieve the prescribed performance of completely tracking errors for a class of uncertain nonlinear systems. The proposed controller contains a feedforward controller and a feedback controller. The feedforward controller is constructed by incorporating the prescribed performance function (PPF) and a state predictor into the neural dynamic surface approach to guarantee the transient and steady-state responses of completely tracking errors within prescribed boundaries. Different from the traditional adaptive laws which are commonly updated by the system tracking error, the state predictor uses the prediction error to update the neural network (NN) weights such that a smooth and fast approximation for the unknown nonlinearity can be obtained without incurring high-frequency oscillations. Since the uncertainties existing in the system may influence the prescribed performance of tracking error and the estimation accuracy of NN, an optimal robust guaranteed cost control (ORGCC) is designed as the feedback controller to make the closed-loop system robustly stable and further guarantee that the system cost function is not more than a specified upper bound. The stabilities of the whole closed-loop control system is certified by the Lyapunov theory. Simulation and experimental results based on a servomechanism are conducted to demonstrate the effectiveness of the proposed method.     

The impact of static output nonlinearities on the control strategies that humans use in command-following tasks

The results of a human-in-the-loop experiment are used to investigate the control strategies that humans use to interact with nonlinear dynamic systems. Two groups of human subjects interact with a dynamic system and perform a command-following task. The first group interacts with a linear time-invariant (LTI) dynamic system. The second group interacts with a Wiener system, which consists of the same LTI dynamics cascaded with a static output nonlinearity. Both groups exhibit improved performance over the trials, but the average of the linear group’s performance is better on more than three-fourths of the trials. A new nonlinear subsystem identification algorithm is presented and used to identify the feedback and feedforward control strategies used by the subjects in both groups. The identification results for the linear group agree with prior studies suggesting that adaptive feedforward inversion is a primary control strategy used by humans for command-following tasks. The main results of this paper address an open question of whether a similar control strategy is used for nonlinear systems. The identification results for the nonlinear group suggest that those subjects also use adaptive feedforward inversion. However, the static output nonlinearity inhibits the human’s ability to approximate the inverse.     

Adaptive neural dissipative control for markovian jump cyber-Physical systems against sensor and actuator attacks

This work addresses the issue of adaptive neural dissipative control for Markovian Jump Cyber-Physical Systems subject to output-dependent sensor and state-dependent actuator attacks. Attackers can inject false information into feedforward and feedback signals to degrade system performance or even destabilize the system. To identify and approximate attack signals, neural network technique is employed. Attacks are successfully withstood by constructing the estimated signals of these approximate functions. New adaptive state and output feedback controllers are being developed in the meantime. Then, by adapting Lyapunov function technique, sufficient conditions are provided to achieve the stochastic stability of the considered system with extended dissipation. Last, two practical examples are applied to elucidate the effectiveness of the devised adaptive neural control approaches.     

Modeling and control of a piezoelectric actuator driven system with asymmetric hysteresis

This study describes the high-precision positioning control of a system with asymmetric hysteresis. A switching system concept is adopted to describe the Preisach-type hysteresis, and a systematic modeling procedure is established to obtain the parameters of the system. A piezoelectric actuator system driven by a voltage amplifier is used to verify the modeling accuracy. A control structure, comprising a feedforward controller and a PD-type feedback controller, is used to realize the high-precision positioning control of the piezoelectric actuator driven stage. To enhance tracking control, a high-frequency modified term is incorporated into the hysteresis model. The experimental results confirm that the addition of this modified term reduces the tracking error and prevents the controlling energy from being saturated.     

A self-tuning hybrid active noise control system

Active Noise Control (ANC) systems are increasingly used to reduce environmental noises. A hybrid ANC system is a combination of feedforward and feedback structures in which the anti-noise signal is generated using both structures. Employing the advantages of feedforward and feedback structures enables a hybrid structure to have a high performance in controlling unwanted noise. The step size of filters used in ANC systems plays an essential role in its performance. Any variation from the appropriate value leads to divergence or inefficiency of the noise controller system. These parameters are often set experimentally, which is a time-consuming task. In addition, there is no guarantee that the system can adapt itself to situational changes. The objective of this paper is to propose a new self-tuning hybrid ANC method. Using this technique, the system continuously estimates the parameters during its operation. Results of computer simulations demonstrate the robustness of the proposed method.     

Backstepping-based adaptive dynamic programming for missile-target guidance systems with state and input constraints

In this paper, a novel backstepping-based adaptive dynamic programming (ADP) method is developed to solve the problem of intercepting a maneuver target in the presence of full-state and input constraints. To address state constraints, a barrier Lyapunov function is introduced to every backstepping procedure. An auxiliary design system is employed to compensate the input constraints. Then, an adaptive backstepping feedforward control strategy is designed, by which the tracking problem for strict-feedback systems can be reduced to an equivalence optimal regulation problem for affine nonlinear systems. Secondly, an adaptive optimal controller is developed by using ADP technique, in which a critic network is constructed to approximate the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation. Therefore, the whole control scheme consists of an adaptive feedforward controller and an optimal feedback controller. By utilizing Lyapunov's direct method, all signals in the closed-loop system are guaranteed to be uniformly ultimately bounded (UUB). Finally, the effectiveness of the proposed strategy is demonstrated by using a simple nonlinear system and a nonlinear two-dimensional missile-target interception system.     

絮凝法提取木薯黄浆废水中植物蛋白质的效果研究

文章采用混凝技术提取木薯黄浆废水中的植物蛋白质物质,实验通过选取最佳的混凝剂和助凝剂种类及其最佳用量,并研究p H、沉淀时间对混凝效果的影响,为木薯黄浆废水的资源化利用提供科学依据。单因素实验结果表明:分别以1%的PAC、1%的氯化铁和1%的改性玉米淀粉处理木薯黄浆废水,三种絮凝剂的最佳投加量分别为0.133g/L、0.133g/L和0.05g/L,最佳絮凝p H分别为9、2和8,三种絮凝剂的沉淀时间均大于15min时处理效果较佳,此时木薯黄浆淀粉废水的浊度去除率均到达80%以上,COD去除率达到50%。复配实验结果表明:PAC与PAM的复配投加后,PAC投加量为0.02g/L,PAM投加量为0.002g/L,浊度去除率从80%左右上升至98.2%,COD去除率上升至51.8%,复配絮凝剂相比单一絮凝剂提取植物蛋白效果更好。