基于DCS的三容水箱液位控制实验平台的研究

本课题以实验室教改项目为背景。该实验平台是将“三容水箱”为控制对象的实验装置和重点实验室“工业过程控制与仿真实验室”提供的Symphony分散控制系统连接起来而形成的闭环控制系统。可以模拟工业生产过程中的液位、流量等变量，并对这些变量进行控制、测量及观测。该综合实验平台具有强大的实验功能，能完成一阶及多阶对象。多输入-多输出解耦等实验；具有很好的人机界面功能。直观的显示出水箱液位。     

Implementation of an adaptive fuzzy compensator for coupled tank liquid level control system

In this paper, an adaptive fuzzy control (AFC) system is proposed to realize level position control of two coupled water tanks, often encountered in practical process control. The fuzzy control system includes an adaptive model identifier and controller. The gains of AFC are obtained by using the fuzzy identifier model which is defined by real system outputs and control inputs. The parameters of fuzzy identifier model are adjusted online by using recursive least square algorithm. Because the controller has a recursive form it treats model uncertainties and external disturbances in an implicit way. Thus there is no need to specify uncertainty and disturbances for this controller design in advance. A well-tuned conventional proportional integral (PI) controller is also applied to the two coupled tank system for comparison with the AFC system. Experimentation of the coupled tank system is realized in two different configurations, namely configuration #1 and configuration #2 respectively. In configuration #1, the water level in the top tank is controlled by a pump. In configuration #2, the water level in the bottom tank is controlled by the water flow coming out of the top tank. Experimental results prove that the AFC shows better trajectory tracking performance than PI controller in that the plant transient responses to the desired output changes have shorter settling time and smaller magnitude overshot/undershoot. Robustness of the AFC with respect to water level variation and capability to eliminate external disturbances are also achieved. Experimental results show that AFC is a strong and a practical choice for liquid level control.     

Experimental sensorless control for a coupled two-tank system using high gain adaptive observer and nonlinear generalized predictive strategy

In this paper an adaptive control for a coupled two-tank system is proposed. This control strategy uses a generalized predictive controller, whose main method is to minimize a multistage cost function defined over a prediction horizon. Furthermore, to implement this controller in the framework of the sensorless control from the only measurement of the liquid level in the bottom tank, an adaptive interconnected high gain observer is developed for estimating the liquid level in the upper tank and the two constant parameters of the system. Two design features are worth to be emphasized. Firstly, the control calibration is achieved through the tuning of only two scalar design parameters. Secondly, the exponential convergence to zero of the state observation and parameter estimation errors is established under a well defined condition. Finally, the theoretical results are corroborated through simulation and experimental results which highlight the efficiency and applicability of the proposed observer–controller scheme.     

液晶自适应光学系统中控制矩阵的精度分析和优化

为了提高自适应光学系统的校正精度,提出了一种新的通过改变测量响应矩阵时的调制波长来准确计算控制矩阵的方法。首先,测量去除平移项的前35项Zernike模式在不同调制波长下的实际光波位相调制曲线。然后,对每个调制波长,依次计算相对于原点的位相调制曲线,最终将找到一个与实际光波位相调制曲线斜率相差最小的位相调制曲线。其所对应的调制波长,就是测量响应矩阵时,在相应的Zernike模式下应该施加在液晶波前校正器上的调制波长。这样,对于35个Zernike模式,便可以找到35个调制波长。最后,对于每个Zernike模式即响应矩阵的每一列对相应的调制波长进行归一化,最终得到准确的控制矩阵。实验表明,用此方法后,液晶自适应光学系统的校正精度由73%提高到了95%以上。校正误差为常规方法的1/5,且此比例系数与系统的最大畸变补偿位相的大小无关。此方法极大地提高了液晶自适应光学系统的校正精度。     

基于模型参考自适应控制的直线电机位置控制

针对直线电机的强非线性和时变特性,采用模型参考自适应控制(MRAC)方法对SISO直线电机闭环位置控制器进行了研究。利用偶极子对消建立了简化的永磁直线电机二阶数学模型,提出了基于局部参数最优化MIT(梯度)方案和全局稳定性理论的Lyapunov方案下的二阶直线电机位置模型参考自适应控制器,并对自适应控制器下的直线电机闭环系统稳定性进行了分析研究。在相同的前馈加反馈的控制器下,对这两种方案下的实验结果进行了对比分析。研究结果表明,基于Lyapunov第二方法设计的二阶控制器比MIT方法下的二阶控制器更能实现对三阶点到点轨迹输入信号的快速响应和跟踪,证实了直线电机位置自适应控制的有效性。     

Decentralized adaptive control of interconnected nonlinear systems with unknown control directions

In this paper, we propose a decentralized adaptive control scheme for a class of interconnected strict-feedback nonlinear systems without a priori knowledge of subsystems' control directions. To address this problem, a novel Nussbaum-type function is proposed and a key theorem is drawn which involves quantifying the interconnections of multiple Nussbaum-type functions of the subsystems with different control directions in a single inequality. Global stability of the closed-loop system and asymptotic stabilization of subsystems' output are proved and a simulation example is given to illustrate the effectiveness of the proposed control scheme.     

Tracking control of cascade systems based on passivity: the nonadaptive and adaptive cases

A new cascade passivity-based control scheme for tracking purposes is proposed in this paper. The proposed scheme is valid for a certain class of nonlinear systems even with unstable zero dynamic, and it is also useful for regulation and stabilization purposes. The cases where all system parameters are assumed to be known (nonadaptive case) and also the case when they are unknown (adaptive case) are considered. Some simulation examples are studied to analyze the behavior of the proposed scheme.     

Internal model control structure using adaptive inverse control strategy

In this paper, we propose a new adaptive internal model control scheme based on adaptive finite impulse response filters. This scheme provides the same design procedure for both minimum and nonminimum phase plants. The plants under consideration may contain time delay. The tracking objective is accomplished for both invertible and noninvertible stable plants. The internal model of the plant and its inverse are estimated by recursive least-squares and least-mean-squares algorithms, respectively. The closed loop is designed such that the system from the reference input to the plant output can be approximately represented by a pure delay. The effect of the process zeros on the output is compensated by using adaptive finite impulse response filters. This avoids the cancellation of noncancellable zeros of the plant and forces the plant output to track the reference input with a delay. The stability of the closed loop for both minimum and nonminimum phase systems is guaranteed. Computer simulation and laboratory scale experimental results are included in the paper to demonstrate the effectiveness of the proposed method.     

MTPA control of mechanical sensorless IPMSM based on adaptive nonlinear control

In this paper, an adaptive nonlinear control scheme has been proposed for implementing maximum torque per ampere (MTPA) control strategy corresponding to interior permanent magnet synchronous motor (IPMSM) drive. This control scheme is developed in the rotor d–q axis reference frame using adaptive input–output state feedback linearization (AIOFL) method. The drive system control stability is supported by Lyapunov theory. The motor inductances are online estimated by an estimation law obtained by AIOFL. The estimation errors of these parameters are proved to be asymptotically converged to zero. Based on minimizing the motor current amplitude, the MTPA control strategy is performed by using the nonlinear optimization technique while considering the online reference torque. The motor reference torque is generated by a conventional rotor speed PI controller. By performing MTPA control strategy, the generated online motor d–q reference currents were used in AIOFL controller to obtain the SV-PWM reference voltages and the online estimation of the motor d–q inductances. In addition, the stator resistance is online estimated using a conventional PI controller. Moreover, the rotor position is detected using the online estimation of the stator flux and online estimation of the motor q-axis inductance. Simulation and experimental results obtained prove the effectiveness and the capability of the proposed control method.     

汽车道路模拟系统的模糊自适应控制及仿真

以汽车道路模拟系统为研究对象,根据模糊控制理论,将模糊控制与常规的增量式PID控制相结合,设计出一套模糊自适应控制系统.并对C级路面谱进行仿真再现,仿真结果表明,该控制器比常规PID控制器具有更高的控制精度和更好的动态性能,在道路模拟仿真试验中可以达到非常满意的效果.     

Event-triggered adaptive control of a class of nonlinear systems

In this paper, the event-triggered adaptive control for a class of nonlinear systems in Brunovsky form is considered. The sensors are event-triggered thus the states are transmitted only at the discrete triggering points, which are more efficient in using communication bandwidth. To solve this problem, we design a set of event-triggered conditions and based on which the controller and parameter estimator are designed without the ISS assumption. It is shown that the proposed control schemes guarantee that all the closed-loop signals are semi-globally bounded and the stabilization error converges to the origin asymptotically. The Zeno behavior is also excluded. Simulation results illustrate the effectiveness of our scheme.     

智能自适应逆控制在无压式淬火机上的应用

将智能控制方法与自适应逆控制思想相结合用于无压式淬火机的综合自动控制．研究建立了基于智能自适应逆控制的终冷温度预测和控制模型，经现场实际验证表明，预测控制模型具有较高的预测精度和良好的控制效果。     

一种新的自适应PID控制算法

针对大惯性工业对象，设计了一种新的自适应PID调节器控制算法并应用手工业温度控制系统中，实验结果表明，利用人工智能算法与PID自适应算法的有机结合，可以使温度控制曲线在不同的阶段平滑过渡，使系统控制过程达到最优。     

水位智能控制的实现

仿人工智能控制算法在大惯性、纯滞后系统中的应用，可克服传统PID控制的相位滞后、积分饱和，解决控制系统的稳定性及准确性的矛盾。将该控制算法应用到水位控制，取得了较好的控制效果。     

神经网络自适应控制的研究进展及展望

关于人工神经网络与自适应结合的研究，近年来已成为智能控制学科的热点之一。自适应具有强鲁棒性，神经网络则具有自学习功能和良好的容错能力，神经网络自适应控制由于较好地结合了二者的优点而具有强大的优势。本文系统地综述了神经网络自适应控制的进展，讨论了神经网络自适应的主要模型和算法，并就其存在的一些问题、应用与发展趋势进行了探讨。     

A comparison of traditional and adaptive control strategies for systems with time delay

A recently developed tuning method is compared to an adaptive Smith Predictor control strategy. The robustness of each method is considered for time-varying plant parameters. Examples with simulations are provided to compare the methods and present conclusions on the advantages and disadvantages of each.     

自适应逆控制原理应用于高精度恒流源

将自适应逆控制理论应用于高精度恒流源的设计,系统结构简单,易于实现。实验结果表明,采用自适应逆控制的方法能够使输出电流具有很高的精度和稳定性,可实际应用于仪器仪表、家用电器、传感技术和通信设备等领域。     

Observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems with unknown dead-zone input

Based on the universal approximation property of the fuzzy-neural networks, an adaptive fuzzy-neural observer design algorithm is studied for a class of nonlinear SISO systems with both a completely unknown function and an unknown dead-zone input. The fuzzy-neural networks are used to approximate the unknown nonlinear function. Because it is assumed that the system states are unmeasured, an observer needs to be designed to estimate those unmeasured states. In the previous works with the observer design based on the universal approximator, when the dead-zone input appears it is ignored and the stability of the closed-loop system will be affected. In this paper, the proposed algorithm overcomes the affections of dead-zone input for the stability of the systems. Moreover, the dead-zone parameters are assumed to be unknown and will be adjusted adaptively as well as the sign function being introduced to compensate the dead-zone. With the aid of the Lyapunov analysis method, the stability of the closed-loop system is proven. A simulation example is provided to illustrate the feasibility of the control algorithm presented in this paper.     

Model reference adaptive control for a piezo-positioning system

Piezoelectric (PZT) actuators having the characteristic of infinitely small displacement resolution are popularly applied as actuators in precision positioning systems. Due to its nonlinear hysteresis effect, the tracking control accuracy of the precision positioning system is difficultly achieved. Hence, it is desirable to take hysteresis effect into consideration for improving the trajectory tracking performance. In this paper, a model reference adaptive control scheme based on hyperstability theory is developed for a moving stage system driven by a PZT actuator. It is worth emphasizing that the controller can be constructed without a nonlinear hysteresis dynamic equation to compensate the hysteresis effect. According to simulation results, the tracking error was only nanometer order. Through experimental examinations, the tracking performance was obtained as precision as ten nanometers order which is the resolution limitation of the measurement system. The effectiveness of the proposed adaptive control scheme was validated.     

基于自调整模糊控制的汽车ABS路面识别

在基于滑移率的变路面汽车ABS模糊控制中,针对滑移率偏差及其变化率在路面识别过程中会发生较大的跳变,导致模糊控制的输入量超过其论域范围,制动压力调节较长时间失效的情况,提出了一种自调整模糊控制算法。该控制算法与常规的模糊控制算法相比,能够自动调节比例系数,使过大的滑移率偏差及其变化率尽快回到模糊控制输入量的论域范围内,更好地进行压力调节,从而将滑移率尽早的控制在最佳滑移率附近,减少制动时间和制动距离。