外文翻译--自适应神经网络补偿气动系统的控制【中英文文献译文】
外文翻译-自适应神经网络补偿气动系统的控制【中英文文献译文】,中英文文献译文,外文,翻译,自适应,神经网络,补偿,气动,系统,控制,节制,中英文,文献,译文
第21页翻译部分英文部分CONTROL OF A PNEUMATIC SYSTEM WITH ADAPTIVE NEURAL NETWORKCOMPENSATIONBY SASAN TAGHIZADEHA thesis submitted to the Department of Mechanical and Materials Engineering in conformity with the requirements for the degree of Master of Applied ScienceABSTRACTSasan Taghizadeh: Control of a Pneumatic System with Adaptive Neural NetworkCompensation. M.A.Sc. Thesis, Queens University, August, 2010.Considerable research has been conducted on the control of pneumatic systems due to theirpotential as a low-cost, clean, high power-to-weight ratio actuators. However, nonlinearities such as those due to compressibility of air continue to limit their accuracy. Among the nonlinearities in a pneumatic system, friction can have a significant effect on tracking performance, especially in applications that use rodless cylinders which have higher Coulomb friction than rodded cylinders.Compensation for nonlinearities in pneumatic systems has been a popular area of research in pneumatic system control. Most advanced nonlinear control strategies are based on a detailed mathematical model of the system. If a simplified mathematical model is used, then performance is sensitive to uncertainties and parameter variations in the robot. Although they show relatively good results, the requirement for model parameter identification has made these methods difficult to implement. This highlights the need for an adaptive controller that is not based on a mathematical model.The objective of this thesis was to design and evaluate a position and velocity controller for application to a pneumatic gantry robot. An Adaptive Neural Network (ANN) structure was mplemented as both a controller and as a compensator. The implemented ANN had online training as this was considered to be the algorithm that had the greatest potential to enhance the performance of the pneumatic system.One axis of the robot was used to obtain results for the cases of velocity and position control. Seven different velocity controllers were tested and their performance compared. For position control, only two controllers were examined: conventional PID and PID with an ANN Compensator (ANNC). The position controllers were tuned for step changes in the setpoint. Their performance was evaluated as applied to sinusoid tracking.It was shown that the addition of ANN as a compensator could improve the performance of both position and velocity control. For position control, the ANNC improved the tracking performance by over 20%. Although performance was better than with conventional PID control, it was concluded that the level of improvement with ANNC did not warrant the extra effort in tuning and implementation.Chapter 1 IntroductionControl systems exist in a virtually infinite variety, both in type of application and level of sophistication. Control Engineering can be summed up as the design and implementation of automatic control systems to achieve specified objectives under given constraints. For a complex system, the overall objectives and constraints will need to be translated into performance specifications for the various subsystems ultimately into control systems specifications for low-level subsystems. Control engineering practice includes the use of better and more efficient control design strategies for improving manufacturing processes, the efficiency of energy use, advanced automobiles, among others. The present challenge to control engineers is the modeling and control of modern, complex, interrelated systems such as navigation control systems, chemical processes and robotic systems.1.1 Problem OverviewPID is the acronym for the classical and most heavily used control algorithm. Proportional plus Integral plus Derivative (PID) control is sufficient for many control problems, particularly when there are benign process dynamics and modest performance requirements. However, there are numerous control situations in which PID control with constant gains fails to meet the requirements. For example, systems with large parameter variations are candidates for more sophisticated control structures.Considerable research has been conducted on the control of pneumatic systems due to their potential as a low-cost, clean, high power-to-weight ratio actuators. However, nonlinearities such as those due to compressibility of air continue to limit their accuracy. Among the nonlinearities in a pneumatic system, friction can have a significant effect on tracking performance, especially in applications that use rodless cylinders which have higher Coulomb friction than rodded cylinders.Compensation for nonlinearities in pneumatic systems has been a popular area of research in pneumatic system control. Most of the compensation strategies use model based algorithms. Although they show relatively good results, the requirement for model parameter identification has made these methods difficult to implement.A pneumatic gantry robot is an example of a pneumatic system that typically requires a controller more sophisticated than PID. Most advanced nonlinear control strategies are based on a detailed mathematical model of the system. If a simplified mathematical model is used, then performance is sensitive to uncertainties and parameter variations. This highlights the need for an adaptive controller that is not based on a mathematical model.1.2 ObjectivesAbu-Mallouh and Surgenor (2008) conducted research on force/velocity control of a pneumatic gantry robot for contour tracking with NN compensation. They used two Proportional Pressure Control (PPC) valves. Both simulation and experimental results were presented. However, the NN compensator was only tested by simulation. They concluded that their work demonstrated the value of NN for online compensation of nonlinear elements in a pneumatic system, but experimental verification was required. The underlying purpose of the thesis is to provide that verification.The objective of this thesis is to design and evaluate a position and velocity controller for application to one axis of the pneumatic gantry robot. An adaptive NN will be tested as both a controller and as a compensator. The implemented NN will be adaptive with online training as this is an algorithm that appears to have the greatest potential to enhance the performance of a pneumatic system. Performance of the NN will be reported quantitatively. Comparison will be made with the performance of a conventional PID controller, in order to provide a benchmark.1.3 Thesis OutlineThe organization of the thesis is as follows:Chapter 2 presents a literature review on six subjects: 1) pneumatic system control, 2) pneumatic control with compensation, 3) Neural Network (NN), 4) NN as a controller, 5) NN as a compensator and 6) online versus offline NN.Chapter 3 provides background on the apparatus including sensor calibration. Details on the Adaptive Neural Network (ANN) algorithm will also be given including the implementation and tuning.In Chapter 4 the apparatus is used to obtain results for the case of velocity control, in order to evaluate the performance of ANN as applied to one axis of the gantry robot. Seven different controllers are tested and their performance compared: 1) P-only, 2) PI, 3) PI+P, 4) ANN, 5) ANN+P, 6) P-only+ANNC (ANN compensator) and 7) PI+ANNC. For ANN and ANN+P, ANN is applied as a stand-alone controller. For P-only+ANNC and PI+ANNC, ANN is applied as a compensator.In Chapter 5 the apparatus is used to obtain results for the case of position control, in order to evaluate the performance of ANN as a compensator. It provides the tuning methodology and comparative performance results. Two controllers are tested: 1) PID and 2) PID+ANNC. The controllers are tuned for step changes in the setpoint. Their performance is evaluated as applied to sinusoid tracking.Chapter 2 Literature ReviewThis chapter presents a literature review on six subjects: 1) pneumatic system control, 2) pneumatic control with compensation, 3) Neural Network (NN), 4) NN as a controller, 5) NN as a compensator and 6) online versus offline NN.2.1 Pneumatic System ControlPneumatic actuators are difficult to control because of low bandwidth and high nonlinearity due mainly to air compressibility and Coulomb friction effects. However, relative to electrically actuated systems, pneumatic systems are cheaper and easier to maintain. This observation has led to considerable interest and research on pneumatic system control. Two specific examples will be given in this section. They were chosen as they gave quantitative and comparative performance results for different control schemes.van Varseveld and Bone (1997) implemented a fast, accurate, and inexpensive position-controlled pneumatic actuator. Figure 2-1 illustrates the pneumatic system that they used. The system used a standard rodded pneumatic cylinder (stroke = 152 mm, diameter = 27 mm) with two on/off solenoid valves. The valves were pulsed using a novel Pulse Width Modulation (PWM) algorithm which produced a very linear open-loop velocity response. Four different schemes of PWM were examined.Figure 2-1 Schematic of pneumatic control system with solenoid valves (van Varseveld and Bone, 1997)Figure 2-2 Closed-loop position controller step responses for PWM schemes (van Varseveld and Bone, 1997)Figure 2-2 shows the closed-loop position controller step response for the different PWM schemes. The results led them to use PWM Scheme 4 due to its better transient response. Then, they added basic friction compensation to the PID controller with PWM Scheme 4. Figure 2-3 illustrates the results for the PID position controller with and without friction compensation. They reported that adding a friction compensator could reduce the average of steady-state error by 40%, from 0.19 mm without the compensator to 0.11 mm with the compensator.Figure 2-3 PID position controller result with and without friction compensation(van Varseveld and Bone, 1997)Figure 2-4 Fuzzy control with P feedback for sine wave input (Chillari et al, 2001)Chillari et al (2001) conducted several experiments on pneumatic system control. They examined PID, Fuzzy, Sliding mode and Neuro-Fuzzy controllers. Experimental results for these controllers applied to different setpoint trajectories were presented. Main parts of the apparatus were: rodded pneumatic cylinder (stroke = 200 mm, diameter = 25 mm) and two pairs of on/off solenoid valves.The controllers were tested on sinusoidal, square, saw-tooth and staircase input signals. In their work, Chillari et al adopted a differential pressure (P) feedback signal in order to compensate for external disturbances and also friction forces that would act against the motion. Figure 2-4 shows the Fuzzy control with P feedback for a sine wave. Unfortunately, they did not present a figure which shows the controller without the P feedback. In addition, they introduced a NN which was able to estimate the P feedback and could be used instead of the differential pressure sensor.Figure 2-5 presents a quantitative performance comparison of the different controllers based on the standard deviation between the desired and the actual position signal in m. According to Figure 2-5, the error increases as the frequency of the signal increases. The Fuzzy controller showed slightly better performance than the PID controller. Adoption of the P feedback improved the performance of the Fuzzy controller still further. The performance of the Fuzzy controller with the NN estimate of P was comparable to that of the Fuzzy controller with real P feedback.Figure 2-5 Performance comparison for different controllers and setpoints (Chillari et al, 2001)2.1.1 Pneumatic Control with CompensationAs discussed in the previous section, van Varseveld and Bone (1997) used a basic Coulomb friction compensation combined with bounded integral control which was found to substantially reduce the steady-state error due to stiction. At zero velocity, the friction force known as stiction is largely responsible for any steady-state error. Friction compensation was disabled once the steady-state error was within a specified tolerance. The results of applying the controllers on step input and S-curve were shown in Figure 2-2 and Figure 2-3, respectively.One of the common compensators in pneumatic controls is deadzone (dead time) compensation. The deadzone is an inherent nonlinearity in pneumatic servo valves, where for a range of input control values, the valve gives no output flow. From Ning and Bone (2002), Figure 2-6 illustrates the measured relationship between the maximum cylinder force versus the valve input (Part a) and the schematic of a servo pneumatic valve showing chambers A and B (Part b). There are three situations for the spool of valve based on the valve input. First, if the valve input is greaterthan , chamber A is filling. Second, if the valve input is less than , chamber B is filling. Third, if the valve input is in between and , the applied force is less than the static friction force. In the third case, the cylinder does not move and this is called the deadzone.(a)Measured relationship between the (b) Schematic of a servo pneumaticmaximum cylinder force and the valve input valve showing chambers A and BFigure 2-6 Working principle of a servo pneumatic valve (Ning and Bone, 2002)Figure 2-7 gives the block diagram of the control system with friction compensation used in their paper. A Proportional plus Velocity plus Acceleration (PVA) position controller was adopted. A friction compensation block was added as a feedforward signal to the PVA output. Unfortunately, the authors did not provide any mechanical specifications for the apparatus. They did mention that a rodless cylinder was used.Figure 2-7 Block diagram of the PVA controller with friction compensation (Ning and Bone, 2002)In Ning and Bone (2002), when the cylinder was in the deadzone, a friction compensation term was added to the control signal to make the cylinder move until it reached the desired steady-state error value. The friction compensation parameters had to be tuned by the user. However, no tuning procedure was presented. They deployed both PV and PVA controllers. Since they used double differentiation for the acceleration feedback, significant noise was seen in the signal. The controller was set to PVA initially. It would be switched back to PV when the piston was 5 mm away from the setpoint. Figure 2-8 illustrates the experimental position and error responses of PVA/PV control where they could get a steady-state accuracy of 0.01 mm. The proportional, velocity and acceleration gains are given. No comparison of performance was presented between the controller with and without the friction compensation.Figure 2-8 Experimental position and error signals of PVA/PV position control (Ning and Bone, 2002)Ning and Bone (2005) conducted an experimental comparison of two servo pneumatic position control algorithms: PVA + feedforward (FF) + deadzone compensation (DZC) and Sliding Mode Control (SMC). They used a rodless cylinder with a Proportional Flow Control (PFC) valve. The DZC was the same as the one used in Ning and Bone (2002). Figure 2-9 gives the block diagram of the PVA+FF+DZC controller.The tracking performances were evaluated by the RMSE . The PVA+FF+DZC controller had a RMSE of 0.910 mm for a sinusoid at 0.5 Hz. For the same sinusoid tracking, SMC could Figure 2-9 Block diagram of PVA+FF+DZC controller (Ning and Bone, 2005)reduce the RMSE to 0.375 mm.Andrighetto and Bavaresco (2009) reported success in using deadzone compensation for their pneumatic apparatus. They used a pneumatic rodless cylinder (stroke = 500 mm, diameter = 25 mm) and a 5 port 3 way PFC valve (the same valve used in this thesis). Figure 2-10 shows the experimental result for a sinusoidal input where deadzone compensation is added to a tuned P-only position controller. Specifically, the input was a sinusoidal wave at 1.6 Hz and amplitude of 200 mm. The maximum error was around 70 mm without deadzone compensation which was reduced to 20 mm with the compensation (70% reduction in the error). They claimed that the deadzone compensation was fairly easy to implement. Despite this statement, they mentioned that this method is only applicable when the deadzone is known and the valve dynamics are fast enough to be neglected.Figure 2-10 P-only position controller with and without deadzone compensation中文部分自适应神经网络补偿气动系统的控制Sasan Taghizadeh本论文符合机械与材料工程学院应用科学硕士学位要求皇后大学加拿大 安大略省 金斯顿2010年9月摘 要Sasan Taghizadeh:自适应神经补偿气动系统的控制,应用科学硕士论文,皇后大学,2010年8月。气动系统的控制具有作为低成本、清洁、执行元件功重比高等优点,因而在气动控制上有越来越多的研究。然而,其精准度仍受到如压缩空气等一些非线性因素的限制。在气动系统的非线性因素中,摩擦会对跟踪性能产生显著的影响,特别是在使用系列无杆气缸的应用上,因为无杆气缸具有比有杆气缸更高的库仑摩擦。在气动系统控制方面,气动系统的非线性补偿已成为一种热门的研究领域。最先进的非线性调控方式是基于一个系统的复杂的数学模型。如果使用一个简化的数学模型,那么它的性能对自动装置的不确定性和参数变化是非常敏感的。虽然他们可以表现出相对较好的结果,但是对模型参数辨识的要求使得这些方法难以实现。这说明自适应控制器需要的并不是基于一个数学模型。本论文的目标是设计和评价位置和速度控制器并应用于气动门式自动装置。自适应神经网络(ANN)结构同时作为控制器和补偿器来实现。实施的ANN作为算法进行网上测试从而最大化地提高气动系统的性能。自动装置的一个轴用于获得每组速度和位置控制的结果。七个不同的速度控制器进行测试和性能比较。对于位置控制,只需要检测两个控制器:传统的PID和具有ANN补偿(ANNC)的PID。位置控制器在定位点调整步长变化。他们的性能评价是适用于正弦曲线跟踪。结果表明,ANN作为补偿器的引入能够改善位置和速度控制这两者的性能。对于位置控制,ANNC提高跟踪性能超过20%。尽管性能优于传统PID控制,但结论是,ANNC改进的水平在优化和实现中并没有起到额外的作用。第一章 介绍控制系统的存在形式多种多样,包括应用类型和复杂程度。控制工程可以概括为在给定的约束条件下,设计和实现自动控制系统去实现指定的目标。对于一个复杂的系统,所有的目标和约束条件都需要转化为各种子系统的性能指标最终为底层子系统的控制系统规范。控制工程实践包括使用更好、更有效的控制对策来改善制造工艺、提高能源使用效率、获得先进的发动机等。目前的挑战来控制工程师是建模和控制的现代的、复杂的、相互关联的系统,如导航控制系统、化工过程和机器人系统。控制设计策略为提高制造过程的效率的能源使用,先进的汽车等。对工程师而言,目前的挑战是是建模和控制新式的、复杂的、相互关联的系统,如导航控制系统、化工过程和自动机械系统。1.1问题综述PID是传统的和最频繁使用的控制算法Proportional plus Integral plus Derivative的缩写。比例积分微分(PID)控制是可以用于解决许多控制问题,特别是在良性过程动力学和适度的性能要求的时候。然而,还有许多控制情况下,具有恒定增益的PID控制不能满足要求。例如,对于更复杂的控制结构,具有大参数变化的系统通常只是作为备用。由于气动系统的控制具有作为低成本、清洁、高强度比的致动器的潜在应用而受到相当多的研究。然而,其精准度仍受到如压缩空气等一些非线性因素的限制。在气动系统的非线性因素中,摩擦会对跟踪性能产生显著的影响,特别是在使用系列无杆气缸的应用上,因为无杆气缸具有比有杆气缸更高的库仑摩擦。在气动系统控制方面,气动系统的非线性补偿已成为一种热门的研究领域。大多数的补偿策略是适用基于算法的模型。虽然他们可以表现出相对较好的结果,但是对模型参数辨识的要求使得这些方法难以实现。气动门式自动装置是典型的需要比PID更复杂的控制器的气动系统的例子。最先进的非线性调控方式是基于一个系统的复杂的数学模型。如果使用一个简化的数学模型,那么它的性能对自动装置的不确定性和参数变化是非常敏感的。这说明自适应控制器需要的并不是基于一个数学模型。1.2 目标Abu-Mallouh和Surgenor (2008)研究了通过NN补偿对气动门式自动装置的力/速度控制进行轮廓跟踪。他们使用两个比例压力控制(PPC)阀门。同时给出了模拟和实验结果。然而,NN补偿仅是通过模拟测试。结论是他们的工作证明了NN在气动系统非线性环节网络补偿中的值,但是需要实验验证。论文的根本目的就是提供这个验证。本论文的目标是设计和评价位置和速度控制器并应用于气动门式自动装置的一轴。自适应NN将作为控制器和补偿器来进行测试。实施的自适应NN作为算法进行进行网上测试从而最大化地提高气动系统的性能。NN的性能将会定量给出。与传统PID控制器性能作比较,以提供一个基准。1.3 论文提纲论文组织如下:第二章给出六个主题的文献综述:1) 气动系统控制,2) 补偿的气动控制,3) 神经网络(NN),4) 神经网络作为控制器,5) NN作为补偿器和 6) 在线与离线NN比较。第三章给出装置的背景,包括传感器标定。详细的自适应神经网络(ANN)算法也将给出,包括实现和优化。在第四章,设备用于获取速度控制情况的结果,用来评价是ANN应用于门式自动装置一轴的性能。七个不同的控制器进行测试和性能比较:1) P-only,2) PI, 3) PI+P, 4) ANN,5) ANN+P,6) P-only+ANNC (ANN 补偿器) 和 7) PI+ANNC。对于ANN和ANN+P,ANN用来作为独立控制器。对于P-only+ANNC和PI+ANNC,ANN用来作为补偿器。在第五章,设备是用于获取位置控制情况的结果,为了评价ANN作为补偿器的性能。给出优化方法和性能对比结果。两个控制器进行测试:1) PID 和 2) PID +ANNC。位置控制器在定位点调整步长变化。他们的性能评价是适用于正弦曲线跟踪。第二章 文献综述第二章给出六个主题的文献综述:1) 气动系统控制,2) 补偿的气动控制,3) 神经网络(NN),4) 神经网络作为控制器,5) NN作为补偿器和 6) 在线与离线NN比较。2.1 气动系统控制主要由空气压缩性和库仑摩擦效应导致的低带宽、高非线性使得气压传动装置难以控制。然而,相对于电动系统,气动系统更便宜和更容易维护。这一点引起人们对气动系统控制相当大的兴趣,对其进行研究。在这一节中将会给出两个具体的例子。选这两个例子是因为他们对不同控制方案给出了定量的性能对比的结果。van Varseveld和Bone (1997)实现了一个快速、准确而且便宜的位置控制气压传动装置。图2-1是他们用到的气动系统的原理图。该系统使用一个带有两个开/关电磁阀的标准有杆气缸(冲程=152mm,直径=27mm)。阀门是脉冲使用新颖的脉冲宽度调制(PWM)算法,产生一个完全线性的开环速度响应。测试四个不同的PWM方案。图2-1 带有电磁阀的气动控制系统的原理图。图2-2是不同的脉宽调制方案的闭环位置控制器的阶跃响应。得出的结果让他们选择PWM方案4,因其具有更好的瞬态响应。然后,他们基本摩擦补偿加入PWM方案4的PID控制器。图2-3显示的是有摩擦补偿和无摩擦补偿的PID位置控制器的结果。他们得出,添加一个摩擦补偿器可以减少40%的平均稳态误差,从无补偿器时的0.19mm到有补偿器时的0.11mm。图2-2 PWM方案下的闭环位置控制器的阶跃响应(Van Varseveld和Bone,1997)图2-3 有摩擦补偿和无摩擦补偿的PID位置控制器的结果(van Varseveld和Bone,1997)Chillari 等人(2001)对气动系统控制进行了几次实验。他们测试了PID、模糊、滑动模态和神经模糊控制器。给出了这些应用到不同定位点轨迹的控制器的实验结果。设备的主要部分是:有杆气缸(冲程=200mm,直径=25mm)和两对开/关电磁
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