Design and experimental realization of a robust decentralized PI controller for a coupled tank system

This paper presents design and realization of a robust decentralized PI controller for regulating the level of a coupled tank system. The proposed controller is designed based on a predefined reference transfer function model in which we adopt a frequency matching of actual and reference models. Realization of control algorithms for a multivariable system is often complicated owing to uncertainties in the process dynamics. In this paper, initially a frequency response fitting model reduction technique is adopted to obtain a First Order Plus Dead Time (FOPDT) model of each higher order decoupled subsystem. Further, using the obtained reduced order model, the proposed robust decentralized PI controller is designed. The stability and performance of the proposed controller are verified by considering multiplicative input and output uncertainties. The performance of the proposed robust decentralized controller has been compared with that of a decentralized PI controller. To validate the performance of the proposed control approach, real-time experimentation is pursed on a Feedback Instrument manufactured coupled tank system.     

单容水箱液位模糊控制系统的设计与研究

针对传统工业单容水箱液位控制过程中存在的稳定性差、响应慢等问题,提出了一种模糊控制方法对液位进行控制。首先根据单容水箱液位的控制要求设计了模糊控制器．然后搭建了基于远程数据采集模块的计算机控制系统．最后在VB环境下．设计了单容水箱液位监控人机界面进行液位控制。实验结果表明．在单容液位过程控制系统中．采用模糊控制与闭环P1D控制相比．稳定性更好、响应速度更快。液位控制性能明显提高。     

Experimental evaluation of adaptive three-tank level control

Liquid level control through regulation of mass flow rates is an important application in various areas of the power industry. Very often a PID controller is used for these applications. This paper compares a nonconventional PID controller and three different types of adaptive controller, a direct model reference adaptive controller (MRAC), an indirect MRAC with Lyapunov estimation, and an indirect MRAC with recursive least-squares (RLS) updating estimation, for liquid level control. By implementing all four controllers on a three-tank system, the performances of each are compared. All controllers track a sinusoidal input very well and overall exhibit somewhat varying performance. The direct MRAC and the indirect MRAC with RLS estimation give the best performance. With Lyapunov estimation and RLS estimation, all the system parameter estimates converge to the reference model values. However, RLS estimation has a much faster convergence. It is concluded that adaptive liquid level control is an improvement over traditional liquid level control when precise level control in three coupled tanks is desired.     

永磁直线伺服系统模糊PI速度控制器研究

针对永磁直线同步电机伺服系统常规PI速度调节器动态响应慢、输出超调大等问题,提出了模糊自适应PI速度控制器,对比常规PI速度控制器进行了仿真和实验。基于永磁直线同步电机矢量速度闭环控制,分析了模糊PI速度控制器和基于模糊PI控制器的伺服矢量控制系统的结构,设计了模糊PI速度控制器,在Matlab/Simulink仿真环境下,建立了基于模糊PI速度控制器的永磁直线同步电机伺服系统仿真模型,并通过实际永磁同步直线电机伺服系统实验对仿真结果进行了实验验证。研究结果表明,模糊PI速度控制器,相对于常规PI控制器,可以明显降低超调量和调节时间。将仿真结果和试验结果对比,两者基本吻合,说明模糊PI速度控制确实可以较好地改善永磁直线同步电机伺服系统的动态性能。     

Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system

This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable.     

Experimental evaluation of control performance of MPC as a regulatory controller

Proportional-integral-derivative (PID) control is widely practised as the base layer controller in the industry due to its robustness and design simplicity. However, a supervisory control layer over the base layer, namely a model predictive controller (MPC), is becoming increasingly popular with the advent of computer process control. The use of a supervisory layer has led to different control structures. In this study, we perform an objective investigation of several commonly used control structures such as ‘Cascaded PI controller’, ‘DMC cascaded to PI’ and ‘Direct DMC’. Performance of these control structures are compared on a pilot-scale continuous stirred tank heater (CSTH) system. We used dynamic matrix control (DMC) algorithm as a representative of MPC. In the DMC cascaded to PI structure, the flow-loops are regulated by the PI controller. On top of that a DMC manipulates the set-points of the flow-loops to control the temperature and the level of water in the tank. The ‘Direct DMC’ structure, as its name suggests, uses DMC to manipulate the valves directly. Performance of all control structures were evaluated based on the integrated squared error (ISE) values. In this empirical study, the ‘Direct DMC’ structure showed a promise to act as regulatory controller. The selection of control frequency is critical for this structure. The effect of control frequency on controller performance of the ‘Direct DMC’ structure was also studied.     

基于模糊干扰观测器的电动Stewart平台自适应模糊控制

建立了一个电动Stewart平台的统一动力学模型,并基于它设计了一种新型的自适应模糊控制算法。这个统一的动力学模型在任务空间中使用了Newton-Euler方法建立,同时结合了平台动力学和执行器动力学模型。自适应模糊控制算法使用计算力矩方法设计运动平台标称模型的逆动力学控制器,然后使用基于模糊干扰观测器的自适应模糊控制器对模型的不确定性和外部扰动进行补偿。通过数值仿真分析表明,在不引入高增益控制器的情况下,成功地消除了平台参数的不确定性和外部干扰的影响,保证了平台的跟踪性能。     

H∞ output-feedback fuzzy proportional-integral control of fully delayed input/output systems

This paper presents the output-feedback fuzzy proportional-integral (PI) controller design for uncertain nonlinear systems with both fully delayed input and output. Based on the Takagi–Sugeno (T–S) fuzzy model representation, the output-feedback PI control is realized via parallel distributed PI compensation and novel LMI gain design. Although the T–S fuzzy PI controller is simple, asymptotic output regulation is assured to overcome the effect of uncertainty, state delay, and full input/output delays. When considering disturbance and measurement noise, the control performance is achieved by robust gain design. Furthermore, state observers and bilinear matrix inequality conditions are removed in this paper. Finally, time-delay Chua׳s circuit system and a continuous-time stirred tank reactor are taken as applications to show the expected performance.     

交流伺服系统自适应模糊滑模控制

介绍了交流伺服系统的自适应模糊滑模控制方案.通过自适应模糊控制解决了扰动补偿问题.正是因为结合了模糊控制的逼近特性和滑模控制的鲁棒性,才使得系统对外部扰动具有很好的稳定性和鲁棒性,同时消除了抖动现象.仿真实验表明这一控制方案具有很好的控制效果.     

模糊无模型自适应控制在CSTR中的仿真研究

连续搅拌反应釜(Continuously Stirred Tank Reactor,CSTR)是工业生产中常用的设备,其温度系统具有强非线性、耦合性、时变性的特点,反应机理复杂,精确建模困难,传统控制方法很难满足生产需要。为解决上述问题,采用基于数据驱动的无模型自适应方法(Model Free Adaptive Control,MFAC)对反应釜出料温度进行控制。在SIMULINK环境下搭建反应釜数学模型,采用基本MFAC方法对其进行仿真研究,针对控制器参数调整复杂的问题,采用模糊控制方法对无模型控制器参数进行优化。仿真实验结果表明,基于模糊控制的MFAC方法改善了系统动静态性能指标,系统的抗干扰能力和自适应能力得到增强。该方法对反应釜实际生产具有一定的指导作用。     

Frequency response adaptation of PI controllers based on recursive least-squares process identification

The frequency domain approach to adaptive control or autotuning of proportional plus integral (PI) controllers is considered in this paper. The frequency response of the process is computed from an overparametrized recursive least-squares (RLS) model and used at sample intervals to tune the PI. By simulations it is shown that the frequency response estimates converge more quickly and are more constant in the presence of disturbances than the RLS coefficients from which they are calculated. The frequency response estimates are therefore more reliable for controller tuning. The method can handle concurrent gain variation of 50% and time constant or delay variations of 100%. The method is implemented and experimentally verified on a pilot distillation column.     

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.     

基于模糊PI参数自整定的无刷直流电机控制

本文通过建立无刷直流电机的数学模型,构造了无刷直流电机控制仿真模块。同时将模糊PI参数自整定控制器引入无刷直流电机控制系统中,根据系统输出误差和误差变化率实时整定PI参数,仿真结果表明,系统的动态和静态性能优越。     

一类非线性系统控制算法的设计

三容系统是一种典型的非线性、时变、强耦合系统,它能较好地模拟非线性多容器流程系统,对其液位控制算法进行研究很有实际意义。该文以三容系统的液位为被控对象,把模糊控制技术应用于控制系统的设计中。利用Matlab软件中Simulink工具箱搭建系统,通过确定模糊规则和隶属度函数,来调节模糊控制器,使之有效地控制系统的液位。仿真结果表明：与传统PID控制算法相比,模糊控制算法是实现多容器流程系统控制的一种有效算法。     

随机出水系统的水位模糊控制与仿真研究

对随机出水系统的水位的模糊控制进行了研究,设计了水位二维模糊控制系统,构建了模糊控制器,设计了模糊控制系统仿真模型图并进行了仿真分析,结果显示该控制系统跟随性较灵敏。     

Adaptive PI controller to voltage regulation in power systems: STATCOM as a case study

Static synchronous compensator (STATCOM) provides the means to improve quality and reliability of a power system as it has the functional capability to handle dynamic disturbances, such as transient stability and power oscillation damping as well as to providing voltage regulation. In this paper, a robust adaptive PI-based optimal fuzzy control strategy is proposed to control a STATCOM used in distribution systems. The proposed intelligent strategy is based on a combination of a new General Type-II Fuzzy Logic (GT2FL) with a simple heuristic algorithm named Teaching Learning Based Optimization (TLBO) Algorithm. The proposed framework optimally tunes parameters of a Proportional-Integral (PI) controller which, similar to most of other researchers regarding control of STATCOM, are in charge of controlling the device. The proposed controller guaranties robustness and stability against uncertainties caused by external disturbances or ever-changing nature of the power systems. The TLBO optimizes the parameters of the controller as well as the input and output membership functions. To validate the efficiency of the proposed controller, the obtained simulation results are compared with those of the two most recent researches applied in this field, namely, conventional Proportional Integral (PI) controller and Optimal Fuzzy PI (OFPI) controller. Results demonstrate the successfulness and effectiveness of the proposed online-TLBO General Type-2 Fuzzy PI (OGT2FPI) controller and its superiority over conventional approaches.     

Adaptive robust motion trajectory tracking control of pneumatic cylinders with LuGre model-based friction compensation

Friction compensation is particularly important for motion trajectory tracking control of pneumatic cylinders at low speed movement. However, most of the existing model-based friction compensation schemes use simple classical models, which are not enough to address applications with high-accuracy position requirements. Furthermore, the friction force in the cylinder is time-varying, and there exist rather severe unmodelled dynamics and unknown disturbances in the pneumatic system. To deal with these problems effectively, an adaptive robust controller with LuGre model-based dynamic friction compensation is constructed. The proposed controller employs on-line recursive least squares estimation（RLSE） to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. In addition, in order to realize LuGre model-based friction compensation, the modified dual-observer structure for estimating immeasurable friction internal state is developed. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology is applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping is used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Finally, the proposed controller is tested for tracking sinusoidal trajectories and smooth square trajectory under different loads and sudden disturbance. The testing results demonstrate that the achievable performance of the proposed controller is excellent and is much better than most other studies in literature. Especially when a 0.5 Hz sinusoidal trajectory is tracked, the maximum tracking error is 0.96 mm and the average tracking error is 0.45 mm. This paper constructs an adaptive robust controller     

CRUISE FUZZY CONTROL FOR AUTOMOBILE WITH CVT

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基于模糊自适应PID的摆式列车倾摆控制系统研究

针对摆式列车伺服倾摆系统大惯性、非线性和时变性等特点,结合PID和模糊控制两者的优点,提出了一种模糊自适应PID控制方法。对模糊自适应PID算法进行了理论分析,对摆式列车的简化模型做了仿真研究。结果表明,采用模糊自适应PID控制,系统的调节时间缩短,响应速度加快,抗干扰能力和适应参数变化的能力都优于常规PID控制,具有更好的动态特性和稳定性。     

RBFNDOB-based neural network inverse control for non-minimum phase MIMO system with disturbances

An adaptive control strategy combining neural network inverse controller (NNIC) with RBFN disturbance observer (RBFNDOB) is developed for a multi-input-multi-output (MIMO) system with non-minimum phase, internal and external disturbances in this paper. Since the inverse model of system is unstable due to the non-minimum phase, a pseudo-plant is constructed, then the RBFN is used to identify the inverse model of pseudo-plant, which can track the parameter variations of system. By copying the structure and parameters of the identifier, the NNIC is obtained. Cascading the NNIC with the original plant, the MIMO system can be decoupled and linearized into independent SISO systems. For the independent decoupled system, the RBFNDOB employs a RBFN to observe the external disturbances and this estimate value is used as a feed-forward compensation term in controller. The case study on ball mill grinding circuit is presented. The effectiveness of the proposed method is demonstrated by simulation results and comparisons.