Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.     

Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.     

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.     

深冷处理系统的自适应模糊PID控制方法研究

深冷处理能极大提高工模具的耐磨性,延长其使用寿命。本文介绍了使用液氮做冷源的深冷处理控制系统的构成。由于该类型冷源的压力波动不能精确控制,影响深冷处理的效果,因此笔者提出了深冷处理系统的模糊PID的自适应控制方法。实验表明:在外界条件稳定时,模糊PID控制器同普通PID控制器效果相当,但在外界条件发生变化时,模糊PID控制器具有更快的响应速度和更好的控制精度。     

主动电磁轴承智能积分型自适应模糊控制器

将传统的PID控制器或者传统复合型模糊控制器应用到主动电磁轴承系统中,其动、静态特性之间存在着一定的矛盾,较难达到理想的控制效果。将自适应模糊控制器和智能积分器相结合,提出了智能积分型自适应模糊控制器,以一个单自由度主动电磁轴承模型为例,比较了智能积分型自适应模糊控制器、传统模糊控制器和传统PID控制器的控制性能,并进行了仿真。仿真结果证明了智能积分型自适应模糊控制器的优越性。     

Indirect predictive type-2 fuzzy neural network controller for a class of nonlinear input - delay systems

In this paper a new indirect type-2 fuzzy neural network predictive (T2FNNP) controller has been proposed for a class of nonlinear systems with input-delay in presence of unknown disturbance and uncertainties. In this method, the predictor has been utilized to estimate the future state variables of the controlled system to compensate for the time-varying delay. The T2FNN is used to estimate some unknown nonlinear functions to construct the controller. By introducing a new adaptive compensator for the predictor and controller, the effects of the external disturbance, estimation errors of the unknown nonlinear functions, and future sate estimation errors have been eliminated. In the proposed method, using an appropriate Lyapunov function, the stability analysis as well as the adaptation laws is carried out for the T2FNN parameters in a way that all the signals in the closed-loop system remain bounded and the tracking error converges to zero asymptotically. Moreover, compared to the related existence predictive controllers, as the number of T2FNN estimators are reduced, the computation time in the online applications decreases. In the proposed method, T2FNN is used due to its ability to effectively model uncertainties, which may exist in the rules and data measured by the sensors. The proposed T2FNNP controller is applied to a nonlinear inverted pendulum and single link robot manipulator systems with input time-varying delay and compared with a type-1 fuzzy sliding predictive (T1FSP) controller. Simulation results indicate the efficiency of the proposed T2FNNP controller.     

Adaptive fuzzy sliding-mode control for the Ti6Al4V laser alloying process

It is well known that surface alloying quality may vary significantly with respect to process parameter variation. Thus a feedback control system is required to monitor the operating parameters for yielding a good quality control. Since this multi-input and multi-output (MIMO) system has nonlinear coupling and time-varying dynamic characteristics, it is very difficult to establish an accurate process model for designing a model-based controller. Hence an adaptive fuzzy sliding-mode controller (AFSMC) which combines an adaptive rule with fuzzy and sliding-mode control is employed in this study. It has an on-line learning ability for responding to a system’s nonlinear and time-varying behaviours. Two adaptive fuzzy sliding-mode controllers are designed for tuning the laser power and the traverse velocity simultaneously to tackle the absorptivitiy and geometrical variations of the work pieces. The simulation results show that good surface lapping performance is achieved by using this intelligent control strategy.     

用自校正模糊滑动控制器解决数控系统中的非线性问题

随着数控系统向高速、高精度方向发展,伺服轴的非线性特性已成为影响控制器性能的重要因素。针对控制系统中的非线性问题,基于变结构的模糊滑动控制奠定了解决问题的理论基础。然而,在实际的轴控制中,由于多种非线性间的耦合效应,单纯采用模糊滑动控制存在着相应的问题。为了消除非线性特性间影响,探讨了将自校正控制引入模糊滑动控制的方法。通过仿真试验,建立了模糊滑动控制的自适应律;根据轴控制对象,设计了自校正环节的控制函数,建立了一种自校正模糊滑动控制器。该控制器不仅可利用模糊滑动控制的鲁棒特性,而且可在线调节控制器的参数。针对反向间隙与死区的仿真试验表明,自校正模糊滑动控制器执行效率更高,误差更小,可以很好地解决数控系统轴控制器中存在的非线性问题。     

自适应模糊控制方法在主动悬挂系统中的应用研究

提出了一种主动悬挂系统的自适应模糊控制方法 ,该模糊控制方法可以在线自适应调整模糊控制的有关参数。 1/ 4车辆模型作为仿真对象 ,模糊控制器可以显著地减小车辆的振动及干扰 ,提高车辆的舒适性。仿真结果表明该模糊控制方法的有效性。另外 ,当主动悬挂系统模型参数发生变化时该模糊控制器表现出良好的鲁棒性     

Robust decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale MIMO nonlinear systems and its application to AHS

This paper presents a novel observer-based decentralized hybrid adaptive fuzzy control scheme for a class of large-scale continuous-time multiple-input multiple-output (MIMO) uncertain nonlinear systems whose state variables are unmeasurable. The scheme integrates fuzzy logic systems, state observers, and strictly positive real conditions to deal with three issues in the control of a large-scale MIMO uncertain nonlinear system: algorithm design, controller singularity, and transient response. Then, the design of the hybrid adaptive fuzzy controller is extended to address a general large-scale uncertain nonlinear system. It is shown that the resultant closed-loop large-scale system keeps asymptotically stable and the tracking error converges to zero. The better characteristics of our scheme are demonstrated by simulations.     

Disturbance observer and adaptive controller design for a linear-motor-driven table system

This paper presents a disturbance observer and adaptive controller design for a direct drive motion control system. An indirect adaptive controller is implemented to achieve desired tracking performance as well as deal with system parameters variation. To reduce tracking errors, a newly designed adaptive feed-forward controller is proposed based on an on-line estimated inverse model of the linear motor drive system. A digital disturbance observer is implemented to be included in the proposed feedback-feed-forward control structure to compensate for the undesired nonlinearity and external load disturbance of the direct drive system. Experimental results show that this control scheme can achieve superior contouring accuracy, disturbance rejection and robustness under the influence of friction and cogging force.     

Discrete-time direct adaptive control for robotic systems based on model-free and if–then rules operation

An adaptive controller for a class of nonlinear discrete-time systems is proposed for robotic systems under the assumption that the parameters and structure of system dynamics are all unknown. This controller is designed with the concept of model-free adaptive control requiring only the input–output of the unknown plant. The robotic system has been generalized to be a nonaffine discrete-time system under reasonable assumptions. The adaptive scheme called fuzzy rules emulated network (FREN) is implemented as a direct controller. The IF–THEN rules for FREN have been defined by the knowledge according to the relation between input and output of the robotic system without any compensator for the unknown mathematical model or nonlinearities. The underlying physical specifications of robotic system such as the operating range, maximum joint velocity, and so on have been considered to initialize the membership functions and adjustable parameters of FREN. The adaptation scheme is developed according to convergence analysis established for both adjustable parameters and the tracking error. The performance of the proposed controller is validated by the experimental system with a 7-degrees-of-freedom robotic arm operated in velocity-mode control.     

太阳能电动车最大功率点模糊控制器设计

太阳能电动车所处环境的多变性导致了太阳能电池板的输出特性也在不断变化,光伏发电系统中采用的最大功率点跟踪控制很难在多变环境下快速、准确、高效地进行最大功率点跟踪.采用模糊控制进行太阳能电动车最大功率点的跟踪,根据太阳能电动车能量控制系统的要求,为提高系统的稳态性和鲁棒性设计了适合于太阳能电动车的带修正因子自调整MPPT模糊控制器,并设计了基于DSP的模糊控制器的硬件电路和应用控制程序.     

Fuzzy MRAC controller design for vane-type air motor systems

Air motors are widely used in the automation industry due to special requirements, such as spark-prohibited environments, the mining industry, chemical manufacturing plants, and so on. The purpose of this paper is to analyze the behavior of a vane-type air motor and to design a model reference adaptive control (MRAC) with a fuzzy friction compensation controller. It has been noted that the rotational speed of the air motor is closely related to the compressed air’s pressure and flow rate, and due to the compressibility of air and the friction in the mechanism, the overall system is actually nonlinear with dead-zone behavior. The performance of the previous controllers implemented on an air motor system demonstrated a large overshoot, slow response and significant fluctuation errors around the setting points. It is important to eliminate the dead-zone to improve the control performance. By considering the effects of the dead-zone behavior, we have developed an MRAC with fuzzy friction compensation controller to overcome the effect of the dead-zone. The following experimental results are given to validate the proposed speed control strategy.     

新型压缩机润滑油站模糊自适应PID压力控制系统

针对中国石油集团某分公司炼油厂加氢裂化装置C1102C/D压缩机新型式整体润滑油站压力控制,提出了一种模糊自适应的PID控制器,为了更好地改进PID控制器的动态和稳态性能,该控制器能够在线修改模糊PID控制器参数,经仿真和实际应用证明该方法具有良好的动静态响应特性.     

防抱制动系统参数自适应滑模变结构控制器的研究

首先针对具有参数不确定性的二阶非线性系统提出了自适应滑模变结构的控制算法 ,该算法的基本思想是用自适应策略来估计不确定系统的参数 ,根据估计出的参数值 ,来设计滑模控制器 ,优点是无须事先已知不确定参数的边界 ,并且由于在自适应变结构控制采用了消颤措施 (增加了消颤项 ) ,能削弱常规滑模控制所引起的颤振现象 ,也能提高单纯的自适应控制的鲁棒性能。而后将这一控制策略应用于防抱死制动系统 (ABS)的研究中 ,设计了防抱死制动系统的自适应滑模变结构控制器 ,通过计算机仿真 ,验证了该控制方案在 ABS应用中的可行性和有效性     

A Novel Adaptive Fuzzy Controller for Application in Autonomous Vehicles

１INTRODUCTIONMostofthecurrentresearchonadaptivefuzzycontrolonlytunestheparametersoftheconsequencesoffuzzyrules．Thismaycausetheapproximationpropertyoffuzzysystemsnottobegood熏andaffecttheperformanceofthecon鄄troller．Aimingatthisproblem熏wehopetotuneallparam鄄etersoffuzzyrules．Inordertotunetheseparameters熏lin鄄earrelationshipbetweenapproximationerrorandallparam鄄etersoffuzzyrulesisestablishedfirst．ThenwedesigntheadaptivelawsoftheseparametersbasedonLyapunovsyn鄄thesisapproach．Theadva…     

Intelligent control of gas-assisted injection molding control systems

This study described the design and construction of gas-assisted injection molding systems incorporating a traditional injection molding machine. This combined system is called a gas-assisted injection molding control system (GAIMCS). The mathematical model of GAIMCS with nonlinear dynamics is difficult to establish accurately. Therefore, model-free intelligent control strategies were developed to control this system and evaluate its control performances. This work presents two intelligent control strategies: (1) traditional fuzzy controller (TFC), and (2) grey prediction fuzzy controller (GPFC). The GAIMCS was controlled by the GPFC, which was compared to a TFC to evaluate the system control performance. The GPFC achieves better control performances in accelerating rise time, and it reduces the system steady-state error better than the TFC for high-pressure gas control in GAIMCS, based on the verified experimental results.     

Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs

In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results.     

模糊逻辑控制器应用于四辊冷轧机液压弯辊系统的研究

为实现四辊冷轧机液压弯辊系统的精确控制，设计应用于一种模糊逻辑控制器。仿真结果表明，模糊控制是对液压弯辊进行控制的有效工具，大大提高了液压弯辊系统的动态响应速度及对目标辊力给定值的跟踪精度。