Design of a real-time adaptive interpolator with parameter compensation

The interpolator is the key part of a CNC system, which has strong effect on machining accuracy, tool motion smoothness, and machining efficiency. In this paper, a real-time adaptive interpolator is developed for non-uniform rational B-spline curves (NURBS) interpolation while considering the maximum acceleration/deceleration of the machine tool. In this proposed interpolator, both constant feedrate and high accuracy are achieved while the inconsistency of feedrate is dramatically reduced as well. In order to deal with the acceleration/deceleration around the feedrate-sensitive sharp corners, a look-ahead function is introduced to detect and adjust the feedrate adaptively. Furthermore, a parameter compensation scheme is proposed to eliminate the parametric truncation error which has been analyzed by several researchers but still not incorporated into any real-time interpolator so far. A case study was conducted to evaluate the feasibility of the developed interpolator.     

On the development of a parametric interpolator with confined chord error, feedrate, acceleration and jerk

A parametric interpolator for the trajectory planning of non-uniform rational basis spline (NURBS) curves is proposed in this study. The input constraints include the chord error, speed limit, acceleration limit and jerk limit. Since there is no unique representation to relate these constraints, appropriate models are developed to determine several feedrates in terms of different kinematic conditions, the minimum one of which is selected as the desired feedrate. A look-ahead stage is developed to plan a series of segment points from the curves, where a segment point represents the change in the acceleration across zero. The segment points, which enable the appropriate design of kinematic profiles, are used in real-time sampling for determining the sampling points. Several examples are presented to demonstrate the feasibility of the proposed algorithm.     

Adaptive parametric interpolation scheme with limited acceleration and jerk values for NC machining

Parametric interpolation has many advantages over the traditional linear or circular interpolation in computer numerical control (CNC) machining. The existing work in this regard is reported to have achieved constant feedrate, confined chord error and limited acceleration/deceleration in one interpolator. However, the excessive jerk still exists due to abrupt change in acceleration profile, which will cause shock to the machine as well as deteriorate the surface accuracy. In this paper, an adaptive interpolation scheme incorporating machine’s dynamics capability consideration is proposed and illustrated in details. In the proposed algorithm, the commanded feedrate is maintained at most of the time and adaptively reduced in large curvature areas to meet the demand of the machining accuracy requirement, while at the same time, the acceleration and jerk values are limited within the machine’s capabilities during the whole interpolation process. It ensures a high machining accuracy, eliminates the phenomenon of overshoot/undershoot and reduces mechanical shock to the machine tools. The real-time performance of this interpolator is also measured to demonstrate its practical application. Two non-uniform rational B-spline (NURBS) curve interpolation experiments are provided to verify the feasibility and advantages of the proposed scheme.     

A real-time NURBS motion interpolator for position control of a slide equilateral triangle parallel manipulator

This study presents a real-time non-uniform rational B-spline (NURBS) motion interpolator command generation for position control of a new design to achieve a six-degree-of-freedom (6dof) slide equilateral triangle parallel manipulator (SETPM). The existing method utilizes the approximate method to obtain the characteristics of curves of the velocity and acceleration for the feed-forward compensation in the joint space. However, the NURBS motion interpolator command generation is related to the geometric characteristics of curves including position, tangent, and curvature to the motion dynamics of the SETPM including position, velocity and acceleration for applied directly the feed-forward compensation in the global space. The inverse dynamics controller (IDC) is the power of control law. However, implementing the inverse dynamics control laws requires accurately knowing the system dynamic model parameters and computing the complete equations of motion in real time. To reduce the time for calculating the inverse dynamic, this study proposes a feed-forward compensation with feedback proportional derivate (PD) control to take the place of inverse dynamics control for reducing both the inverse dynamic calculation time and the tracking error. The proposed real-time NURBS motion interpolator command generation and feed-forward compensation with feedback PD control are also successfully applied to a slide equilateral triangle parallel manipulator on a PC-based system to achieve high motion precision.     

Development of FPGA Based NURBS Interpolator and Motion Controller with Multiprocessor Technique

The high-speed computational performance is gained at the cost of huge hardware resource,which restricts the application of high-accuracy algorithms because of the limited hardware cost in practical use.To solve the problem,a novel method for designing the field programmable gate array(FPGA)-based non-uniform rational B-spline(NURBS) interpolator and motion controller,which adopts the embedded multiprocessor technique,is proposed in this study.The hardware and software design for the multiprocessor,one of which is for NURBS interpolation and the other for position servo control,is presented.Performance analysis and experiments on an X-Y table are carried out,hardware cost as well as consuming time for interpolation and motion control is compared with the existing methods.The experimental and comparing results indicate that,compared with the existing methods,the proposed method can reduce the hardware cost by 97.5% using higher-accuracy interpolation algorithm within the period of 0.5 ms.A method which ensures the real-time performance and interpolation accuracy,and reduces the hardware cost significantly is proposed,and it’s practical in the use of industrial application.     

转台速率波动自适应抑制方案的设计

提出了一种能够有效地抑制转台速率波动的自适应补偿方法,阐述了该方法的原理、结构配置,开发了自适应控制律,进行了系统稳定性分析,并进行了仿真和试验研究。结果表明:该方法能够有效地抑制位置反馈元件周期性误差导致的速率波动,提高转台的速率平稳性。采用该方案,对转台输入200°/s的速率指令,在经过大约6 s的自适应调节时间后其速率波动由原来的5°/s降低到0.4°/s,波动量减少到自适应前的1/12;试验结果与仿真结果是一致的,验证了该方法的有效性和可行性。     

Design of adaptive backstepping congestion controller for TCP networks with UDP flows based on minimax

The congestion control problem of TCP network systems with user datagram protocol (UDP) flows is investigated in this paper. A nonlinear TCP network model with strict-feedback structure is first established. The unknown UDP flow is regarded as the external disturbance, and the maximum UDP flow is calculated by using the minimax approach. And then, a congestion control algorithm is proposed by using the adaptive backstepping approach. Meanwhile, the adaptive law is employed to estimate the unknown link capacity. The design of the adaptive law is to introduce a parameter mapping mechanism to limit the parameter identification range to a specified interval, thereby improving the estimation efficiency of the parameters. Furthermore, a state-feedback congestion controller is presented to make sure that the output of the system tracks the desired queue. The simulation results show the superiority and feasibility of the proposed method.     

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.     

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.     

Design of an adaptive super-twisting decoupled terminal sliding mode control scheme for a class of fourth-order systems

This paper proposes an adaptive super-twisting decoupled terminal sliding mode control technique for a class of fourth-order systems. The adaptive-tuning law eliminates the requirement of the knowledge about the upper bounds of external perturbations. Using the proposed control procedure, the state variables of cart-pole system are converged to decoupled terminal sliding surfaces and their equilibrium points in the finite time. Moreover, via the super-twisting algorithm, the chattering phenomenon is avoided without affecting the control performance. The numerical results demonstrate the high stabilization accuracy and lower performance indices values of the suggested method over the other ones. The simulation results on the cart-pole system as well as experimental validations demonstrate that the proposed control technique exhibits a reasonable performance in comparison with the other methods.     

Stability and tunability of an adaptive controller for one-dimensional parabolic PDE with spatially varying coefficients

This paper presents tunability analysis for a proposed model reference adaptive control algorithm for linear, one-dimensional, parabolic partial differential equations. Unknown parameters in the known system structure are either constant or spatially-varying, and distributed actuation and sensing are assumed to be available. The adaptation laws are obtained by the Lyapunov redesign method. It is shown that the concept of persistency of excitation in infinite dimensional adaptive systems should be investigated in relation to time variable, spatial variable, and boundary conditions as well. It is shown that even a constant input signal can be sufficiently rich in infinite dimensional adaptive systems in the sense that it can guarantee the convergence of parameter errors to zero.     

Anti-windup adaptive PID control design for a class of uncertain chaotic systems with input saturation

In this paper, the stabilization problem of actuators saturation in uncertain chaotic systems is investigated via an adaptive PID control method. The PID control parameters are auto-tuned adaptively via adaptive control laws. A multi-level augmented error is designed to account for the extra terms appearing due to the use of PID and saturation. The proposed control technique uses both the state-feedback and the output-feedback methodologies. Based on Lyapunov׳s stability theory, new anti-windup adaptive controllers are proposed. Demonstrative examples with MATLAB simulations are studied. The simulation results show the efficiency of the proposed adaptive PID controllers.     

Design and implementation of adaptive PI control schemes for web tension control in roll-to-roll (R2R) manufacturing

In this paper, two adaptive Proportional-Integral (PI) control schemes are designed and discussed for control of web tension in Roll-to-Roll (R2R) manufacturing systems. R2R systems are used to transport continuous materials (called webs) on rollers from the unwind roll to the rewind roll. Maintaining web tension at the desired value is critical to many R2R processes such as printing, coating, lamination, etc. Existing fixed gain PI tension control schemes currently used in industrial practice require extensive tuning and do not provide the desired performance for changing operating conditions and material properties. The first adaptive PI scheme utilizes the model reference approach where the controller gains are estimated based on matching of the actual closed-loop tension control systems with an appropriately chosen reference model. The second adaptive PI scheme utilizes the indirect adaptive control approach together with relay feedback technique to automatically initialize the adaptive PI gains. These adaptive tension control schemes can be implemented on any R2R manufacturing system. The key features of the two adaptive schemes is that their designs are simple for practicing engineers, easy to implement in real-time, and automate the tuning process. Extensive experiments are conducted on a large experimental R2R machine which mimics many features of an industrial R2R machine. These experiments include trials with two different polymer webs and a variety of operating conditions. Implementation guidelines are provided for both adaptive schemes. Experimental results comparing the two adaptive schemes and a fixed gain PI tension control scheme used in industrial practice are provided and discussed.     

基于粗糙集理论的受训人员培训效果评估研究

近年来,培训外包成为企业开发人力资源,提高自身竞争力的一个重要途径。因此,科学有效的评估受训人员的培训效果变得非常重要。目前,关于培训效果评估的研究有很多,但是尚未形成一套系统全面有效的外包受训人员培训效果评估体系,将综合评估方法粗糙集理论引入这一领域的研究也尚属空白。论文立足这一现状,首先分析外包培训项目的评估影响因素,初步构建起一套受训人员培训效果评估指标体系;再根据评估指标存在的特点,结合粗糙集理论在中评估中的优势,运用粗糙集理论从初步构建的指标体系中遴选出关键指标集,确定基于粗糙集理论的指标权重,形成综合评估模型;最后,将综合评估模型运用到实际项目中。