含磁滞补偿的电液伺服系统预设性能跟踪控制

针对电液伺服系统普遍存在的参数不确定性、不确定非线性(磁滞、摩擦、外干扰等),提出一种基于自适应鲁棒控制的含磁滞补偿的预设性能跟踪控制策略。以阀控单出杆液压缸位置伺服系统为例,首先建立了含磁滞非线性的系统数学模型,然后通过定义预设性能函数,实现了对跟踪误差收敛速率、最大超调量和稳态精度的预先规划,基于规划后的转换误差设计了自适应鲁棒控制器,并提高了稳态和瞬态跟踪性能。仿真对比结果表明：该控制策略可以减小磁滞对系统跟踪精度的影响,提高跟踪误差的收敛速度,减小最大超调量,最终实现优良的跟踪性能。     

A Self-Organising Fuzzy Logic Controller for a Coordinate Machine

For a 3D coordinate measurement system, the dynamic accuracy of the moving table will influence the measuring accuracy directly. If a classical PID controller were designed for this measuring table without an accurate mathematical model, the gain parameters may need to be regulated frequently by trial-and-error to obtain the precise motion control objective, good adaptability, and robustness. In this paper, a model-free fuzzy controller and a self-organising fuzzy controller (SOFC) were employed to eliminate the above controller design problems and improve the tracking control accuracy. The control performances of these intelligent control strategies were compared, based on the experimental results. The SOFC has the best tracking accuracy and its learning ability significantly reduces the trial-and-error design effort of a traditional fuzzy controller.     

超磁致伸缩致动器的小脑神经网络前馈逆补偿-模糊PID控制

针对超磁致伸缩致动器(GMA)在精密致动控制中存在的迟滞和位移非线性,提出了小脑神经网络(CMAC)前馈逆补偿结合模糊PID控制的新策略。通过小脑神经网络(CMAC)学习获得超磁致伸缩致动器动态逆模型用于对超磁致伸缩致动器迟滞非线性进行补偿;利用模糊PID控制降低小脑神经网络(CMAC)学习时的误差和抑制扰动,提高系统的跟踪控制性能,从而实现超磁致伸缩致动器的精密致动控制。仿真和实验结果表明:所采用的控制策略有效地消除了迟滞非线性的影响,系统的跟踪误差降低到了5%以下,而位移跟踪误差均方差仅为0.58。此外,这种策略的特点是学习和控制同时进行,控制系统能够适应被控对象动态特性的变化,使系统具有较强的鲁棒性,同时也能够有效地抑制外界的干扰,提升系统的自适应控制性能。     

Precision tracking control of a piezoelectric-actuated system

In this paper, precision tracking control of piezoelectric-actuated systems is discussed. In order to obtain precision tracking control, a modified Prandtl–Ishlinskii (MPI) model is used to model the hysteresis nonlinearity. Then, the inverse MPI model is used to reduce the hysteresis nonlinearity, and a sliding-mode controller is used to compensate for the remaining nonlinear uncertainty and disturbances. In general, the piezoelectric-actuated system can be modeled as a linear model coupled with a hysteresis. When the linear model is identified, it is used to design the sliding-mode controller. Finally, this design method is applied to the motion control of a nano-stage, and experimental results are presented to verify the usefulness of this method.     

Practical motion control with acceleration reference for precision motion—New NCTF control and its application to non-contact mechanism

This paper presents an improved practical controller for enhancing precision motion performance. For practical use, high motion control performance and ease of controller design are desired. A nominal characteristic trajectory following control (NCTF control) has been studied to satisfy the desired response. The NCTF controller consists of a nominal characteristic trajectory (NCT) which is the reference motion of control system and a compensator which makes the motion of the controlled object to follow the NCT. The NCT is easily determined from experimental open-loop time responses of the mechanism. The controller parameters can be also determined easily, without any given model parameters. In the present paper, the Continuous Motion NCTF controller for high continuous motion performance is improved in order to enhance the following characteristic of the object motion on NCT and improve the positioning and tracking accuracies of the system. The improved Continuous Motion NCTF controller (referred to as Acceleration Reference-Continuous Motion NCTF controller (AR-CM NCTF controller)) provides the advantages such as the high overshoot reduction characteristics and the low sensitivity disturbance. The AR-CM NCTF controller includes the structure of the Continuous Motion NCTF controller and acceleration reference for the object motion as the additional controller elements. The design procedure of the AR-CM NCTF controller remains easy and practical. In order to confirm the advantages, the AR-CM NCTF controller was examined in positioning and tracking motion performances using the non-contact mechanism. The experimental results prove that the AR-CM NCTF controller achieves the better positioning and tracking performances than the Continuous Motion NCTF controller.     

Ultra-precise tracking control of piezoelectric actuators via a fuzzy hysteresis model

In this paper, a novel Takagi-Sugeno (T-S) fuzzy system based model is proposed for hysteresis in piezoelectric actuators. The antecedent and consequent structures of the fuzzy hysteresis model (FHM) can be, respectively, identified on-line through uniform partition approach and recursive least squares (RLS) algorithm. With respect to controller design, the inverse of FHM is used to develop a feedforward controller to cancel out the hysteresis effect. Then a hybrid controller is designed for high-performance tracking. It combines the feedforward controller with a proportional integral differential (PID) controller favourable for stabilization and disturbance compensation. To achieve nanometer-scale tracking precision, the enhanced adaptive hybrid controller is further developed. It uses real-time input and output data to update FHM, thus changing the feedforward controller to suit the on-site hysteresis character of the piezoelectric actuator. Finally, as to 3 cases of 50 Hz sinusoidal, multiple frequency sinusoidal and 50 Hz triangular trajectories tracking, experimental results demonstrate the efficiency of the proposed controllers. Especially, being only 0.35% of the maximum desired displacement, the maximum error of 50 Hz sinusoidal tracking is greatly reduced to 5.8 nm, which clearly shows the ultra-precise nanometer-scale tracking performance of the developed adaptive hybrid controller.     

混合输入机构的模糊自适应变结构控制

针对混合输入机构中常速电机可不可控的特点,提出了基于常速电机位置跟踪的控制策略来对伺服电机进行控制,对常速电机的速度波动进行补偿,并给出了控制框图。因为系统的精确动力学模型难以获得,故考虑系统参数的不确定、外部扰动和非线性摩擦,设计了模糊自适应滑模变结构控制器以实现混合输入机构的轨迹跟踪。应用模糊自适应推理逼近系统的不确定之和,从而得到连续的控制增益,消除了变结构控制的抖振。     

永磁同步电机的模糊滑模控制

为了实现高性能永磁同步电动机伺服系统快速而精确的位置跟踪控制,在滑模控制策略中引入模糊控制算法,设计了基于模糊规则的滑模控制器;并通过理论分析和控制仿真,证实了模糊滑模控制很好地解决了抖振问题,对参数变化和负载扰动具有很好的鲁棒性,永磁同步电机可获得很好的位置跟踪效果。     

Optimal design of motion control for scan tracking measurement: A CMAC approach

This paper is concerned with the optimal design of motion control for scan tracking measurement using Cerebellar Model Articulation Controller (CMAC) neural networks in order to improve the measuring efficiency while maintaining the measurement accuracy, and to achieve friction compensation. Aiming at the effect of geometric shape and material friction of the model surface on the precision and efficiency of scan tracking measurement, technologies of model surface geometric and friction feature identification and quantification are researched. A novel optimal motion controller for scan tracking measurement is designed and realized, which automatically predicts the surface features (including geometric feature and friction feature) and adjusts the scan tracking velocity in advance. The approach to friction quantification and compensation in measuring process is given specifically. Finally, through Matlab simulation experiments, the realizability and application effect of the studied optimal motion control method for scan tracking measurement are verified, and the measuring efficiency is increased by 123.33%. Simulation results show that the proposed motion controller is an effective way to enhance the measurement efficiency remarkably compared with the traditional control strategy.     

Fuzzy logic versus a PID controller for position control of a muscle-like actuated arm

This study compares three different control algorithms for a muscle-like actuated arm developed to replicate motion in two degrees-of-freedom (df): elbow flexion/extension (f/e) and forearm pronation/supination (p/s). Electromyogram (EMG) is employed to help determine the control signal used to actuate the muscle cylinders. Three different types of control strategies were attempted. The first algorithm used fuzzy logic with EMG signals and position error as control inputs (Fuzzy Controller). The second algorithm incorporated moment arm information into the existing fuzzy logic controller (Fuzzy-MA Controller). The third algorithm was a conventional Proportional-Integral-Derivative (PID) controller, which operated solely on position and integration error (PID Controller). Overall, moment arm scaling aided the fuzzy logic control algorithm by improving movement accuracy as determined by relative error and correlation. The PID controller resulted in the most accurate movement tracking after fine tuning the control gains. This study implies that moment arm scaling is an effective tool for improving motion tracking accuracy of the fuzzy controller in the mechanical arm. The study also implies that PID controller can be used as a substitute for the fuzzy based controller once the desired motion is prescribed.     

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。     

Neural network controller for robotic motion control

Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations.     

具有迟滞和蠕变特性压电作动器的模糊自适应逆控制方法

针对具有迟滞和蠕变特性的压电作动器非线性模型,提出了一种前馈控制和反馈控制相结合的自适应模糊逆控制方案。在前馈控制器中压电作动器的迟滞和蠕变非线性特性的逆模型由自适应模糊逻辑系统近似;在反馈控制器中比例控制器用来调节压电作动器的输出误差。该方法可以实时补偿压电作动器的迟滞和蠕变特性,减少作动器跟踪误差。仿真计算结果表明了该方法的有效性。     

滚珠丝杠传动机构的微动特性及轨迹跟踪控制

滚珠丝杠传动的机床进给机构在微观运动条件下的各种非线性因素和进给系统较高的机械增益是影响机床运动的控制精度进一步提高的主要因素.本文研究了滚珠丝杠进给机构的微动特性,结果表明库仑摩擦和微弹性现象是滚珠丝杠在微动条件下的主要运动特性.针对这一特性,提出了一种基于误差的增益自适应控制器,该控制器能够有效地提高系统的稳定性,并能保证足够的控制精度.对幅值为1μm的正弦输入,其跟踪控制误差小于0.04μm.对幅值为1mm的正弦输入,其跟踪控制误差为0.5μm.     

混合输入机构精确实现给定运动的模糊滑模控制

混合输入机构运行过程中,由于负载、惯性力等变化,引起常速电机速度的波动,会影响输出运动的精度。针对混合输入机构中常速电机可测不可控的特点,实时检测常速电机的角位置,并对系统的动力学模型进行简化。提出了基于常速电机位置跟踪的控制策略对伺服电机进行控制,并给出了控制框图。考虑系统参数的不确定和外部扰动,设计了模糊滑模变结构控制器实现混合输入机构的轨迹跟踪,应用模糊推理确定切换控制的幅值和采用软切换连续控制的方法减小抖振。仿真结果表明本文方法正确有效。     

基于自抗扰重复控制的压电驱动器高精度跟踪控制

针对压电驱动器的高精度控制问题,提出一种自抗扰重复控制设计方法。首先,给出压电驱动系统的动力学模型;然后,在线性自抗扰控制(LADRC)中引入输出反馈积分控制器和一类插入式重复控制器,提出一种具有阶跃、斜坡和周期信号跟踪/抑制能力的自抗扰重复控制策略。进一步,结合小增益定理,分析闭环系统的稳定性及控制系统的设计方法。最后,将所提方法应用于一类压电驱动系统,实验结果表明该方法与LADRC相比,能显著提升控制效果,且高精度跟踪/抑制多种外部信号。     

挖掘机器人的模型与自适应模糊滑模控制

为实现挖掘机器人的自动挖掘,在挖掘机器人的轨迹规划器给出铲斗期望运动轨迹的情况下,需要挖掘机器人的控制系统能够控制其工作装置实现对给定轨迹的准确跟踪.利用拉格朗日方法建立了挖掘机器人工作装置的三自由度动力学方程,设计了自适应模糊滑模变结构控制器.利用模糊控制动态调节切换增益,将滑模控制的切换项转化为连续的模糊系统,增强了控制系统对挖掘机器人工作装置不确定性和外界干扰的鲁棒性,削弱了滑模控制的抖振现象,并且有较强的自适应跟踪能力.利用MATLAB7.4/Simulink工具箱对所设计的控制器进行了仿真,给出了自适应模糊滑模控制的跟踪性能及误差.     

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     

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.     

Adaptive fuzzy sliding mode control for a robotic aircraft flexible tooling system

A robotic aircraft flexible tooling system is proposed in this paper, of which high-precision synchronous motion control of dual robots is a key part. In order to alleviate the effects of the mechanical coupling over synchronous and tracking errors of the two robots, a cross-coupling scheme based on an adaptive fuzzy sliding mode controller (AFSMC) is developed. First, the mechanical coupling model is established by dynamics analysis of the dual-robot driving system. Then, a novel cross-coupling error is proposed, which combines both the position and speed tracking and synchronous errors of dual robots. Moreover, the cross-coupling control scheme based on AFSMC is presented. For the proposed AFSMC, a fuzzy logic controller is adopted to generate the hitting control signal, and the output gain of the sliding mode control is tuned online by a supervisory fuzzy system. Finally, the preferable performance of the proposed AFSMC cross-coupling approach is verified by the simulation results compared with the conventional proportional-integral-derivative control and SMC cross-coupling controls.