电力系统非线性自适应励磁控制设计

考虑了发电机参数的不确定性,根据滤波后的线性参数化模型,通过使参数更新的方向与关于参数的预测误差平方的梯度方向相反,提出了一种在线自适应参数估计非线性励磁控制方法。数字仿真试验表明,设计的非线性自适应控制律具有较强的参数自适应能力,控制器以发电机端电压为输出,保证了系统内动态的稳定。     

基于神经网络的一类不确定非线性系统自适应H∞跟踪控制

基于神经网络针对一类具有输入不确定性的非线性系统提出了一种H∞自适应跟踪控制方法.控制器由等效控制器、H∞控制器及参数自适应控制器三部分组成.H∞控制器用于减弱外部及神经网络的逼近误差对跟踪性能的影响,参数自适应控制器用于抑制输入干扰对跟踪性能的影响.所设计的控制器不仅保证了整个闭环系统的稳定性,而且使外部干扰及神经网络的逼近误差对跟踪的影响减小到给定的性能指标.最后给出一个算例验证了该方法的有效性.     

基于神经网络的一类不确定非线性系统自适应H∞跟踪控制

基于神经网络针对一类具有输入不确定性的非线性系统提出了一种H∞自适应跟踪控制方法.控制器由等效控制器、H∞控制器及参数自适应控制器三部分组成.H∞控制器用于减弱外部及神经网络的逼近误差对跟踪性能的影响,参数自适应控制器用于抑制输入干扰对跟踪性能的影响.所设计的控制器不仅保证了整个闭环系统的稳定性,而且使外部干扰及神经网络的逼近误差对跟踪的影响减小到给定的性能指标.最后给出一个算例验证了该方法的有效性.     

多关节机器人反馈线性化双模糊滑模控制

为了提高多关节机器人轨迹跟踪控制性能,提出了一种反馈线性化双模糊滑模控制方法。该方法在对机器人非线性动力学模型反馈线性化的基础上,设计了一种双模糊滑模控制器。通过设计一个模糊控制器,根据跟踪误差和误差变化率自适应地调整滑模面的斜率,从而加快响应速度。通过设计另一个模糊控制器,根据滑模面自适应地调整滑模控制的切换控制部分,从而减弱抖振。利用李亚普诺夫定理证明了控制系统的稳定性。针对空间三关节机器人进行了仿真实验,结果表明了所提方法的有效性。     

高速开关阀控气动位置伺服系统的自适应鲁棒控制

针对高速开关阀控气动位置伺服系统所具有的模型参数不确定性、不确定非线性以及外干扰,为实现气缸的高精度运动轨迹跟踪控制,设计了基于标准投影映射的自适应鲁棒控制器。该控制器通过在线最小二乘参数估计来减小模型中参数不确定性,利用基于反步法设计的非线性鲁棒控制来抑制参数估计误差、不确定非线性以及外干扰的影响,从而保证一定的瞬态性能和高的气缸运动轨迹控制精度。由于运用了标准投影映射以保证在线参数估计有界,控制器的两个部分可以独立进行设计。试验表明,所设计的控制器能获得良好的轨迹跟踪控制性能,对干扰具有较强的性能鲁棒性,系统跟踪幅值为0.09 m,频率为0.5 Hz的正弦期望轨迹时,最大绝对跟踪误差为1.51 mm,标准跟踪误差0.72 mm。     

水下运载器纵向轨迹自适应跟踪控制

针对强非线性、大俯仰角运动的水下运载器纵向运动轨迹跟踪问题提出了一类非线性自适应控制方案.首先,直接采用非线性运动模型,在控制器设计过程中引入饱和函数,通过麦克劳林展开公式避免了俯仰角为小角度的假设限制;其次,考虑到运载器非线性运动模型很难给出精确的数学描述并且实际运载器系统存在模型误差,采用在线自适应方法近似逼近其非线性模型;最后,利用Backstepping方法设计了非线性自适应控制器,并利用Lyapunov理论证明了控制系统的稳定性.半实物仿真结果表明:在考虑测量噪声和参数不确定性的情况下,该算法对给出的3种轨迹的跟踪误差均小于0.5m,俯仰舵偏均小于15°,俯仰力矩均在105 N.m量级.结果验证了本文提出的控制系统鲁棒性强,满足跟踪性能要求.     

数控机床直线伺服系统非线性自适应鲁棒控制的研究

对数控机床永磁直线伺服系统提出非线性自适应鲁棒控制的设计方法.在永磁直线伺服系统非线性数学模型的基础上,为实现对速度和电流的准确跟踪,建立了误差系统的动态模型.将跟踪和干扰抑制归结为非线性自适应鲁棒控制器设计问题,通过构造存储函数得到包含电阻辨识算法的自适应鲁棒控制器的定理,证明定理给出的控制器能满足干扰抑制和系统的渐进稳定.仿真结果表明,用该方法设计的系统能很好地抑制扰动和跟踪给定,满足对数控机床永磁直线伺服系统控制的要求.     

数控机床直线同步电动机磁悬浮系统的神经网络直接自适应控制

针对数控机床可控励磁直线同步电动机磁悬浮系统的强非线性、外部扰动不确定性的问题,设计基于RBF神经网络直接自适应控制器.通过分析磁悬浮系统的运行机理,推导运动方程及悬浮力方程,进而建立系统的状态方程;用悬浮高度的跟踪误差和误差的变化量构造误差函数,设计直接自适应理想控制器并采用RBF神经网络对其进行逼近;设计自适应律来估计神经网络理想权值,对误差函数的变化率构造二次型Lyapunov函数,利用Lyapunov稳定性理论来证明系统稳定;通过Matlab对控制系统进行计算机仿真,结果表明该方法设计的控制器与自适应模糊滑模控制器和PID控制器相比,空载启动时调节时间减少了23.5％,突加负载时动态降落减少了64.7％,恢复时间减少了38.2％,具有稳态误差小,调节时间和恢复时间短,抗扰性较强的优点,能有效提高磁悬浮系统的控制性能.     

智能扫地机器人双环积分滑模控制方法研究

针对Mecanum轮型扫地机器人在车轮打滑和重心偏移等不确定非线性因素影响下的轨迹跟踪精度问题,提出了一种基于修正动力学模型的轨迹跟踪控制方法。首先,对机器人进行了运动学与动力学分析。然后,根据外界干扰及参数估计的不确定性对动力学模型进行了修正,设计了双环积分滑模控制器,并通过Lyapunov函数证明了控制系统的稳定性。最后,在不同扰动作用下,以圆为参考轨迹进行跟踪仿真,结果表明:该控制系统具有较好的抗干扰性和鲁棒性,避免了因不确定性参数估计带来的建模误差,为扫地机器人在实际轨迹跟踪控制运用中奠定了理论基础。     

机械臂运动的智能自适应模糊控制策略

为了提高机械臂轨迹跟踪控制精度同时节省驱动能量,提出了机械臂运动的智能自适应模糊控制策略。介绍了双连杆机械臂结构并建立了其动力学模型;设计了机械臂系统控制方案和智能自适应模糊控制器的实现方案;在粒子群算法基础上增加了多策略进化方法和多子群协同搜索方法,提出了多策略协同进化粒子群算法;以机械臂轨迹跟踪误差和驱动力矩最小为目标,以多策略协同进化算法为寻优算法,设计了具有智能自适应调节能力的模糊控制器。经仿真验证,自适应模糊控制器的跟踪误差幅值为PID控制误差幅值的26%左右,同时模糊控制器驱动力矩的平均振动幅值不足PID控制器力矩振动幅值的17%,充分证明了智能自适应模糊控制器能够以更小的力矩实现更小的跟踪误差。     

Finite time parameter estimation-based adaptive predefined performance control for servo mechanisms

This paper proposes an adaptive funnel control scheme for two-inertia servo systems with unknown parameters (e.g., inertia and stiffness coefficient). To improve the transient and steady-state performance, a modified funnel variable, which relaxes the limitation of the original funnel control, is developed by using the tracking error to replace the scaling factor. Then, an error transformation with the new funnel variable is introduced and used in the controller design. An auxiliary filter operation is designed to derive the information of parameter estimation error, which is used as a new leakage term in the parameter update law. Then a sliding mode technique is introduced in the adaption law to achieve finite-time convergence. Appropriate comparison to the gradient descent method is provided concerning with the estimation convergence. Simulation and experimental results are used to illustrate the effectiveness of the devised control scheme.     

Adaptive control of nonlinear uncertain active suspension systems with prescribed performance

This paper proposes adaptive control designs for vehicle active suspension systems with unknown nonlinear dynamics (e.g., nonlinear spring and piece-wise linear damper dynamics). An adaptive control is first proposed to stabilize the vertical vehicle displacement and thus to improve the ride comfort and to guarantee other suspension requirements (e.g., road holding and suspension space limitation) concerning the vehicle safety and mechanical constraints. An augmented neural network is developed to online compensate for the unknown nonlinearities, and a novel adaptive law is developed to estimate both NN weights and uncertain model parameters (e.g., sprung mass), where the parameter estimation error is used as a leakage term superimposed on the classical adaptations. To further improve the control performance and simplify the parameter tuning, a prescribed performance function (PPF) characterizing the error convergence rate, maximum overshoot and steady-state error is used to propose another adaptive control. The stability for the closed-loop system is proved and particular performance requirements are analyzed. Simulations are included to illustrate the effectiveness of the proposed control schemes.     

Adaptive Robust Tracking Control of Pressure Trajectory Based on Kalman Filter

When adaptive robust control(ARC) strategy based on backstepping design is applied in pneumatic servo control, accurate pressure tracking in motion is especially necessary for both force and position trajectories tracking of rodless pneumatic cylinders, and therefore an adaptive robust pressure controller is developed in this paper to improve the tracking accuracy of pressure trajectory in the chamber when the pneumatic cylinder is moving. In the proposed adaptive robust pressure controller, off-line fitting of the orifice area and on-line parameter estimation of the flow coefficient are utilized to have improved model compensation, and meanwhile robust feedback and Kalman filter are used to have strong robustness against uncertain nonlinearities, parameter fluctuations and noise. Research results demonstrate that the adaptive robust pressure controller could not only track various pressure trajectories accurately even when the pneumatic cylinder is moving, but also obtain very smooth control input, which indicates the effectiveness of adaptive model compensation. Especially when a step pressure trajectory is tracked under the condition of the movement of a rodless pneumatic cylinder, maximum tracking error of ARC is 4.46 kPa and average tracking error is 0.99 kPa, and steady-state error of ARC could achieve 0.84 kPa, which is very close to the measurement accuracy of pressure transducer.     

电液位置伺服系统的自适应滑模鲁棒跟踪控制

针对存在参数不确定性的电液位置伺服系统的跟踪控制问题，基于滑模控制理论，提出了一种具有参数自适应能力的自适应滑模控制方法。通过自适应方法，来消除参数不确定性对系统控制性能的影响，进而实现鲁棒控制。基于李雅普诺夫稳定性理论证明了自适应滑模控制系统的渐近稳定性。将该方法应用于某疲劳试验机电液伺服系统的跟踪控制，仿真和实时控制结果证明了该方法的有效性。     

模糊自适应PID在高空模拟舱中压力控制的应用研究

针对高空环境模拟舱压力控制系统变参数、强干扰、大惯性、强耦合等特点,将模糊控制与自适应PID控制结合起来,设计了模糊自适应PID控制器。利用模糊推理方法实现对PID参数的在线自整定,进一步完善PID控制器的性能,提高系统的控制精度。仿真结果表明该方法的控制效果优于常规的PID控制,并消除了模糊控制稳态误差较大的缺点,具有响应时间短、控制精度高、稳定性好等优点,有较好的工程应用前景。     

Convergence of fractional adaptive systems using gradient approach

Conditions for boundedness and convergence of the output error and the parameter error for various Caputo's fractional order adaptive schemes based on the steepest descent method are derived in this paper. To this aim, the concept of sufficiently exciting signals is introduced, characterized and related to the concept of persistently exciting signals used in the integer order case. An application is designed in adaptive indirect control of integer order systems using fractional equations to adjust parameters. This application is illustrated for a pole placement adaptive problem. Advantages of using fractional adjustment in control adaptive schemes are experimentally obtained.     

Robust adaptive integral backstepping control for opto-electronic tracking system based on modified LuGre friction model

This paper presents a robust adaptive integral backstepping control strategy with friction compensation for realizing accurate and stable control of opto-electronic tracking system in the presence of nonlinear friction and external disturbance. With the help of integral control term to decrease the steady-state error of the system and combining robust adaptive control approach with the backstepping design method, a novel control method is constructed. Nonlinear modified LuGre observer is designed to estimate friction behavior. Robust adaptive integral backstepping control strategy is developed to compensate the changes in friction behavior and external disturbance of the servo system. The stability of the opto-electronic tracking system is proved by Lyapunov criterion. The performance of robust adaptive integral backstepping controller is verified by the opto-electronic tracking system with modified LuGre model in simulation and practical experiments. Compared to the adaptive integral backstepping sliding mode control method, the root mean square of angle error is reduced by 26.6% when the proposed control method is used. The experiment results demonstrate the effectiveness and robustness of the proposed strategy.     

改进平衡优化器的超声电机无模型自适应控制

为了提高超声电机的控制性能,将基于数据驱动的无模型自适应控制（Model Free Adaptive Control,MFAC）方法应用到超声电机的速度控制中,并针对MFAC存在参数调整困难的问题,提出一种改进的平衡优化器（Improved Equilibrium Optimizer, IEO）算法用于MFAC参数寻优。首先,利用自适应生成概率策略来平衡算法的探索与开发能力;其次,引入折射反向学习策略来扩大解的搜索范围,提高算法的收敛速度,同时采用柯西变异策略来提高算法跳出局部最优的能力;最后,提出一种改进的时间乘以绝对误差积分（Improved Integral Time Absolute Error, IITAE）指标函数用于MFAC的参数寻优。仿真和实验结果表明,与基于原始平衡优化器算法的MFAC相比,基于改进平衡优化器算法的MFAC的稳态误差和调整时间明显减小,系统的控制性能得到显著提高。     

Adaptive control of 5 DOF upper-limb exoskeleton robot with improved safety

This paper studies an adaptive control strategy for a class of 5 DOF upper-limb exoskeleton robot with a special safety consideration. The safety requirement plays a critical role in the clinical treatment when assisting patients with shoulder, elbow and wrist joint movements. With the objective of assuring the tracking performance of the pre-specified operations, the proposed adaptive controller is firstly designed to be robust to the model uncertainties. To further improve the safety and fault-tolerance in the presence of unknown large parameter variances or even actuator faults, the adaptive controller is on-line updated according to the information provided by an adaptive observer without additional sensors. An output tracking performance is well achieved with a tunable error bound. The experimental example also verifies the effectiveness of the proposed control scheme.