基于参数估计误差的鲁棒自适应律设计及验证

针对传统自适应控制系统设计的自适应律参数收敛慢进而影响控制系统瞬态性能的问题,研究一类新的基于参数估计误差修正的鲁棒自适应律设计.首先引入滤波操作给出参数估计误差的提取方法,构建出含参数估计误差修正项的自适应律,进而将该自适应律用于控制器设计和分析中,可同时实现控制误差和参数估计误差指数收敛.对比分析了几类传统自适应律和所提出自适应律的收敛性和鲁棒性,并给出了保证参数收敛所需持续激励条件的一种直观、简便的在线判别方法.数值仿真及基于自制三自由度直升机系统俯仰轴实验结果表明,基于参数误差修正的自适应律及控制器可得到优于传统自适应方法的跟踪控制和参数估计性能.     

永磁同步电机伺服系统的自适应模糊滑模控制

针对永磁同步电机伺服系统的跟踪控制问题,提出了一种基于扰动观测器的自适应模糊滑模控制方法.通过扰动观测器估计等效扰动,改善了系统的动态性能和稳态性能,并且只需要等效扰动的变化有界,而不是为零,放宽了要求;根据模糊控制原理引入3条模糊规则,在保证滑模条件的前提下有效地削弱了抖振;采用自适应策略估计模糊系统参数的最优值,简化了控制器的设计.实验结果表明,与常规自适应模糊滑模控制相比,本文提出的控制方法不仅能够有效地减小跟踪误差,而且能够改善参数估计过程,保证了参数估计的有界性.     

高精度机械伺服系统的一种新型自适应滑模控制方法

针对高精度机械伺服系统,提出一种高性能的新型自适应滑模控制方法,使闭环系统渐近跟踪给定的参考模型.该方法对转动惯量的大范围变化及非线性摩擦等外干扰均具有很强的鲁棒性.该方法的主要思想是用滑模方法抑制系统中的外部力矩扰动,对系统参数进行自适应估计,用估计值来补偿转动惯量的变化.对于控制算法的全局稳定性,采用李雅普诺夫直接法给出了严格的证明.该算法简单,其实现不需要误差的高阶微分信号,适于实时控制.本文方法以某高精度飞行仿真转台为例,对提出方法进行了实验研究,结果表明了该方法具有良好的跟踪性能,暂态响应和鲁棒性.     

机器人全局PID模糊滑模跟踪控制与仿真研究

为解决机器人跟踪控制过程中采用PID控制算法会出现抖动和误差的问题,提出了一种机器人全局PID模糊滑模跟踪控制算法.通过将PID滑模控制和模糊控制相结合,设计了全局PID模糊滑模控制;基于模糊规则,对滑模控制增益进行自适应调整,从而消除建模误差和干扰,削弱了控制时产生的抖振,在线调整控制器参数和估计误差,并通过积分来消除外界干扰,因此提高了控制精度.仿真结果表明,与常规的PID算法相比,该方法在处理控制抖动和消除误差以及干扰方面具有极高的鲁棒性.     

永磁同步电动机新型自适应滑模控制

永磁同步电动机(PMSM)是多变量、强耦合、非线性时变系统, 对外界干扰及内部参数摄动较为敏感, 为提高系统的鲁棒性, 本文提出一种基于非线性滑模面的自适应滑模变结构控制方法. 根据复合非线性反馈控制理论, 为PMSM滑模控制系统设计非线性滑模面, 通过实时改变控制系统的阻尼系数来提高PMSM伺服系统的瞬态响应性能. 在PMSM伺服系统外界扰动及内部参数摄动的上下界未知的情况下, 采用自适应参数校正律来调节控制增益的大小, 改善了系统的抖振现象. 此外, 对电机的电流及转速进行了饱和限制, 使得所设计的伺服控制系统可用于大范围的位移跟踪. 仿真结果表明, 与基于线性滑模面的控制器相比较, 本文所设计的基于非线性滑模面的自适应滑模控制器使得电机转子位移能够更快且无超调的到达给定值, 且系统的抖振现象明显减弱.     

保证瞬态性能的迭代学习控制

讨论一类不确定非线性系统的可保证瞬态性能的迭代学习控制问题.引入限定跟踪误差瞬态特性的界函数,通过误差转换方法,定义一个转换误差变量,将跟踪误差的保证瞬态特性问题转化为该误差变量的有界性问题.采用Lyapunov方法,设计迭代学习控制器处理系统中参数和非参数不确定性.并且,采用完全限幅学习机制,保证转换误差变量的有界性和一致收敛性.从而既能得出系统输出在整个作业区间的完全跟踪性能,同时又能够保证跟踪误差在每次迭代的过程中具有保证的瞬态特性.仿真结果验证了所提控制方法的有效性.     

一类不确定非线性系统基于特征模型的复合自适应控制

针对一类不确定非线性系统的跟踪控制问题,提出一种基于特征模型的复合自适应控制方法.该方法的创新性在于基于系统的误差特征模型,构建一种综合跟踪控制误差和模型估计误差的特征参量复合自适应律,该自适应律用于控制器设计和分析,可同时实现跟踪控制误差和模型估计误差的收敛.此外,为便于特征参量自适应律的设计和分析,根据特征参量的慢时变特性,将其视为未知标称常数项和时变误差项之和,并且选用其中常数项的估计量作为自适应控制参数.进一步,为抑制特征参量中时变误差项对系统稳定性和模型估计误差收敛性的影响,在控制器及复合自适应律设计中引入带饱和函数的非线性环节.理论分析证明闭环控制系统稳定,且跟踪控制误差和模型估计误差收敛到原点的一个邻域内.仿真结果表明,与现有仅根据模型估计误差调节的基于特征模型的自适应控制方法相比,所提出的复合自适应控制方法具有更好的控制性能.     

电液伺服系统的多滑模鲁棒自适应控制

针对一类参数与外负载非匹配不确定的非线性高阶系统,提出了一种基于逐步递推方法的多滑模鲁棒自适应控制策略.应用逐步递推的多滑模控制方法简化了高阶系统的控制问题,同时在自适应控制中加入鲁棒控制的方法,以消除不确定性对控制性能的影响.首先利用逐步递推方法与状态反馈精确线性化理论,得出确定系统的多滑模控制器设计方法;然后基于Lyapunov稳定性分析方法,给出不确定系统的参数自适应律,及鲁棒自适应控制器的设计方法.本文把该控制策略应用到电液伺服系统的位置跟踪控制中,仿真结果显示,该控制方法具有较强的鲁棒性及良好的跟踪效果.     

基于自组织小波小脑模型关节控制器的不确定非线性系统鲁棒自适应终端滑模控制

针对一类不确定非线性系统的跟踪控制问题,在考虑建模误差、参数不确定和外部干扰情况下,以良好的跟踪性能及强鲁棒性为目标,提出基于自组织小脑模型(self-organizing wavelet cerebellar model articulation controller,SOWCMAC)的鲁棒自适应积分末端(terminal)滑模控制策略.首先,将小脑模型、自组织神经网络和小波函数各自优势相结合,给出一种SOWCMAC,以保证干扰估计方法具有快速学习能力和更好的泛化能力.其次,设计两种改进的terminal滑模面构造方法,并分别给出各自的收敛时间.然后,基于SOWCMAC和改进的积分terminal滑模面,给出不确定非线性系统鲁棒自适应非奇异terminal控制器的设计过程,其中通过构造自适应鲁棒项抑制干扰估计误差对系统跟踪性能的影响,并利用Lyapunov理论证明闭环系统的稳定性.最后,将该方法应用于近空间飞行器姿态的控制仿真实验,结果表明所提出方法有效性.     

一种可保证瞬态特性的改进的鲁棒模型参考自适应控制

针对典型的鲁棒模型参考自适应控制中瞬态性能无法得到保障的问题, 提出一种改进的鲁棒模型参考自适应控制器. 该控制器在标准的鲁棒自适应控制中加入??_∞ 补偿器, 以抑制闭环自适应系统中参数估计误差和不确定扰动对系统输出跟踪性能造成的不利影响. 理论分析和仿真验证表明, 所提出的控制器不但保留了典型鲁棒模型参考自适应控制的理想特性, 并且通过设计适当的??∞ 补偿器使得闭环系统的瞬态性得到了较大的改善, 其改善的程度依赖于??∞ 补偿器性能指标的大小.

     

Improving transient performance of adaptive control via a modified reference model and novel adaptation

This paper presents a new model reference adaptive control (MRAC) framework for a class of nonlinear systems to address the improvement of transient performance. The main idea is to introduce a nonlinear compensator to reshape the closed‐loop system transient, and to suggest a new adaptive law with guaranteed convergence. The compensator captures the unknown system dynamics and modifies the given nominal reference model and the control action. This modified controlled system can approach the response of the ideal reference model. The transient is easily tuned by a new design parameter of this compensator. The nominal adaptive law is augmented by new leakage terms containing the parameter estimation errors. This allows for fast, smooth and exponential convergence of both the tracking error and parameter estimation, which again improves overall reference model following. We also show that the required excitation condition for the estimation convergence is equivalent to the classical persistent excitation (PE) condition. In this respect, this paper provides an intuitive and numerically feasible approach to online validate the PE condition. The salient feature of the suggested methodology is that the rapid suppression of uncertainties in the controlled system can be achieved without using a large, high‐gain induced, learning rate in the adaptive laws. Extensive simulations are given to show the effectiveness and the improved response of the proposed schemes. Copyright © 2016 John Wiley & Sons, Ltd.     

Study on the exponential path tracking control of robot manipulators via direct adaptive methods

Exponential path tracking control represents an important issue pertaining to the transient performance of robot control systems. In this paper, the so-called Exp-transformation is applied to obtain transformed robot dynamics models which are used to derive several adaptive control algorithms that achieve exponential path tracking. In contrast to the existing composite adaptive control method; where both the tracking error and the prediction error are used and persistent excitation (p.e.) is required, the proposed strategy requires only the tracking error. This makes the control structure simpler and easier to implement. The main contribution of this paper is the development of practical control strategies for which the p.e. requirement is completely removed (as opposed to relaxing it to semi-p.e. as was done in a recent work). The fundamental idea introduced for exponential stability analysis is conceptually simple and global results are obtained.     

基于神经网络补偿的转台伺服系统控制研究

针对飞行仿真转台伺服系统中存在的非线性摩擦干扰进行了研究,采用一种基于RBF神经网络进行误差补偿的在线自适应控制策略。在基于逆动力学的计算力矩控制方法的基础上,利用RBF神经网络的万能逼近特性在线辨识模型误差,从而对系统进行补偿,其权值自适应律根据Lyapunov稳定性理论推导,保证了系统跟踪误差的收敛及稳定,仿真结果表明该控制策略可使位置MAE指标从0.0087m提高到0.0016m,使位置MSE指标从1.0128e-4m提高到3.3002e-6m,具有较高的鲁棒性和稳态控制精度。最后分别从隐层节点数及节点中心学习算法的变化两方面提出两种改进方案,仿真结果表明隐层节点数的增加可以进一步减小位置误差,而采用K-means均值聚类算法解决了神经网络节点中心按经验选取或试凑的困难。     

Application of self-tuning fuzzy PID controller for a SRM direct drive volume control hydraulic press

A new kind of volume control servo hydraulic press driven directly by Switched Reluctance Motor (SRM) is presented in this paper. In considering the saturation and dead zone nonlinearity as well as the time-variability and the time lag existed in SRM direct drive volume control system, a fuzzy PID control method is introduced to improve the overall performance of the electro-hydraulic position servo system. The relationships between the PID parameters and the response characteristics of electro-hydraulic position servo system are investigated. The fuzzy inference rules which enable adaptive adjustment of PID parameters are established based on the error and change in error. The simulations and experiments of step response and cosine tracking are carried out on the SRM direct drive hydraulic press. The results indicate that the fuzzy self-tuning PID method has great ability of restraining external disturbance, and it can effectively raise the position tracking ability of the volume control electro-hydraulic servo system.     

Adaptive neural control with fast approximation for uncertain nonlinear systems: A novel composite learning approach

In existing adaptive neural control approaches, only when the regressor satisfies the persistent excitation (PE) or interval excitation (IE) conditions, the constant optimal weights of neural network (NN) can be identified, which can be used to establish uncertainties in nonlinear systems. This paper proposes a novel composite learning approach based on adaptive neural control. The focus of this approach is to make the NN approximate uncertainties in nonlinear systems quickly and accurately without identifying the constant optimal weights of the NN. Hence, the regressor does not need to satisfy the PE or IE conditions. In this paper, regressor filtering scheme is adopted to generate prediction error, and then the prediction error and tracking error simultaneously drive the update of NN weights. Under the framework of Lyapulov theory, the proposed composite learning approach can ensure that approximation error of the uncertainty and tracking error of the system states converge to an arbitrarily small neighborhood of zero exponentially. The simulation results verify the effectiveness and advantages of the proposed approach in terms of fast approximation.     

Desired Compensation Adaptive Repetitive Control of Electrical‐Optical Gyro‐Stabilized Platform with High‐Precision Disturbance Compensation

In this paper, the influences of unknown disturbances are first analyzed, and the structural properties of the disturbances are given. By appropriately applying Fourier series approximation, a novel continuously differentiable nonlinear friction model is synthesized by modifying the traditional piecewise continuous LuGre model, then a desired compensation version of the proposed adaptive repetitive controller is developed for precise tracking control of servo systems to compensate for spatial periodic disturbance and random disturbance. To further reduce noise sensitivity and improve tracking accuracy, the desired compensation robust control term is also constructed to effectively attenuate the effect of approximation errors, and thus a theoretically asymptotic tracking performance is achieved by the proposed controller, which is very important for the high accuracy tracking control of servo systems. Extensive comparative experimental results are obtained to verify the high‐performance nature of the proposed control strategies.     

Adaptive control of robots with an improved transient performance

By using a robust control technique, this note proposes an adaptive control for rigid robots with the following important features: under a parameter-dependent persistent excitation (PE) condition, it gives a guaranteed transient performance of tracking a smooth desired trajectory while assuring the parameter, estimation error to go to a residual set of the origin arbitrarily fast. Simulations are included to support the theoretical results     

Adaptive robust control for four-motor driving servo system with uncertain nonlinearities

A novel adaptive robust control (ARC) is presented for the four-motor driving servo systems with the uncertain nonlinearities and actuation failures, such that the load tracking control is achieved with the proximate optimal-time. By applying the proposed scheme, several control objectives are achieved. First, the nonlinear synchronization algorithm is presented to maintain the velocity synchronization of each motor, which provides fast convergence without chatting. Moreover, the time-varying bias torque is applied to eliminate the effect of backlash and reduce the waste of energy. Then, the ARC is designed to achieve the proximate optimal-time output tracking with the transient performance in $L_2$ norm, where the friction and actuation failures are addressed by the adaptive scheme based on the norm estimation of unknown parameter vector. Finally, the extensive simulated and experimental results validate the effectiveness of the proposed method.     

Adaptive actuator failure compensation control of uncertain nonlinear systems with guaranteed transient performance

In order to accommodate actuator failures which are uncertain in time, pattern and value, we propose two adaptive backstepping control schemes for parametric strict feedback systems. Firstly a basic design scheme on the basis of existing approaches is considered. It is analyzed that, when actuator failures occur, transient performance of the adaptive system cannot be adjusted through changing controller design parameters. Then we propose a new controller design scheme based on a prescribed performance bound (PPB) which characterizes the convergence rate and maximum overshoot of the tracking error. It is shown that the tracking error satisfies the prescribed performance bound all the time. Simulation studies also verify the established theoretical results that the PPB based scheme can improve transient performance compared with the basic scheme, while both ensure stability and asymptotic tracking with zero steady state error in the presence of uncertain actuator failures.