一种自适应CMAC在交流励磁水轮发电系统中仿真研究

在分析常规CMAC结构的基础上,针对一类非线性、参数时变和不确定的控制系统,提出了一种自适应CMAC神经网络的控制器．该控制器以系统动态误差和给定信号量作为CMAC的激励信号,并与自适应线性神经元网络相结合构成系统的复合控制．为了验证其有效性,将其应用到交流励磁水轮发电机系统的多变量非线性控制中,并与常规的PID控制效果进行了比较．仿真结果表明,该控制器具有较强鲁棒性和自适应能力,控制品质优良。     

连续时间混沌系统的参数自适应控制1)

研究了连续时间混沌系统的参数自适应控制,提出了关于多重参数混沌非线性系统的参数自适应控制的新方法.考虑系统参数是线性形式的自适应控制,利用Lyapunov方法证明了参数控制方程是全局渐近稳定的.研究结果表明该控制方法是分析混沌参数自适应控制的一个十分有效的方法.     

连续时间混沌系统的参数自适应控制

研究了连续时间混沌系统的参数自适应控制,提出了关于多重参数混沌非线性系统 的参数自适应控制的新方法.考虑系统参数是线性形式的自适应控制,利用Lyapunov方法 证明了参数控制方程是全局渐近稳定的.研究结果表明该控制方法是分析混沌参数白适应控 制的一个十分有效的方法.     

多变量周期系统的多模型二阶段自适应控制

对于一类参数未知的多变量周期系统,传统自适应控制方法存在参数收敛慢的问题,导致系统暂态响应差、控制效果不理想.因此,本文针对多变量周期系统设计了多模型二阶段自适应控制器.首先根据先验知识,确定不确定区域范围,并在不确定区域内建立多个自适应模型.然后根据李雅普诺夫理论得到第一阶段辨识方程;在第二阶段中,充分考虑辨识误差并确定了权值自适应律,以此获取虚拟模型以提高参数的收敛速度.接着,利用得到的虚拟模型参数设计了二阶段自适应控制器,在保证了系统稳定性的基础上,提高了系统的暂态性能.最后,给出的仿真结果表明多模型二阶段自适应控制器提高了参数的收敛速度,改善了系统的暂态性能.     

基于PD增益的模型参考自适应同步控制

在保证自适应控制精度的前提下,找出快速收敛的参数,从而使被控系统和参考系统达到同步.针对传统的PI控制器无法获得参数波动的系统的较高的控制性能.基于模型参考自适应控制,利用PD控制器可以预料到系统误差的方向的优点,设计了一种自适应同步控制器.仿真结果表明,该自适应同步控制器能够使被控系统和参考系统达到同步,并能够较精确地控制被控系统的输出,使被控系统满足系统所要求的动态性能.这对研究组合自适应控制策略和开展多模型自适应控制器的研究提供了基础.     

基于神经网络逆控制的水轮机调节系统

根据神经网络对非线性系统模型的辨识能力,将其与自适应逆控制相结合,对水轮发电机组的逆模型进行建模,构建一种新的水轮机调节系统。该方案以逆系统以及系统辨识理论为基础,以水轮发电机组作为被控对象,分别针对其频率和负荷扰动,建立神经网络在线逆控制器,对系统进行调控,并将仿真结果与传统PID控制进行比较。从仿真结果可以看出,所提的控制方案能够实现对水轮发电机组的有效控制,使系统具有较好的动态性能和鲁棒性。     

严格反馈随机非线性系统的鲁棒自适应逆最优跟踪

针对具有未知定常参数和标准Wiener噪声扰动的严格反馈非线性系统,结合参考信号,构造了误差系统,使用Backstepping算法设计了误差系统的自适应逆最优控制律和参数自适应律,进而解决了原系统的鲁棒自适应逆最优跟踪.     

一类高阶非线性参数化系统自适应重复学习控制

针对一类高阶非线性参数化系统,利用参数重组技巧,提出了一种自适应重复学习控制方法.该方法结合反馈线性化,可以处理参数在一个未知紧集内周期性、快时变的非线性系统.通过引进微分-差分参数自适应律,设计了一种自适应控制策略,使广义跟踪误差在误差平方范数意义下渐近收敛于零.通过构造Lyapunov泛函,给出了闭环系统收敛的一个充分条件.实例仿真结果说明了该方法的可行性和有效性.     

含有非线性不确定参数的电液系统滑模自适应控制

针对含有非线性不确定参数的电液控制系统, 提出了一种滑模自适应控制方法. 该控制方法主要是为了解决由于初始控制容积的不确定性而引起的, 非线性不确定参数自适应律设计的难题. 其主要特点为, 通过定义一个新型的特Lyapunov 函数, 进而构建系统的自适应控制器及参数自适应律, 并结合滑模控制方法及一种简单的鲁棒设计方法, 给出整个电液系统的滑模自适应控制器, 及所有不确定参数的自适应律. 试验结果表明, 采用该控制方法能够取得良好的性能, 尤其可以补偿非线性不确定参数对系统的影响.     

一类未知参数非线性系统的自适应无源控制

对一类含有未知参数的本质非线性系统,通常的Backstepping方法无法应用.为此,提出一种基于状态反馈的自适应无源控制对策,通过对非线性系统进行反馈无源化控制,设计自适应无源镇定控制器和自适应无源输出跟踪控制器.设计中适当调节自适应无源控制器参数,能够保证闭环系统稳定且所有信号全局一致有界,从而解决了此类含有未知参数非线性系统的稳定性和输出跟踪问题.仿真算例验证了提出控制方案的有效性,表明系统具有较强的稳定性和跟踪特性.     

Probabilistic estimation of the reachable set of model reference adaptive controllers using the scenario approach

A fundamental and critical problem for Model Reference Adaptive Control (MRAC) systems is the characterisation of the system response during transients. This problem is strictly related to the estimation of the reachable set (RS) from a fixed set of initial conditions and it is typically tackled using the Lyapunov's direct method. One well-known drawback of this approach is the excessive conservatism in the estimation of the RS. To overcome this limitation the authors propose a novel probabilistic framework where uncertain parameters and control signals are considered as random variables. In this framework the RS design is translated into a stochastic convex optimisation problem. This brings the benefit that (probabilistic) LMIs with reduced conservatism can be worked out. The so-called scenario optimisation approach is then used to solve the stochastic optimisation problem with a-priori specified level of reliability. The novel approach is compared with an existing worst-case approach in determining the RS of MRAC systems in the presence of matched and input uncertainty via simulation studies. The proposed methodology can potentially be a useful tool for the probabilistic analysis and design of a broad category of existing adaptive control systems.     

Stability limit of human-in-the-loop model reference adaptive control architectures

Model reference adaptive control (MRAC) offers mathematical and design tools to effectively cope with many challenges of real-world control problems such as exogenous disturbances, system uncertainties and degraded modes of operations. On the other hand, when faced with human-in-the-loop settings, these controllers can lead to unstable system trajectories in certain applications. To establish an understanding of stability limitations of MRAC architectures in the presence of humans, here a mathematical framework is developed whereby an MRAC is designed in conjunction with a class of linear human models including human reaction delays. This framework is then used to reveal, through stability analysis tools, the stability limit of the MRAC–human closed-loop system and the range of model parameters respecting this limit. An illustrative numerical example of an adaptive flight control application with a Neal–Smith pilot model is presented to demonstrate the effectiveness of developed approaches.     

Globally stable model reference adaptive control based on fuzzy description of the plant

A novel fuzzy adaptive control algorithm is presented that belongs to direct model reference adaptive techniques based on a fuzzy (Takagi-Sugeno) model of the plant. The global stability of the overall system is proven, namely all the signals in the system remain bounded while the tracking error and estimated parameters converge to some residual set that depends on the size of disturbance and high-order parasitic dynamics. The hallmarks of the approach are its simplicity and transparency. The proposed algorithm is a straightforward extension of classical model reference adaptive control (MRAC) with a robust adaptive law to nonlinear systems described by fuzzy models. The performance of the approach was tested on a simulated plant and compared with the performance of a PI controller and a classical MRAC.     

Model reference adaptive control using the delta operator

Reference is made to a recent paper by G.C. Goodwin, R. Lozano Leal, D.Q. Mayne, and R.H. Middleton (Automatica, vol.22, p.199-207, 1986), where it is shown that a cancellation of nonminimum-phase zeros appearing in discrete-time model reference adaptive control (MRAC) at a fast sampling rate can be easily avoided if the model to be identified is expressed in terms of the delta operator. A new proof of stability of the MRAC is given using the delta operator which, in contrast to the proof given in the above paper, does not require an assumption that a system consisting of a model determined by estimated parameters and a control law based on certainty equivalence principle is exponentially stable. A simple parameterization is proposed for the discrete-time MRAC using the delta operator which allows application of the usual (i.e. without dead zone) estimation algorithms     

A review on model reference adaptive control of robotic manipulators

The accuracy of the motion control for robotic mechanisms will have an effect on their overall performance. Under the condition where the robotic end-effector carries different loads, the motions for each joint of robotic mechanisms change depending on different payload masses. Conventional control systems possess the potential issue that they cannot compensate the load variation effect. Adaptive control, especially the model reference adaptive control (MRAC), has therefore been put forward to handle the above issue. Adaptive control is generally divided into three categories, model reference, self-tuning and gain-scheduled. In this study, the authors only focus on the model-reference approach. To the best of the authors’ knowledge, very few recent research articles can be found in the area of MRAC especially for robotic mechanisms since robotic system is a highly nonlinear system, and it is difficult to guarantee the stability of MRAC in such system. This study presents a review and discussion on the MRAC of robotic mechanisms and some issues of MRAC for robotic mechanisms are also demonstrated. This study can provide a guideline for upcoming research in the field of MRAC for robotic mechanisms.     

Model reference adaptive controller for enhancing depth of penetration and bead width during Cold Metal Transfer joining process

In this paper, an adaptive control scheme is employed for joining Aluminium 6061 alloy sheets by Cold Metal Transfer (CMT) process. The transfer function model of the CMT welding system is derived using empirical equations. The CMT plant transfer function is estimated using system identification technique. For the estimated plant model, a conventional PID controller is initially designed by tuning the controller parameters. The designed control system is tested for its ability to control the welding current when short circuit phase and arcing phase are detected. Following the conventional PID controller, a Model Reference Adaptive Controller is implemented to maintain the welding current at desired range during melting and electrode wire short circuiting. The performance analysis for the proposed adaptive control scheme and the conventional PID controller is compared. The simulation results indicate that the conventional PID controller is unable to retrieve the desired current during short circuit phase and arcing phase. Nevertheless, the proposed MRAC for CMT process successfully maintains the welding current at the setpoint when subjected to arcing phases and short circuit respectively, while ensuring arc stability. The experimental validation is carried out in the CMT welding set up using the designed MRAC. The experimental results emphasize that the MRAC improves the welding performance by yielding good weld joints swiftly and enhanced quality besides minimizing the design complexities.     

具有理想跟踪特性的多变量变结构模型参考自适应控制

考虑一个广义相对阶为1的多变量系统之变结构模型参考自适应控制器(VS-MRAC) 设计问题,提出了一种切换控制方案,解决了现有多变量VS-MRAC和多变量模型参考自适应 控制(MRAC)中两个尚未解决的问题:1)使系统跟踪误差的每个分量均满足预先给定的性能指 标;2)无须假定对象高频增益矩阵满足通常的正定性条件.     

Toward the intelligent control of robots

A new adaptive control architecture for the intelligent control of robotic manipulators is developed. The design is capable of utilizing external sensory information for the robot control. To achieve this, first a model reference adaptive control (MRAC) is developed which can be applied to a robot arm in the task space. Then the concept of virtual adaptive model is defined, which is used to formulate a maneuvering strategy in response to the external information. Finally, the virtual model provides the modified reference signals to the MRAC system to control the corresponding modified motion of the robot in the environment. The design does not require any knowledge about the dynamic parameters of the robot or that of the environment.     

Exponential parameter and tracking error convergence guarantees for adaptive controllers without persistency of excitation

In model reference adaptive control (MRAC) the modelling uncertainty is often assumed to be parameterised with time-invariant unknown ideal parameters. The convergence of parameters of the adaptive element to these ideal parameters is beneficial, as it guarantees exponential stability, and makes an online learned model of the system available. Most MRAC methods, however, require persistent excitation of the states to guarantee that the adaptive parameters converge to the ideal values. Enforcing PE may be resource intensive and often infeasible in practice. This paper presents theoretical analysis and illustrative examples of an adaptive control method that leverages the increasing ability to record and process data online by using specifically selected and online recorded data concurrently with instantaneous data for adaptation. It is shown that when the system uncertainty can be modelled as a combination of known nonlinear bases, simultaneous exponential tracking and parameter error convergence can be guaranteed if the system states are exciting over finite intervals such that rich data can be recorded online; PE is not required. Furthermore, the rate of convergence is directly proportional to the minimum singular value of the matrix containing online recorded data. Consequently, an online algorithm to record and forget data is presented and its effects on the resulting switched closed-loop dynamics are analysed. It is also shown that when radial basis function neural networks (NNs) are used as adaptive elements, the method guarantees exponential convergence of the NN parameters to a compact neighbourhood of their ideal values without requiring PE. Flight test results on a fixed-wing unmanned aerial vehicle demonstrate the effectiveness of the method.     

一种克服不可测扰动的MRAC系统设计方法

对于形式已知的可测干扰,本文给出了一种新的模型参考自适应设计方法。数字仿真结果表明:被控对象能够较好的跟随参考模型的输出,由于构造的控制器结构简单,可调参数较少,因此容易实现,便于工程应用。