间接型的混杂模型参考自适应控制

针对相对阶为1的理想系统,本文考虑了具有混杂自适应律的间接型模型参考自适应控制问题.通过建立系统和控制器的离散参数估计和它们的插值四者之间关系的性质,严格地分析了闭环系统的稳定性,证明了闭环系统中所有的信号都一致有界,并且跟踪误差渐进收敛于零.     

矩阵分解的多变量模型参考自适应控制

研究了基于高频增益矩阵分解的多变量模型参考自适应控制 .为此首先基于矩阵分解构造出新的参数化方程 ,选取必然等价自适应控制律 .然后 ,引入规范化信号和类Lyapunov函数 ,获得了规范化自适应律 .并进一步给出了虚拟规范化信号的有用性质 .最后 ,严格地给出了闭环系统的稳定性和收敛性分析     

一类直接模型参考Backstepping自适应控制

提出一种新的直接模型参考Backstepping自适应控制系统结构:谈系统在直接模型参考卓适应控制(MRAC)结构的基础上,增加了Beckstepping控制信号发生器,通过Backstepping方法的灵活设计获得良好的过渡过程品质,得到直接MRAC在稳定性和鲁棒性设计等方面的优点.采用高阶调节器设计了未具规范化的直接模型参考Backstepping自适应律,克服了传统自适应律引入规范化信号后使系统过渡过程品质下降的缺点.     

Integrated model reference adaptive control and time-varying angular rate estimation for micro-machined gyroscopes

Owing to the imposed but undesired accelerations such as quadrature error and cross-axis perturbation, the micro-machined gyroscope would not be unconditionally retained at resonant mode. Once the preset resonance is not sustained, the performance of the micro-gyroscope is accordingly degraded. In this article, a direct model reference adaptive control loop which is integrated with a modified disturbance estimating observer (MDEO) is proposed to guarantee the resonant oscillations at drive mode and counterbalance the undesired disturbance mainly caused by quadrature error and cross-axis perturbation. The parameters of controller are on-line innovated by the dynamic error between the MDEO output and expected response. In addition, Lyapunov stability theory is employed to examine the stability of the closed-loop control system. Finally, the efficacy of numerical evaluation on the exerted time-varying angular rate, which is to be detected and measured by the gyroscope, is verified by intensive simulations.     

基于模型参考自适应Smith预估器的反馈式AGC厚度控制系统

针对具有纯时间延迟特性的反馈式AGC(automatic gauge contro1)系统,利用Smith预估器对纯时间延迟具有补偿作用的原理,建立了模型参考白适应Smith预估器反馈式AGC控制系统模型,给出了控制系统结构.推导了自适应调节律和设计算法,结合预估器模型,计算得到轧件出口厚度偏差值的表达式.仿真结果表明,模型参考自适应Smith预估器反馈式AGC系统对铝薄板带材轧制出口厚度偏差控制具有很好的控制效果,解决了反馈式AGC厚度控制由于纯时间延迟特性对控制性能产生很大影响的问题,消除了出口厚度波动现象,响应速度加快,可以满足高精度铝薄板带材轧制生产的控制要求.     

Direct model reference adaptive control using Kp = LDU factorization for multivariable discrete-time plants

For a large class of discrete-time multivariable plants with arbitrary relative degrees, the design and analysis of the direct model reference adaptive control scheme are investigated under less restrictive assumptions. The algorithm is based on a new parametrization derived from the high frequency gain matrix factorization Kp=LDU under the condition that the signs of the leading principal minors of/fp are known. By reproving the discrete-time Lp and L2σ norm relationship between inputs and outputs, establishing the properties of discrete-time adaptive law, defining the normalizing signal, and relating the signal with all signals in the closed-loop system, the stability and convergence of the discrete-time multivariable model reference adaptive control scheme are analyzed rigorously in a systematic fashion as in the continuous-time case.     

具有未规范化自适应律的直接型模型 参考自适应反推控制器

对于相对阶m>3的系统,给出了具有未规范化自适应律的直接型模型参考自适应反推控制器的设计和性能分析,从而解决了对于任意相对阶系统,具有未规范化自适应律的直接型模型参考自适应控制器的设计和性能分析问题.     

Direct and indirect model reference adaptive control

The paper considers the control of an unknown linear time-invariant plant using Direct and Indirect Model Reference Adaptive Control. Employing a specific controller structure and the concept of positive realness, adaptive laws are derived using Indirect Control which are identical to those obtained in the case of Direct Control. The stability questions that arise are also shown to be the same. Simulation results using the new scheme are presented for the control of both stable and unstable plants.     

On fractional order composite model reference adaptive control

This paper presents a novel composite model reference adaptive control approach for a class of fractional order linear systems with unknown constant parameters. The method is extended from the model reference adaptive control. The parameter estimation error of our method depends on both the tracking error and the prediction error, whereas the existing method only depends on the tracking error, which makes our method has better transient performance in the sense of generating smooth system output. By the aid of the continuous frequency distributed model, stability of the proposed approach is established in the Lyapunov sense. Furthermore, the convergence property of the model parameters estimation is presented, on the premise that the closed-loop control system is stable. Finally, numerical simulation examples are given to demonstrate the effectiveness of the proposed schemes.     

Robust model reference adaptive control for transient performance enhancement

To circumvent the potentially poor transient response induced by nonlinear uncertain dynamics in the adaptive control system, this article proposes a new model reference adaptive control design scheme to improve its transient control response. We first construct a compensator to online extract the undesired dynamics in the online learning, which is incorporated into the reference model and control simultaneously. Then, an error feedback term is incorporated into the reference model to speed up the convergence of both the compensator and tracking error. Moreover, a new leakage term containing the estimation error is constructed and then added in the adaptive law to guarantee the convergence of both the estimation error and tracking error. To further reveal the mechanisms behind these proposed methods, a new methodology to analyze the transient error bounds based on L₂‐norm and Cauchy‐Schwartz inequality is also developed. Based on the analysis results, we find that the proposed methods can effectively reduce the bound of the tracking error and thus achieve an improved transient control performance without violating the system stability even with high‐gain adaptation. In addition, the frequency‐domain analysis is resorted to show the comparative responses of different adaptive laws, which indicate that the proposed adaptive law can maintain the stability margin even with a high‐gain learning rate. A numerical example is given to demonstrate improved control responses of these proposed schemes.     

Model reference adaptive impedance control for physical human-robot interaction

This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.     

Higher order direct model reference adaptive control with generic uniform ultimate boundedness

This paper proposes a new higher order model reference adaptive control (HO-MRAC) approach following direct adaptive control philosophy, which estimates unknown time-varying parameters. This approach leads to a Lyapunov based conventional MRAC update law, augmented by an observer type parameter predictor dynamics. The predictor dynamics are composed of a stable known part, a feedback of the parameter error and unknown higher order parameters, which are updated using a Lyapunov based adaptive design. So, this HO-MRAC can cope with rapidly changing parameters, due to estimation of their time derivatives. Moreover, for stability analysis, a Lyapunov based generic ultimate boundedness theorem is presented, which allows for a computation of separate bounds for each state vector partition. Furthermore, this theorem formulates the explicit specification of transient and ultimate bounds, reaching time on the ultimate bounds and a set of admissible initial conditions. Two challenging illustrative examples demonstrate the effectiveness of the proposed approach.     

The study on the model reference adaptive control with proportional derivative action

This article presents a study on the novel method of model reference adaptive control (MRAC). As a new contribution, the derivative action is included into MRAC control to improve the controller performance and efficiency. The stability is analysed using a recent technique called immersion and invariance. The simulations of derived laws are presented based on nonlinear models.     

一种具有强鲁棒性的模型参考自适应控制系统

提出一种模型参考自适应控制系统．该系统只要求知道对象阶次的上界,不要求对象严格正实和最小相位,并且允许存在建模误差和作用在对象任何部位的大幅值高频干扰．系统的强鲁棒性来源于特殊的系统结构和对信号的良好滤波．Lyapunov稳定性理论证明了系统的渐近稳定性,Matlab仿真验证了系统的强鲁棒性．     

Robustness analysis of discrete-time indirect model reference adaptive control with normalized adaptive laws

For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control (MRAC) with normalized adaptive law are investigated. The main work includes three parts. Firstly, it is shown that the constructed parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms, but also avoids the possibility of division by zero. Secondly, by establishing a relationship between the plant parameter estimate and the controller parameter estimate, some similar properties of the latter axe also established. Thirdly, by using the relationship between the normalizing signal and all the signals of the closed-loop system, and some important mathematical tools on discrete-time systems, as in the continuous-time case, a systematic stability and robustness analysis approach to the discrete indirect robust MRAC scheme is developed rigorously.     

On the convergence of a model reference adaptive control algorithm with unknown high frequency gain

A model reference adaptive control algorithm is shown not to be exponentially convergent when the high frequency gain of the plant is unknown. The implication is that such an algorithm will be lacking in some way in robustness.     

基于强化学习的模型参考自适应控制

提出了一种基于强化学习的模型参考自适应控制方法,控制器采用自适应启发评价算法,它由两部分组成:自适应评价单元及联想搜索单元.由参考模型给出系统的性能指标,利用系统反馈的强化信号在线更新控制器的参数.仿真结果表明:基于强化学习的模型参考自适应控制方法可以实现对一类复杂的非线性系统的稳定控制和鲁棒控制,该控制方法不仅响应速度快,而且具有较高的学习速率,实时性较强.     

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.     

基于系统辨识的模型参考自适应控制

介绍了荷兰Twente大学控制工程系研发的先进控制实验平台的基本组成、功能和基于机电一体化设计软件20-sim。以先进控制演示仪为研究对象,在20-sim环境下建立了系统的图标化数学模型,并从理论上对模型作了降阶分析。根据系统辨识的模型参考自适应控制（MRAC）原理,编制了模型参考自适应和Lyapunov求解模块。采用线性二次型调节器为控制模块,构成了完整的先进控制演示仪的MRAC控制系统。该系统仿真结果显示所建立的模型针对性强,可用于各种先进控制策略的仿真研究。     

两轮自平衡小车模型参考自适应控制平衡算法

两轮自平衡小车是一类非完整的本征不稳定系统,其动力学方程具有复杂非线性、高阶次、强耦合、欠驱动等特点,采用牛顿力学分析法进行系统建模,利用前馈、反馈以及输入量构成自适应机构,在此基础上提出模型参考自适应控制（Model Reference Adaptive Control,MRAC）策略对两轮自平衡小车的姿态和速度进行控制。通过仿真,结果表明采用MRAC算法能够在保证系统稳定的前提下,获得接近于给定理想参考模型的动态性能,并使系统在平衡点附近具有良好的鲁棒性。