基于神经网络的自适应模糊控制器

本文提出了一种基于神经网络的自适应模糊控制器，控制器为５层前向结构，其输入和输出均为数值量。根据给定的训练数据，通过学习算法，能够实现前件参数和后件参数的辨识，提取控制规则，最后通过仿真实验证明了这种方法的有效性。     

一类非线性离散系统的直接自适应模糊控制

针对一类含延迟非线性离散系统,提出了一种直接自适应模糊控制器设计的新方案．将系统用T-S模糊模型来表示,并基于并行分布补偿(PDC)基本思想设计了一种具有未知参数的模糊控制器,同时采用梯度下降算法对该控制器的参数进行在线辨识．通过输入到状态稳定(ISS)方法,证明了系统输出和参考输出的误差有界且满足一定的平均性能．仿真表明本方法的有效性．     

模块化反步自适应大机动飞行控制

针对飞机大机动飞行时模型非线性和参数不确定性的特点,提出一种输入状态稳定反步自适应控制的模块化设计方法.基于模块化设计思想,设计一个输入状态稳定的反步控制器,保证在输入有界情况下系统状态的有界特性.基于最小二乘算法设计参数自适应律和滤波器的辨识器模块,保证独立于输入状态稳定控制器之外的参数误差及其导数有界,并利用一种基于免疫克隆原理的改进粒了群算法优化固定参数,改善动态性能.仿真结果表明了所提出算法的有效性.     

自由漂浮空间机器人末端轨迹优化自适应控制

惯性参数不确定情况下的自由漂浮空间机器人(FFSR)轨迹跟踪控制是当前FFSR自主控制研究的重点与难点之一.针对该问题,提出一种FFSR末端轨迹优化自适应跟踪控制方法.该方法首先基于离散状态依赖黎卡提方程(DSDRE),设计两级DSDRE优化跟踪控制器,然后在控制器输出基础上,通过求解有约束条件下的非线性优化问题实现FFSR惯性参数的辨识,进而根据辨识结果调整控制器相关参数,实现FFSR末端轨迹的优化自适应跟踪控制.最后,采用平面两连杆FFSR模型进行仿真,验证了所提出方法的有效性.     

一种基于过程神经元网络辨识的PID控制模型及方法

针对非线性动态系统PID过程控制问题,提出了一种基于过程神经元网络辨识的PID参数自适应整定的控制模型和方法。利用过程神经元网络对于动态系统时变输入/输出信号的学习机制,在某种最优控制律下通过对被控对象进行辨识来追踪被控对象的输出对控制输入变化的灵敏度信息,实现参数自适应匹配的PID控制。给出了基于过程神经元网络辨识的PID控制系统结构以及相应的实现机制,实验结果验证了模型和算法的有效性。     

一种新的神经网络自适应控制器

1引言神经网络具有很强的非线性逼近能力，现已广泛应用于控制系统的设计［‘］。一种方法是利用它进行自适应控制【’」，另一种方法是利用它来决定控制输出［3］。fg是，在这些文献中，其控制器的设计大多数是离线进行的，且要依赖于系统的较多验前信息。在文卜」中提出了利用神经网络控制器来解决CARMA系统的辨识问题以及在线实现自适应控制。由于多层前向神经网络模型存在固有误差，这里利用文［5」中提出的带有输入补偿的神经网络来辨识系统模型，利用预测误差来估计系统噪声。同时，利用神经网络来辨识系统的逆模，求出控制器的输出…     

基于偏格式线性化的MIMO系统无模型自适应控制

针对常规PID控制器不能很好兼顾抗干扰性与鲁棒性以及基于模型的控制算法过于依赖受控系统数学模型的缺点,提出一种适用于离散时间多输入多输出(MIMO)非线性系统的无模型自适应控制算法.该算法首先通过偏格式线性化方法将非线性系统线性化,再利用一种新型的投影算法在线辨识受控系统参数,根据辨识得到的受控系统参数直接递推计算无模...     

基于随机梯度的双率系统自校正控制方法

针对输入更新频率是输出刷新频率整数倍的未知参数双率系统,设计一个损失输出估计器计算采样间输出,再根据随机梯度算法设计参数估计器并得到系统模型的估计参数,基于最小方差控制原则设计出双率系统的自适应控制器。通过与基于最小二乘方法辨识系统参数的自适应控制算法进行比较,可以看出该算法的计算量较小,尤其是在输入数据更新频率与输出数据刷新频率相差较大时,计算量的差距更加明显。最后用仿真例子说明了该算法的有效性。     

一种鲁棒间接自适应调节器*

本文讨论了确定性连续时间系统的鲁棒自适应调节问题，提出了一种新型间接自适应极点配置控制器，该控制器利用辨识参数在线修正方法，保证系统辨识模型在自适应过程不不失其能控性。本文还证明了闭环系统的全局稳定性。     

含有界扰动系统的多模型自适应控制

对含有有界扰动和参数不确定性的离散时间被控对象建立多个辨识模型, 覆盖被控对象的参数不确定性. 给定指标切换函数, 构成多模型自适应控制器. 引入“局部化”技术, 在保持计算精度的同时, 提高了计算速度. 同时证明, 多模型自适应控制可以保证闭环系统输入输出稳定, 且保证对给定有界参考输入、被控对象输出可在一给定界范围内跟踪参考输入.     

一种新的随机多变量自适应极点配置控制器

本文提出的随机多变量自适应极点配置控制器,由于采用了一种新的极点配置结构,它不仅可以避免求解伪交换矩阵方程而实现任意极点配置,跟踪时变参考输入,而且可以抑制随机噪声干扰.该控制器不仅可以控制开环不稳定或非最小相位系统,而且还可以控制具有任意、未知或变化的延时结构和具有任意输入输出个数的随机多变量系统。     

Adaptive backstepping control design for linear multivariable plants

A nonlinear backstepping scheme is developed for adaptive control of linear plants with multiple inputs and multiple outputs. Solutions to plant parametrization, state observer, and adaptive control law for the multivariable backstepping design are proposed. The developed adaptive controller has the desired properties for ensuring closed-loop signal boundedness and asymptotical tracking.     

An optimal stochastic multivariable PID controller: a direct output tracking approach

This paper deals with the design of an optimal stochastic controller possessing tracking capability of any reference output trajectory in the presence of measurement noise. We consider multi-input multi-output linear time-invariant systems and a proportional-integral-derivative (PID) controller. The system under consideration needs not be stable. A recursive algorithm providing optimal time-varying PID gains is proposed for the case where the number of inputs is larger than or equal to the number of outputs. The development of the proposed algorithm aims for per-time-sample minimisation of the mean-square output error in the presence of erroneous initial conditions, measurement noise, and process noise. Necessary and sufficient conditions are provided for the convergence of the output error covariance. In addition, convergence results are presented for discretised continuous-time plants. Simulation results are included to illustrate the performance capabilities of the proposed algorithm. Performance comparison with an optimal stochastic iterative learning control scheme, an optimal PID controller, an adaptive PID controller, and a recent optimal stochastic PID controller are also included.     

多变量鲁棒自适应极点配置算法

在系统具有一般不确定结构的情况下,提出了一种多变量自适应极点配置算法,解 决了多变量自适应极点配置算法的奇异性问题,证明了它的全局收敛性和稳定性.该算法构 造了一个估计参数的修正策略，保证估计模型的一致能控性,并且得到能控度的一个下界.所 需要的先验知识仅为系统的能观性指数和能控性指数的上界以及干扰上界.     

Distributed Adaptive Output Consensus of Unknown Heterogeneous Non-Minimum Phase Multi-Agent Systems

This article addresses the leader-following output consensus problem of heterogeneous linear multi-agent systems with unknown agent parameters under directed graphs. The dynamics of followers are allowed to be non-minimum phase with unknown arbitrary individual relative degrees. This is contrary to many existing works on distributed adaptive control schemes where agent dynamics are required to be minimum phase and often of the same relative degree. A distributed adaptive pole placement control scheme is developed, which consists of a distributed observer and an adaptive pole placement control law. It is shown that under the proposed distributed adaptive control scheme, all signals in the closed-loop system are bounded and the outputs of all the followers track the output of the leader asymptotically. The effectiveness of the proposed scheme is demonstrated by one practical example and one numerical example.      

Multivariable adaptive control: A survey

Adaptive control is a control methodology capable of dealing with uncertain systems to ensure desired control performance. This paper provides an overview of some fundamental theoretical aspects and technical issues of multivariable adaptive control, and a thorough presentation of various adaptive control schemes for multi-input–multi-output systems, literature reviews on adaptive control foundations and multivariable adaptive control methods, and related technical problems. It covers some basic concepts and issues such as certainty equivalence, stability, tracking, robustness, and parameter convergence. It discusses some of the most important topics of adaptive control: plant uncertainty parametrization, stable controller adaptation, and design conditions for different adaptive control schemes. The paper also presents a detailed study of well-developed multivariable model reference adaptive control theory and design techniques. It provides an introduction to multivariable adaptive pole placement and adaptive nonlinear control, and it concludes by identifying some open research problems.     

多变量解耦极点配置组合自校正前馈控制器

本文提出了一种新的多变量解耦极点配置自校正前馈控制器,它具有使伺服跟踪性能和 随机调节性能两者均优的组合自校正特性.仿真例子说明了其有效性.     

Multivariable control of transmitted vibrations to the seat model of the human body

Advanced control techniques are required for the vibrations suppression of the seated human body in different places like automotive and public transportation lines. In this paper, an active multivariable control strategy is applied to a seated human body model and results are simulated and examined via MATLAB. Dynamic equations of the model are derived using Lagrange method and they are linearized around the equilibrium point of the system. After assessing the deficiencies of the previous models and control strategies proposed for the human body, an active control method is presented based on pole placement analysis. This control strategy is designed for a realistic model of the human body with 5 degrees of freedom (5DOF) and in the presence of road excitations. In the proposed multivariable control system, human body movements in five directions (as the five outputs) are controlled via manipulation of the forces in vertical and horizontal directions and a moment about y-axis (as the three inputs). For the simulation purposes, it is assumed that these control inputs are provided by actuators (e.g., piezo-electrics). Dynamic behavior of the system is evaluated around its natural frequencies and the effectiveness of the proposed active multivariable control is investigated. It is observed that under various resonance conditions, the controller acts efficiently in vibrations suppression.     

Adaptive pole placement pitch angle control of a flapping-wing flying robot

An adaptive pole placement control scheme for the adaptive pitch angle control of a bird-like flapping-wing flying robot is designed and implemented. The salient aims of this work are notably twofold: first, since the dynamics of bird-like flapping-wing robots are still not well understood and hence obfuscate the process of deriving a high-fidelity aerodynamical model, we instead elect to designate the system identification component of the control scheme to provide real-time estimates of the low level robot parameters. Input and output data are collated during flight and the recursive least squares method is applied to obtain real-time parameter estimates. Estimated parameters are subsequently used in designing the control law using adaptive pole placement via the polynomial method where we prescribe the desired closed-loop characteristic equation. Secondly, even if the dynamics of the robot varies over time, it is accounted by the adaptive controller without any need to perform tuning since proportional gain values are spontaneously generated. Numerical simulations are first used to assist the design and validate the correct operation of the control scheme. It is then implemented on a real bird-like flapping-wing flying robot; experimental results obtained exhibit close congruence with simulation results.     

Multipurpose controllers for multivariable systems

The primary purpose of this paper is to outline a new procedure for designing controllers which simultaneously achieve a variety of desired design goals in deterministic, unity feedback, linear multivariable systems. More specifically, we will present a new algorithm for the systematic design of a "three-part" multivariable controller which simultaneously ensures: 1) a noninteractive or decoupled closed-loop design, 2) complete and arbitrary closed-loop pole placement, which implies desired (single-loop) transient performance as well as closed-loop stability, 3) zero steady-state errors between the plant outputs and any nondecreasing deterministic inputs, 4) complete steady-state output rejection of nondecreasing deterministic disturbances, and 5) robustness with respect to stability, disturbance rejection, and zero error tracking for rather substantial plant parameter variations. Our development will employ the more "modern" (Laplace-transformed) differential operator approach for controller synthesis, which involves transfer matrix factorizations and the manipulation of polynomial matrices in the Laplace operators.