基于反馈线性化同步伺服电机鲁棒跟踪控制

为了保证具有不确定非线性的PM同步伺服电机驱动系统的稳定性,确保闭环系统的输出准确跟踪期望输出并减少不确定项对该驱动系统的影响,利用Lyapunov稳定性理论,设计了一种基于反馈线性化的鲁棒跟踪控制器,并作了相应的仿真研究。仿真结果表明,该控制器不仅确保闭环系统的输出按指数规律跟踪期望输出,而且保证闭环系统状态的一致最终有界。该控制器设计简单,易于实现,具有很好的实用性。     

近似线性化方法综述

回顾了精确线性化方法及目前存在的主要问题,讨论了基于平衡流型和工作点邻域的状态方程近似线性化问题,研究了输入输出近似线性化在自理非最小相位或不可控非线性问题时的作用。最后对未来的研究方向进行了探讨。     

Dynamic Load Carrying Capacity of Flexible Cable Suspended Robot: Robust Feedback Linearization Control Approach

In this paper dynamic load carrying capacity (DLCC) of a cable robot equipped with a closed loop control system based on feedback linearization, is calculated for both rigid and flexible joint systems. This parameter is the most important character of a cable robot since the main application of this kind of robots is their high load carrying capacity. First of all the dynamic equations required for control approach are represented and then the formulation of control approach is driven based on feedback linearization method which is the most suitable control algorithm for nonlinear dynamic systems like robots. This method provides a perfect accuracy and also satisfies the Lyapunov stability since any desired pole placement can be achieved by using suitable gain for controller. Flexible joint cable robot is also analyzed in this paper and its stability is ensured by implementing robust control for the designed control system. DLCC of the robot is calculated considering motor torque constrain and accuracy constrain. Finally a simulation study is done for two samples of rigid cable robot, a planar complete constrained sample with three cables and 2 degrees of freedom and a spatial unconstrained case with six cables and 6 degrees of freedom. Simulation studies continue with the same spatial robot but flexible joint characteristics. Not only the DLCC of the mentioned robots are calculated but also required motors torque and desired angular velocity of the motors are calculated in the closed loop condition for a predefined trajectory. The effectiveness of the designed controller is shown by the aid of simulation results as well as comparison between rigid and flexible systems.     

Neural Network Identification Based Multivariable Feedback Linearization Robust Control for a Two-Link Manipulator

Regarding to the variations of the load and unmodeled dynamic, robot manipulators are known as a nonlinear dynamic system. Overcoming such problems like uncertainties and nonlinear characteristics in the model of two-link manipulator is the principal goal of this paper. To approach to this aim, a neural network is combined with a linear robust control in which the result has the advantages of, the first, approximated nonlinear elements and the second, the guaranteed robustness. To design the proposed controller, at first, multivariable feedback linearization is employed to convert the nonlinear model to linear one. Second, the unknown parameters of the system are identified by neural network based on a new proposed learning rule. Third, Mixed linear feedback-H_?∞? robust control method is proposed to stabilize the closed loop system. The closed loop system based on the proposed controller is analyzed and some numerical simulations are performed. Results show suitable responses of the closed loop system.     

单级倒立摆反馈线性化控制仿真

本文以单级倒立摆为控制对象,介绍了反馈线性化的基本原理,设计神经网络控制器对消系统的非线性,实现对单级倒立摆的反馈线性化控制。并通过MATLAB软件进行仿真实验,结果表明了该方法的有效性。     

Optimal Continuous Stochastic Control Systems with Incomplete Feedback: Approximate Synthesis

The sufficient ε-optimality conditions for the control of the nonlinear continuous stochastic systems with incomplete feedback were formulated and proved. They enabled one to estimate the precision of approximate control as compared with the value-optimal performance functional. Relations were established to determine the ε-optimal control, and a strategy was worked out to determine its use in minimizing the total mismatch of the resulting relations.     

Feedback Linearization Control for Systems with Mismatched Uncertainties via Disturbance Observers

This paper focuses on a novel feedback linearization control (FLC) law based on a self‐learning disturbance observer (SLDO) to counteract mismatched uncertainties. The FLC based on BNDO (FLC‐BNDO) demonstrates robust control performance only against mismatched time‐invariant uncertainties while the FLC based on SLDO (FLC‐SLDO) demonstrates robust control performance against mismatched time‐invariant and ‐varying uncertainties, and both of them maintain the nominal control performance in the absence of mismatched uncertainties. In the estimation scheme for the SLDO, the BNDO is used to provide a conventional estimation law, which is used as the learning error for the type‐2 neuro‐fuzzy system (T2NFS), and T2NFS learns mismatched uncertainties. Thus, the T2NFS takes the overall control of the estimation signal entirely in a very short time and gives unbiased estimation results for the disturbance. A novel learning algorithm established on sliding mode control theory is derived for an interval type‐2 fuzzy logic system. The stability of the overall system is proven for a second‐order nonlinear system with mismatched uncertainties. The simulation results show that the FLC‐SLDO demonstrates better control performance than the traditional FLC, FLC with an integral action (FLC‐I), and FLC‐BNDO.     

鲁棒H-状态反馈控制

本文对结构参数不确定线性定常多变量系统的鲁棒Ｈ∞状态反馈设计问题进行了研究，给出了系统鲁棒稳定和干扰衰减的条件，及相应的鲁棒Ｈ∞状态反馈设计方法，并用这种方法设计了一个倒立摆平衡控制器。仿真结果表明，与ＬＱ设计方法相比，按本文所提出的设计方法得到的控制器具有鲁棒稳定能力强、闭环动态性能良好等优点。     

Robust Stable Fuzzy Control via Fuzzy Modeling and Feedback Linearization with Its Applications to Controlling Uncertain Single-link Flexible Joint Manipulators

In this paper, a robust stable fuzzy control design based on feedback linearization is presented. Takagi–Sugeno fuzzy model is used as representing the nonlinear plant model and uncertainty is assumed to be included in the model structure with known bounds. For this structured uncertainty, the closed system can be analyzed by applying the perturbation system stability analysis to the fuzzy feedback linearization systems and a sufficient condition is derived to guarantee the stability of the closed-loop system with bounded parameter uncertainties. Based on the developed analysis method, we can design a robust fuzzy controller by choosing the control parameters satisfying the robust stability condition.     

基于反馈线性化的STATCOM控制研究

利用基尔霍夫定律建立STATCOM开关函数模型,采用反馈线性化解耦控制方法,实现STATCOM非线性系统有功电流和无功电流的解耦,并基于线性控制理论中的极点配置方法为解耦线性化模型设计了控制器;并根据瞬时无功功率理论对负载线路中的无功电流进行动态实时检测,作为STATCOM补偿无功功率的给定,实现对无功变化负载的快速响...     

非线性反馈线性化方法在控制pH过程中的应用

pH过程广泛存在于化工、生物工艺、污水处理、发电等工业中,对这一过程进行良好的控制有着十分重要的意义.本文主要介绍了对pH过程使用反馈线性化的非线性控制设计.这种设计能较好的处理pH过程的非线性和滞后性.     

基于反馈线性化的永磁同步电机模型预测控制

提出一种基于反馈线性化和模型预测控制(MPC)策略的永磁同步电机(PMSM)控制方案.运用微分几何理论讨论了非线性PMSM模型可进行反馈线性化的充分必要条件,并将其转换为新坐标空间中的线性模型;分析了MPC原理和对系统约束条件的处理方法.针对获得的PMSM线性模型,分别采用MPC和状态反馈极点配置方法设计了控制器.在有...     

Nonlinear Control VIA Generalized Feedback Linearization Using Neural Networks

A novel approach to nonlinear control, called Generalized Feedback Linearization (GFL), is presented. This new strategy overcomes one important drawback of the well known Feedback Linearization strategy, in the sense that it is able to handle a broader class of nonlinear systems, namely those having unstable zero dynamics. It is shown that the use of a nonlinear predictor for the system output is a key feature in the derivation of the control strategy. For certain types of systems this predictor can be found as a nonlinear function of the system input and output, allowing an output feedback control solution. The use of Artificial Neural Networks (ANN) to directly parameterize the predictor of the controlled variable when an explicit model for the system is not available, is investigated via computer simulations. This approach is based on the functional approximation capability of multi layer ANN.     

基于反馈线性化的无人直升机航向控制

本文着重解决小型无人直升机航向自适应控制问题．通过求非线性函数导数,把原始系统扩展为一个带有伪状态变量的新系统．这种方法不必求解非线性函数的逆,并且降低了计算量．证明了该方法的稳定性．针对实际模型直升机实验平台航向动力学模型,仿真结果表明了该方法的有效性．     

一种非线性反馈线性化的控制方法及其应用

对基于微分几何的输入／输出反馈线性化处理方法作了研究，并将其应用于飞机的非线性控制方程中．同时对飞机自动着陆的纵向飞行轨迹设计了跟踪控制方案。     

参数摄动系统的鲁棒LQ反馈控制

本文研究了不确定系统的鲁棒LQ反馈控制器的综合问题.对于具有线性有界实参数摄动的系统,首先把不确定系统的鲁棒LQ综合问题转化成N个确定系统的联立LQ综合问题.并据此提出了相应的综合算法.理论分析和实例计算结果表明所提出的方法具有较广的应用范围和较少的保守性.     

反馈线性化最优滑模变结构励磁控制

将滑模变结构控制具有对摄动的完全自适应性与反馈线性化理论和最优控制理论结合起来,针对励磁系统,提出了一种基于反馈线性化最优的滑模变结构控制方法,给出了控制器的设计过程,避免了通常设计滑模变结构控制器时参数选取的盲目性,保证系统运行在较佳状态,且按指数趋近律设计的滑模变结构控制器不易产生抖动。仿真结果表明,这种方法能够充分发挥滑模变结构控制的鲁棒性优点,对电力系统具有较好的稳定作用。     

反馈线性化方法在双容系统中的仿真应用

针对双容系统这一典型的非线性、多变量、时变的控制对象,为了提高系统跟踪性能,首先利用非线性微分几何理论中的输入-输出反馈线性化方法,即选择合适的系统输出,并对各分量连续求导直至其表达式中出现控制输入变量,当满足一定条件后通过一定的控制输入变量替换就可将系统全局线性化,进而可以利用成熟线性控制理论对得到的线性化子系统进行控制,从而实现闭环系统的指数稳定.仿真研究表明,所提方法是有效的,且设计的闭环系统具有良好的静、动态性能。     

Adaptive Feedback Control by Constrained Approximate Dynamic Programming

A constrained approximate dynamic programming (ADP) approach is presented for designing adaptive neural network (NN) controllers with closed-loop stability and performance guarantees. Prior knowledge of the linearized equations of motion is used to guarantee that the closed-loop system meets performance and stability objectives when the plant operates in a linear parameter-varying (LPV) regime. In the presence of unmodeled dynamics or failures, the NN controller adapts to optimize its performance online, whereas constrained ADP guarantees that the LPV baseline performance is preserved at all times. The effectiveness of an adaptive NN flight controller is demonstrated for simulated control failures, parameter variations, and near-stall dynamics.      

基于状态反馈精确线性化的三相四桥臂逆变器的控制

根据三相四桥臂逆变器的工作原理,应用开关函数建立了控制系统数学模型,引入开关周期平均算子将离散的系统转化为连续系统.根据系统的主要控制目标选取状态变量、输入变量和输出变量,得到适合于微分几何方法的3输入3输出的仿射非线性系统模型.根据非线性微分几何理论,从理论上证明了该模型满足多输入、多输出系统精确线性化的条件,推导出非线性状态反馈控制律.对非线性坐标变换后得到的线性系统,利用二次型最优控制策略时,根据无源性控制方法的思想,提出一种闭环系统能量函数,并推导出权矩阵的参数形式.将最优化得到的控制律进行逆变换来实现原系统的优化控制设计.仿真结果验证了该方法的有效性和正确性.