基于PSO的变结构变时滞自校正控制

针对变结构、变时滞被控对象,将粒子群优化(PSO)算法与广义最小方差相结合,采用实时自校正过程对其进行控制,提出基于PSO自校正控制器算法.该算法应用隐式辨识方式,可减少辨识计算量,通过跟踪误差来改变辨识精度.以工业上典型的一阶、二阶和三阶系统的结构变化并伴随着有时滞突变的复杂被控对象进行仿真,并和基于最小二乘的传统自校正控制方法比较得知,在运用PSO自校正控制器的控制下,系统输出量与期望输出之间的方差趋于更小,控制跟随性和鲁棒性均较好.仿真结果表明该自校正控制器的有效性与应用价值.     

无刷直流电机神经网络自校正控制研究

针对无刷直流电机的非线性、强耦合、变参数等特点,利用多层神经网络对非线性函数的任意精度拟合性,提出了基于BP神经网络的无刷直流电机神经网络自校正控制。神经网络自校正控制不需要被控对象的准确数学模型,并可以通过在线学习来适应系统工作环境和系统本身参数的变化,给出了控制系统软硬件的具体实现方案。仿真和实验结果表明,神经网络自校正控制的速度跟随性、动态响应过程较之PI控制更好,具有很好的控制效果。     

阀控马达的速度控制研究

对液压阀控马达速度伺服系统进行了研究.在系统建模分析的基础上,分别采用PI控制策略和最小方差自校正控制策略对系统进行了控制,并用Matlab对两种控制结果进行了仿真比较.研究表明,采用最小方差自校正控制比PI控制具有更好的综合效果.     

未知不确定非线性系统的直接自校正滑模控制

针对一类具有未知不确定性的非线性系统,提出一种参数直接自校正滑模控制方法.将系统的非线性、参数变化和外部干扰都视作系统不确定性,控制器的设计无需不确定项的上下界等信息:为改善跟踪性能与减小输入抖振,控制器设计中引入可调边界层厚度的双极性sigmoid函数与可变滑模切换增益,推导出控制增益和边界层厚度的直接自校正律,并基于Lyapunov判据给出了闭环系统稳定性证明.仿真实例证明了该方法的有效性和正确性.     

统一的自校正控制器

本文提出两种统一的广义最小方差自校正控制器,以控制具有任意维数输入/输出的 (ADIADO)系统.这两种新的控制器解决了以前无法处理的输入/输出维数不等、Bo奇异系 统的自校正控制问题.几个典型的仿真例子证明了这两种控制器的统一性和有效性.     

基于竞赛自平衡小车的双闭环控制设计与仿真研究

针对具有多变量、不稳定、非线性特性的自平衡小车系统,提出了一种基于偏角伺服控制与LQR控制相结合的双闭环控制方法.在该方法中,先根据系统的等效模型,采用LQR方法控制自平衡小车的倒立平衡,采用伺服控制进行偏角跟随控制.并且在设计过程中,先保证自平衡小车的倒立平衡,然后再进行伺服控制,运行中两个闭环实时控制,达到使小车倒立快速寻迹运行.数字仿真结果与理论分析基本一致,表明了该方法的可行性.     

基于神经网络的热处理炉温度自校正控制系统

热处理加热炉是一种具有纯滞后的大惯性不确定系统,常规的控制方法很难达到控制要求。本文提出了一种基于神经网络的自校正调节系统,可使加热炉按照预定的温度曲线升温,以获得良好的跟随性能。运行结果表明,这种系统可获得满意的控制效果。     

一类时滞系统的自校正模糊Smith控制器设计

Smith预估控制是解决时滞系统控制的有效方法,但其对系统模型参数的精确要求限制了其应用.而模糊控制本质上属于PD控制,无法消除系统的稳态误差.文中设计了一种自校正模糊Smith 控制器,结合模糊控制、Smith预估器及自校正控制的优点,既保持了模糊控制鲁棒性较强的特点,又消除了系统的稳态误差,且改善了系统的动态性能.仿真结果表明了自校正模糊控制器的有效性和优越性.     

一种带有模型误差反馈的鲁棒自校正控制器

本文提出一种新的带有模型误差反馈的自校正控制算法，并且证明了：在有界外部扰动及由模型阶次不匹配生产未建模动态存在的条件下，不论系统是否为最小相位，本文算法都能保证闭环系统是鲁棒稳定的，而且平均跟随误差趋于零，仿真结果验证了此算法的有效性。     

极点配置自校正PID调节器及其应用

提出一种设计极点配置自校正ＰＩＤ调节器的新方法。该方法具有设计简单，工程意义直观、鲁棒性强和容易实现等优点。并在电阻加热炉温度控制系统中获得了令人满意的控制效果。对自校正控制理论及应用的研究具有一定的参考价值。     

电驱动刚性机器人的鲁棒神经网络复合控制

采用逐步逆向的设计思想，提出一种新的电驱动刚性机器人轨迹跟踪的鲁棒神经网络复合控制策略，该策略不仅能有效地消除模型不确定性的影响，而且可避免复杂的求导运算以及对关节角加速度可测的要求。给出了控制器的具体组成和神经网络连接权的在线学习算法，理论表明该算法能保证跟踪误差及神经网络连接权估计最终一致有界，仿真结果也验证了算法的有效性。     

On the stability of a self-tuning controller in the presence ofbounded disturbances

A proof of bounded-input bounded-output stability of a discrete-time self-tuning control system in the presence of bounded disturbances is given. A recursive least-squares with a dead zone is utilized in the parameter estimation scheme, so that self-tuning takes place only when the identification error exceeds a certain threshold. The controller minimizes a quadratic cost function so that the control signal fluctuations are penalized. The controller can deal with nonminimum-phase systems, which is significant because the nonminimum-phase cases often occur in discrete-time systems. The upper bound of tracking error is also given     

Position Control of a Flexible Manipulator Using a New Nonlinear Self-Tuning PID Controller

In this paper, a new nonlinear self-tuning PID controller(NSPIDC) is proposed to control the joint position and link deflection of a flexible-link manipulator(FLM) while it is subjected to carry different payloads. Since, payload is a critical parameter of the FLM whose variation greatly influences the controller performance. The proposed controller guarantees stability under change in payload by attenuating the non-modeled higher order dynamics using a new nonlinear autoregressive moving average with exogenous-input(NARMAX) model of the FLM. The parameters of the FLM are identified on-line using recursive least square(RLS) algorithm and using minimum variance control(MVC) laws the control parameters are updated in real-time. This proposed NSPID controller has been implemented in real-time on an experimental set-up. The joint tracking and link deflection performances of the proposed adaptive controller are compared with that of a popular direct adaptive controller(DAC). From the obtained results, it is confirmed that the proposed controller exhibits improved performance over the DAC both in terms of accurate position tracking and quick damping of link deflections when subjected to variable payloads.     

新型多变量鲁棒自校正解耦控制直接算法

本文提出了一种新型多变量鲁棒自校正解耦控制直接算法，由于在算法中采用了一种新的控制律，所以较彻底地克服了系统未建模动态和不可测干扰的影响，在系统具有较强的未建动态和有界不可测干扰的情况下，本文算法不仅可以保证系统的稳定性，而且实现了鲁棒自校正解耦控制，在算法中采用一种新的方法确定正则化系数和死区界，这种方法保证了系统跟踪特性不受死区和正则化的影响，本文还给出了所提出算法的鲁棒性分析和仿真结果。     

Nonlinear adaptive control using neural networks and its application to CSTR systems

In this paper, adaptive tracking control is considered for a class of general nonlinear systems using multilayer neural networks (MNNs). Firstly, the existence of an ideal implicit feedback linearization control (IFLC) is established based on implicit function theory. Then, MNNs are introduced to reconstruct this ideal IFLC to approximately realize feedback linearization. The proposed adaptive controller ensures that the system output tracks a given bounded reference signal and the tracking error converges to an -neighborhood of zero with being a small design parameter, while stability of the closed-loop system is guaranteed. The effectiveness of the proposed controller is illustrated through an application to composition control in a continuously stirred tank reactor (CSTR) system.     

Robust adaptive tracking control of robotic systems with uncertainties

To deal with the uncertainty factors of robotic systems, a robust adaptive tracking controller is proposed. The knowledge of the uncertainty factors is assumed to be unidentified; the proposed controller can guarantee robustness to parametric and dynamics uncertainties and can also reject any bounded, immeasurable disturbances entering the system. The stability of the proposed controller is proven by the Lyapunov method. The proposed controller can easily be implemented and the stability of the closed system can be ensured; the tracking error and adaptation parameter error are uniformly ultimately bounded （UUB）. Finally, some simulation examples are utilized to illustrate the control performance.     

The neural sliding mode controller design of fan-plate system

This paper proposed a sliding mode angle control with neural network estimator design for a fan-plate system. The neural network estimator is based on radial basis function and it estimates the unknown lumped bounded uncertainty of parameter variations and external disturbances in real-time. The abilities of anti-disturbance and anti-chattering are better than conventional sliding mode controller and adaptive sliding mode controller. The Lyapunov stability theorem is employed to ensure the stability of the proposed controller. The convergence and signal tracking properties are better than the conventional sliding mode controller. Finally, we employed the experiment to validate the proposed method is feasible.     

A robust adaptive sliding-mode control for rigid robotic manipulators with arbitrary bounded input disturbances

In this paper, a robust adaptive sliding-mode control scheme for rigid robotic manipulators with arbitrary bounded input disturbances is proposed. It is shown that the prior knowledge on the upper bound of the norm of the input disturbance vector is not required in the sliding-mode controller design. An adaptive mechanism is introduced to estimate the upper bound of the norm of the input disturbance vector. The estimate is then used as a controller gain parameter to guarantee that the output tracking error asymptotically converges to zero and strong robustness with respect to bounded input disturbances can be obtained. A simulation example is given in support of the proposed control scheme.     

力矩输入有界的柔性关节机器人轨迹跟踪控制

为解决柔性关节机器人在关节驱动力矩输出受限情况下的轨迹跟踪控制问题,提出一种基于奇异摄动理论的有界控制器.首先,利用奇异摄动理论将柔性关节机器人动力学模型解耦成快、慢两个子系统.然后,引入一类平滑饱和函数和径向基函数神经网络非线性逼近手段,依据反步策略设计了针对慢子系统的有界控制器.在快子系统的有界控制器设计中,通过关节弹性力矩跟踪误差的滤波处理加速系统的收敛.同时,在快、慢子系统控制器中均采用模糊逻辑实现控制参数的在线动态自调整.此外,结合李雅普诺夫稳定理论给出了严格的系统稳定性证明.最后,通过仿真对比实验验证了所提出控制方法的有效性和优越性.     

Locomotion Control of a Hydraulically Actuated Hexapod Robot by Robust Adaptive Fuzzy Control with Self-Tuned Adaptation Gain and Dead Zone Fuzzy Pre-compensation

Hydraulically actuated robotic mechanisms are becoming popular for field robotic applications for their compact design and large output power. However, they exhibit nonlinearity, parameter variation and flattery delay in the response. This flattery delay, which often causes poor trajectory tracking performance of the robot, is possibly caused by the dead zone of the proportional electromagnetic control valves and the delay associated with oil flow. In this investigation, we have proposed a trajectory tracking control system for hydraulically actuated robotic mechanism that diminishes the flattery delay in the output response. The proposed controller consists of a robust adaptive fuzzy controller with self-tuned adaptation gain in the feedback loop to cope with the parameter variation and disturbances and a one-step-ahead fuzzy controller in the feed-forward loop for hydraulic dead zone pre-compensation. The adaptation law of the feedback controller has been designed by Lyapunov synthesis method and its adaptation rate is varied by fuzzy self-tuning. The variable adaptation rate helps to improve the tracking performance without sacrificing the stability. The proposed control technique has been applied for locomotion control of a hydraulically actuated hexapod robot under independent joint control framework. For tracking performance of the proposed controller has also been compared with classical PID controller, LQG state feedback controller and static fuzzy controller. The experimental results exhibit a very accurate foot trajectory tracking with very small tracking error with the proposed controller.