具有输入饱和的不确定非线性系统自适应渐近跟踪控制

将一类具有输入饱和的严格反馈单输入单输出非线性系统作为研究对象,解决其自适应渐近跟踪控制问题.与已有结果不同,所考虑的虚拟控制参数可以是未知且增益函数的上界信息也是未知的,这给控制器的设计带来了挑战.通过结合光滑函数及有界估计方法,设计一种新颖的自适应渐近跟踪控制策略;其次,通过引入Nassbaum函数解决由输入饱和不确定参数以及未知虚拟控制参数带来的影响;此外,通过利用未知增益的下界信息巧妙地构造一个特殊的李雅普诺夫函数并结合不等式技巧,可以消除对控制增益函数上界信息的需要,并保证系统的全局稳定性和跟踪性能;最后,通过实例仿真及对比仿真表明所提出自适应渐近跟踪控制算法的有效性.     

具有未知函数非线性系统的全局渐近稳定控制

本文讨论了一类具有未知函数和未知控制方向非线性系统的全局渐近稳定问题.通过提出一个引理处理未知函数问题,从而得到了一种基于反步法和Nussbaum增益技术的全局渐近稳定控制算法.与逼近方法处理未知函数的算法相比,本文提出的算法解决了非线性系统的全局渐近稳定问题;与现存解决非线性系统的全局渐近稳定控制算法相比,本文避免了使用未知函数的假设条件,因此降低了保守性.值得一提的是本文的算法也解决了反步法的“微分爆炸”问题,因此所提出的控制方案不仅仅得到了全局渐近稳定控制方案,而且降低了计算的复杂性.最后,将该方案应用到刚性单链杆机械手系统中,仿真结果验证了其有效性.     

具有未知输入增益的非线性系统的跟踪控制

针对具有未知输入增益的非线性系统, 提出了一种可实现系统输出跟踪控制的自适应控制方法. 通过在backstepping设计中引入一种新的Nausbaum增益, 按该方法设计的控制器可以在系统输入增益未知的情况下实现系统输出的渐近跟踪.     

一类大系统的分散自适应模糊滑模控制

研究了一类具有未知函数控制增益的非线性大系统的分散模糊控制问题.基于滑模控制原理和模糊集理论,提出了一种分散自适应模糊控制器的设计方法.通过理论分析,证明了分散自适应模糊控制系统是全局稳定的,跟踪误差可收敛到零的一个领域内.     

一类非线性时滞系统自适应控制研究

针对一类非线性时滞系统，本文提出一种自适应控制器的设计方案，采用backstepping和domination方法构建了一个无记忆自适应控制器。放松了对非线性时滞函数的要求(例如全局Lipschitz条件)，实现了对给定目标轨线的全局渐近跟踪，保证了闭环系统所有信号全局一致有界：基于Lyapunov—Krasoviskii泛函方法证明了闭环系统的稳定性。仿真结果说明了这种控制方法的可行性和优点。     

非线性增益递归滑模动态面自适应NN控制

针对一类严反馈非线性不确定系统的跟踪控制问题,提出一种非线性增益递归滑模动态面 (Dynamic surface control, DSC)自适应控制方法. 通过设计一个新的非线性增益函数,并构造递归滑模动态面的控制策略和新的Lyapunov函数,同时利用神经网络在线逼近系统不确定项, 该方法有效解决了具有输入饱和约束条件下系统控制精度与动态品质间的矛盾,增强了控制器对其自身参数摄动的非脆弱性. 理论证明了闭环系统所有状态是半全局一致最终有界的,且跟踪误差可收敛至任意小.     

全局稳定的PD+前馈机器人鲁棒自适应控制

研究应用PD+前馈控制结构的不确定性机器人轨迹跟踪问题.在忽略摩擦力和外部扰动情况下,设计了一大类综合的自适应控制策略,能保证系统全局的渐近稳定;在摩擦力和外部扰动存在时,提出两种新颖的鲁棒自适应混合控制方法,不仅可以保证闭环系统的全局稳定性,同时还能给出系统清晰的暂态性能.严格的理论证明和二自由度机器人的仿真验证了控制器的有效性.     

导弹纵向动力系统自适应反演积分控制研究

研究导弹的纵向高精度控制问题.导弹运动数学模型可以看成一组非线性微分方程的动力系统,在对其进行纵向运动控制过程中,传统方法使用小扰动理论进行分段线性化,分别求取增益调度在各段的控制参数.在大扰动条件下,由于动力系数的剧烈变化,使得模型参数存在不确定性问题,导致传统的增益调度算法控制器无法保证控制系统的稳定性.提出了一种应用李雅普洛夫法和反演技术设计的参数自适应非线性跟踪控制器,同时在反演镇定函数中加入积分项以消除稳态跟踪误差,使系统能够全局渐近跟踪参考指令.仿真结果表明,改进方法对于参数不确定的导弹纵向运动控制有很好的自适应性,能够在2秒内跟踪到俯仰角输入信号,并且消除跟踪误差,同时保证控制器具有全局稳定性.     

一类非线性时滞系统的自适应模糊动态面控制

针对一类具有未知方向增益函数的严格反馈非线性时滞系统, 提出了一种自适应模糊动态面控制(Dynamic surface control, DSC)算法. 通过利用DSC设计技术和Lyapunov-Krasovskii函数, 该算法不仅克服了计算膨胀的问题, 而且补偿了未知的时滞. 采用Nussbaum函数解决了虚拟控制增益的符号问题, 并且避免了控制器的奇异性. 所设计的控制器保证了闭环系统所有的状态和信号是半全局有界的, 并且通过选择合适的设计参数可使跟踪误差为任意小. 仿真结果表明了所提出控制器的有效性.     

一类MIMO非线性系统的直接自适应模糊滑模控制

针对一类具有下三角形函数控制增益矩阵的非线性系统, 基于滑模控制原理, 并利用Ⅱ型模糊系统的逼近能力, 提出了一种直接自适应模糊滑模控制器设计的新方案. 通过引入积分型李雅普诺夫函数及逼近误差自适应补偿项, 证明了闭环系统是全局稳定的, 跟踪误差收敛到零. 仿真结果表明了该方法的有效性.     

Nonlinear asymptotic attitude tracking control of an underactuated 3-degree-of-freedom helicopter using neural network feedforward term

This paper proposes a new asymptotic attitude tracking controller for an underactuated 3-degree-of-freedom (DOF) laboratory helicopter system by using a nonlinear robust feedback and a neural network (NN) feedforward term. The nonlinear robust control law is developed through a modified inner-outer loop approach. The application of the NN-based feedforward is to compensate for the system uncertainties. The proposed control design strategy requires very limited knowledge of the system dynamic model, and achieves good robustness with respect to system parametric uncertainties. A Lyapunov-based stability analysis shows that the proposed algorithms can ensure asymptotic tracking of the helicopter’s elevation and travel motion, while keeping the stability of the closed-loop system. Real-time experiment results demonstrate that the controller has achieved good tracking performance.     

基于积分型切换函数的自适应神经网络控制

针对一类具有未知函数控制增益的非线性系统,利用RBF神经网络的逼近能力,依据滑模控制原理,提出了一种直接自适应神经网络控制器设计新方案。通过引入积分型切换函数及逼近误差自适应补偿项,监督控制用饱和函数代替符号函数,根据李雅普诺夫稳定性理论,证明了闭环系统是全局稳定的,跟踪误差收敛到零。该算法应用于连续搅拌型化学反应器CSTR（Continuous Stirred Tank Reactor）,仿真结果显示,该算法能很好地使CSTR跟踪给定的温度信号,表明了该控制策略的有效性。     

非线性互联系统的分散混合自适应智能控制

针对一类高阶互联MIMO非线性系统，利用TS模糊系统和神经网络的通用逼近能力，在神经网络控制器中引入模糊基函数，提出一种分散混合自适应智能控制器设计的新方案．基于等价控制思想，设计分散自适应控制器，无需计算TS模型．通过对不确定项进行自适应估计，取消了其存在已知上界的假设．通过理论分析，证明了闭环智能控制系统所有信号有界，跟踪误差收敛到零．     

Nonlinear control techniques for an atomic force microscope system

Two nonlinear control techniques are proposed for an atomic force microscope system. Initially, a learning-based control algorithm is developed for the microcantilever-sample system that achieves asymptotic cantilever tip tracking for periodic trajectories. Specifically, the control approach utilizes a learning-based feedforward term to compensate for periodic dynamics and high-gain terms to account for non-periodic dynamics. An adaptive control algorithm is then developed to achieve asymptotic cantilever tip tracking for bounded tip trajectories despite uncertainty throughout the system parameters. Simulation results are provided to illttstrate the efficacy and performance of the control strategies.     

Nonlinear control techniques for an atomic force

Two nonlinear control techniques are proposed for an atomic force microscope system. Initially ,a learning- based control algorithm is developed for the microcantilever-sample system that achieves asymptotic cantilever tip tracking for periodic trajectories. Specifically ,the control approach utilizes a learning- based feedforward term to compensate for periodic dynamics and high- gain terms to account for non-periodic dynamics. An adaptive control algorithm is then developed to achieve asymptotic cantilever tip tracking for bounded tip trajectories despite uncertainty throughout the system parameters. Simulation results are provided to illustrate the efficacy and performance of the control strategies.     

Universal stabilisation design for uncertain nonlinear system with sign-switching unknown control direction

A universal robust control approach is proposed to stabilise a class of nonlinear systems. The system contains both nonlinear dynamics uncertainty and an unknown control direction, which is the multiplier of the control term. Being different from most previous studies, the unknown control direction is allowed to switch its sign in this article. A new Nussbaum gain technique is designed and integrated with robust controller to tackle the sign-switching unknown control direction. It is proven that the proposed control approach can yield asymptotic stabilisation and guarantee the boundedness of the closed-loop signals. In addition, these results can be readily extended to tracking problems under a different assumption.     

Model-free robot joint position regulation and tracking with prescribed performance guarantees

The problem of robot joint position control with prescribed performance guarantees is considered; the control objective is the error evolution within prescribed performance bounds in both problems of regulation and tracking. The proposed controllers do not utilize either the robot dynamic model or any approximation structures and are composed by simple PID or PD controllers enhanced by a proportional term of a transformed error through a transformation related gain. Under a sufficient condition for the damping gain, the proposed controllers are able to guarantee (i) predefined minimum speed of convergence, maximum steady state error and overshoot concerning the position error and (ii) uniformly ultimate boundedness (UUB) of the velocity error. The use of the integral term reduces residual errors allowing the proof of asymptotic convergence of both velocity and position errors to zero for the regulation problem under constant disturbances. Performance is a priori guaranteed irrespective of the selection of the control gain values. Simulation results of a three dof spatial robotic manipulator and experimental results of one dof manipulator are given to confirm the theoretical findings.     

含有广义饱和函数的全局渐近稳定非线性PID控制

为解决传统饱和函数条件比较苛刻的问题,本文提出了一种饱和函数,并将其应用于线性PD(比例-微分)+非线性PI (比例-积分)控制律.应用李亚普诺夫稳定性定理和拉萨尔不变原理推导了非线性PID控制律全局渐近稳定条件.为提高非线性PID控制精度,以位置跟踪误差绝对值时间积分和关节力矩输出误差绝对值时间积分为目标函数,以全局渐近稳定性条件以及关节额定驱动力矩为约束条件,应用多目标遗传算法NSGA-II(non-dominated sorted genetic algorithm-II)进行非线性PID控制律参数整定.选择优化后位置跟踪误差绝对值时间积分最小的饱和函数,并研究含有该饱和函数的非线性PID控制律对模型不确定性、输入干扰力矩和噪声的鲁棒性.与传统PID控制律和含有传统饱和函数的非线性PID控制律相比,本文方法的位置跟踪精度分别提高了近2个数量级和1个数量级.提出的饱和函数在靠近平衡点处具有较强反作用,使误差快速收敛于平衡点,有助于提高非线性PID控制律的位置跟踪精度以及鲁棒性.     

基本积分型李亚普诺夫函数的直接自适应神经网络控制

针对一类具有下三角形函数控制增益矩阵的非线性系统,基于滑模控制原理,并利用多层神经网络的逼近能力,提出了一种直接自适应神经网络控制器设计的新方案.通过引入积分型李亚普诺夫函数及残差与逼近误差和的上界函数的自适应补偿项,证明了闭环系统是全局稳定的,跟踪误差收敛到零.     

Adaptive control of uncertain pure-feedback nonlinear systems

An adaptive control approach is proposed to solve the globally asymptotic state stabilisation problem for uncertain pure-feedback nonlinear systems which can be transformed into the pseudo-affine form. The pseudo-affine pure-feedback nonlinear system under consideration is with nonlinearly parameterised uncertainties and possibly unknown control coefficients. Based on the parameter separation technique, a novel backstepping controller is designed by adopting the adaptive high gain idea. The proposed control approach could avoid the drawbacks of the approximation-based approaches since no estimators are needed to estimate the virtual and the actual controllers. In addition, it could guarantee globally asymptotic state stabilisation even though there exist nonlinearly parameterised uncertainties in the considered system while comparing to the existing approximation-free approaches. A numerical and a realistic examples are employed to demonstrate the effectiveness of the proposed control method.