具有不确定噪声的随机非线性系统的鲁棒自适应跟踪

研究了一类随机非线性系统的鲁棒自适应跟踪问题.文中利用随机控制Lyapunov设计方法,对于受方差不确定Wiener噪声干扰的参数严格反馈形式的系统,给出了参数自适应律和控制律,使得跟踪误差在4次均方意义下收敛到一个小范围内.     

一类Markov跳跃非线性系统的鲁棒自适应镇定

研究一类具有Markov 跳跃参数的随机非线性系统的鲁棒自适应镇定问题.利用随机控制的Lyapunov 设计方法,对受Wiener 噪声干扰的参数严格反馈形式的跳跃系统,利用backstepping 方法设计参数自适应律和控制律,使得闭环系统状态在4 阶矩意义下全局一致有界,并能收敛到平衡点的任意小邻域内.仿真结果表明了该设计方法的有效性.     

基于虚拟模型的自适应解耦抗扰控制

针对一类多变量非线性耦合系统,提出了一种基于虚拟模型的非线性自适应控制器.首先将非线性系统线性化处理并将其作为虚拟模型,对该模型设计线性自适应控制律.然后将线性控制律分别应用在虚拟系统和受控的实际非线性系统上,根据两者的输出误差设计补偿控制律,以达到对实际被控对象进行自适应解耦抗扰的目的.利用李雅普诺夫稳定理论给出了控制系统稳定性条件.实验仿真验证了控制算法的有效性.     

浸入与不变方法原理及其在非线性自适应控制中的应用

非线性系统理论的实际工程需求和复杂性使其成为控制学科中最具吸引力和挑战性的研究领域,为此介绍了一种新的非线性控制律设计方法——浸入与不变（I&I）理论.该方法首先选择一个比被控系统维数低的（局部）渐近稳定的目标系统,然后设计浸入映射和控制律,使得原系统在控制律作用下的动态轨迹都是目标系统在浸入映射下的像,并且该控制律能够保持目标系统的像为不变吸引流形,且使闭环轨迹有界.针对未知点质量模型,设计了一种新的非线性浸入与不变自适应控制律,实现了对参考指令的精确跟踪.将其与基于确定等价原则的自适应控制律相比较,仿真结果表明,所设计的浸入与不变控制律能够更好地处理带有未知参数的系统.     

严格反馈随机非线性系统的鲁棒自适应逆最优跟踪

针对具有未知定常参数和标准Wiener噪声扰动的严格反馈非线性系统,结合参考信号,构造了误差系统,使用Backstepping算法设计了误差系统的自适应逆最优控制律和参数自适应律,进而解决了原系统的鲁棒自适应逆最优跟踪.     

具有不确定Wiener噪声随机非线性系统的自适应逆最优控制

针对一类具有不确定Wiener噪声扰动和未知定常参数的随机非线性系统,采用随 机微分方程描述系统,基于Backstepping算法,利用随机控制Lyapunov函数,研究了自适 应逆最优控制问题的可解定理,系统地给出了全局依概率渐近稳定和自适应逆最优控制策略 的设计方法.这种方法可同时获得控制律和自适应律,仿真结果表明该控制算法的有效性.     

改进的非线性鲁棒自适应动态面控制

针对不确定多输入多输出严格反馈块控非线性系统,提出一种鲁棒自适应动态面控制方法.该方法在反推自适应神经网络控制中引入动态面控制简化控制律,同时对自适应律进行改进以改善系统的过渡过程动态品质,保证了系统在简化的控制律下仍具有良好的动态特性.通过Lyapunov方法证明了闭环系统所有信号均有界,系统的跟踪误差指数收敛到有界紧集内.最后给出的某新型战斗机六自由度仿真结果表明了该方法的有效性.     

非线性扩展结构大系统自适应神经网络跟踪控制

针对一类非线性关联大系统在结构扩展时的跟踪控制问题, 提出一种采用自适应神经网络的控制方法. 该方法要求在不改变原结构系统控制律的前提下设计新加入子系统的控制律和自适应律, 使扩展后所有子系统都具有很好的跟踪性能. 这里主要利用神经网络的逼近功能以及Backstepping 技术来设计自适应律和控制律, 通过Lyapunov 理论证明在该控制器的作用下闭环系统的所有信号均是有界的, 并可使系统准确跟踪. 仿真结果验证了所提出方法的有效性.

     

飞行器自适应神经网络时滞控制

在飞行器稳定性控制问题的研究中,针对含有外部扰动、参数不确定性、状态和控制时滞的非线性飞行器系统,提出了一种时滞状态反馈控制与神经网络自适应估计相结合的方法.对非线性系统线性化处理得到飞行器线性模型,并由线性矩阵不等式(LMI)设计反馈控制律;采用径向基函数(RBF)神经网络自适应在线估计策略,对反馈控制律进行补偿以消除未知非线性影响;采用Lyapunov稳定性理论证明了在所设计控制律作用下,闭环系统渐近稳定同时满足H∞性能指标.仿真结果验证了上述方法的可行性及有效性.     

含有非线性不确定参数的电液系统滑模自适应控制

针对含有非线性不确定参数的电液控制系统, 提出了一种滑模自适应控制方法. 该控制方法主要是为了解决由于初始控制容积的不确定性而引起的, 非线性不确定参数自适应律设计的难题. 其主要特点为, 通过定义一个新型的特Lyapunov 函数, 进而构建系统的自适应控制器及参数自适应律, 并结合滑模控制方法及一种简单的鲁棒设计方法, 给出整个电液系统的滑模自适应控制器, 及所有不确定参数的自适应律. 试验结果表明, 采用该控制方法能够取得良好的性能, 尤其可以补偿非线性不确定参数对系统的影响.     

Prediction-based discrete-time adaptive nonlinear stochasticcontrol

Adaptive control of a class of discrete-time parametric-strict-feedback nonlinear systems with additive white noises is considered in this paper. The control law is designed based on weighted least squares (WLS) algorithms and on recursive adaptive predictors. Global stability and tracking error bounds are established for the closed-loop systems     

Multidimensional Taylor network adaptive control for MIMO time‐varying uncertain nonlinear systems with noises

In this paper, an adaptive control approach based on the multidimensional Taylor network (MTN) is proposed here for the real‐time tracking control of multiple‐input–multiple‐output (MIMO) time‐varying uncertain nonlinear systems with noises. Two MTNs are used to formulate the optimum control and adaptive filtering approaches. The feed‐forward MTN controller (MTNC) is developed to realize the precise tracking control. The closed‐loop errors between the filtered outputs and expected values are directly chosen as the MTNC's inputs. A valid initial value selection scheme for the weights of the MTNC, which can ensure the initial stability of adaptive process, is introduced. The proposed MTNC can update its weights online according to errors caused by system's uncertain factors, based on stable learning rate. The resilient backpropagation algorithm and the adaptive variable step size algorithm via linear reinforcement are utilized to update the MTNC's weights. The MTN filter (MTNF) is developed to eliminate measurement noises and other stochastic factors. The proposed adaptive MTN filtering system possesses the distinctive properties of the Lyapunov theory–based adaptive filtering system and MTN. Lyapunov function of the filtering errors between the measured values and MTNF's outputs is defined. By properly choosing the weights update law in the Lyapunov sense, the MTNF's outputs can asymptotically converge to the desired signals. The design is independent of the stochastic properties of the input disturbances. Simulation of the MTN‐based control is conducted to test the effectiveness of the presented results.     

随机多智能体系统一致性增益的设计与分析

在固定和切换拓扑中通信网络含有加性随机噪声的情况下,针对随机多智能体系统一致性跟踪控制问题,本文采用自适应控制方法给出了一种新的一致性增益设计方法.在基于邻居智能体状态设计的分布式自适应控制协议中,每个跟随者的一致性增益自适应律仅仅依赖于跟踪误差,并且与通信网络全局信息无关.结合代数图论,随机理论工具和自适应控制得到两个结论:1)每个跟随者以均方意义下跟踪上领导者; 2)每个跟随者的一致性增益趋于一个理想估计值.通过两个仿真实例验证算法的有效性.     

Recursive filtering adaptive fault‐tolerant tracking control for uncertain switched multivariable nonlinear systems

This article synthesizes a recursive filtering adaptive fault‐tolerant tracking control method for uncertain switched multivariable nonlinear systems. The multivariable nonlinear systems under consideration have both matched and mismatched uncertainties, which satisfy the semiglobal Lipschitz condition. A piecewise constant adaptive law generates adaptive parameters by solving the error dynamics with the neglection of unknowns, and the recursive least squares is employed to minimize the residual error by categorizing the total uncertainty estimates into matched and mismatched components. A filtering control law is designed to compensate the actuator faults and nonlinear uncertainties such that a good tracking performance is delivered with guaranteed robustness. The matched component is canceled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is performed to eliminate the effect of the mismatched component on the output. By exploiting the average dwell time principle, the error bounds are derived for the states and control inputs compared with the virtual reference system which defines the best performance that can be achieved by the closed‐loop system. Both numerical and practical examples are provided to illustrate the effectiveness of the proposed switching recursive filtering adaptive fault‐tolerant tracking control architecture, comparisons with model reference adaptive control are also carried out.     

Iterative Learning Control for Discrete-time Stochastic Systems with Quantized Information

An iterative learning control (ILC) algorithm using quantized error information is given in this paper for both linear and nonlinear discrete-time systems with stochastic noises. A logarithmic quantizer is used to guarantee an adaptive improvement in tracking performance. A decreasing learning gain is introduced into the algorithm to suppress the effects of stochastic noises and quantization errors. The input sequence is proved to converge strictly to the optimal input under the given index. Illustrative simulations are given to verify the theoretical analysis.      

Adaptive guidance law design based on characteristic model for reentry vehicles

In this paper an adaptive guidance law based on the characteristic model is designed to track a reference drag acceleration for reentry vehicles like the Shuttle. The characteristic modeling method of linear constant systems is extended for single-input and single-output （SlSO） linear time-varying systems so that the characteristic model can be established for reentry vehicles. A new nonlinear differential golden-section adaptive control law is presented. When the coefficients belong to a bounded closed convex set and their rate of change meets some constraints, the uniformly asymptotic stability of the nonlinear differential golden-section adaptive control system is proved. The tracking control law, the nonlinear differential golden-section control law, and the revised logical integral control law are integrated to design an adaptive guidance law based on the characteristic model. This guidance law overcomes the disadvantage of the feedback linearization method which needs the precise model. Simulation results show that the proposed method has better performance of tracking the reference drag acceleration than the feedback linearizaUon one.     

Adaptive output-feedback tracking of stochastic nonlinear systems

We address the adaptive stabilization and tracking problems for a class of output feedback canonical systems driven by Wiener noises of unknown covariance. Filtered transformation and backstepping techniques are employed in the stochastic control design. We obtain two adaptive controllers that guarantee the global stability in probability for vanishing perturbations or the input-to-state stability in probability for nonvanishing perturbations respectively. The tracking error can converge to a small residual set around the origin in the sense of mean quartic value.     

Advanced Programmed Motion Tracking Control of Nonholonomic Mechanical Systems

Trajectory tracking control of nonholonomic systems has been extended to tracking a desired motion. The desired motion is specified by equations of constraints, referred to as programmed, which may be differential equations of high order and may be nonholonomic. The strategy enables motion tracking control under the assumption that the system dynamics are accurately known. It is referred to as a model reference tracking control strategy for programmed motion. In this paper, adaptive and repetitive extensions of the strategy are proposed. Two selected advanced tracking control algorithms, i.e., the desired compensation adaptation law and the repetitive control law, which were originally dedicated to holonomic systems, are adapted to motion tracking control of nonholonomic systems. Simulation studies that illustrate programmed motion tracking control of systems with unknown parameters and the performance of repetitive motions are provided. A new performance measure to evaluate a programmed motion tracking performance is introduced.