Approximation-based adaptive fault compensation backstepping control of fractional-order nonlinear systems: An output-feedback scheme

An observer-based adaptive fuzzy backstepping approach is proposed for nonlinear systems with respect to fractional-order differential equations, unmatched uncertainties, unmeasured states, and actuator faults. The approximation capability of fuzzy logic system and minimal learning parameter approaches are applied to identify uncertain functions in a simultaneous manner. For estimating the unavailable conditions, a fuzzy fractional-order state-observer is extended. Applying fault-tolerant approach in a backstepping design methodology would provide a new fault-tolerant adaptive fuzzy output-feedback approach for fractional-order strict-feedback systems. This control structure would assure the considered system stability through selection of the appropriate Lyapunov candidate function. Two numerical simulations are run to exhibit the validity herein.     

Integral barrier Lyapunov function-based adaptive fuzzy output feedback control for nonlinear delayed systems with time-varying full-state constraints

In this article, an adaptive fuzzy output feedback control method is presented for nonlinear time-delay systems with time-varying full state constraints and input saturation. To overcome the problem of time-varying constraints, the integral barrier Lyapunov functions (IBLFs) integrating with dynamic surface control (DSC) are applied for the first time to keep the state from violating constraints. The effects of unknown time delays can be removed by using designed Lyapunov-Krasovskii functions (LKFs). An auxiliary design system is introduced to solve the problem of input saturation. The unknown nonlinear functions are approximated by the fuzzy logic systems (FLS), and the unmeasured states are estimated by a designed fuzzy observer. The novel controller can guarantee that all signals remain semiglobally uniformly ultimately bounded and satisfactory tracking performance is achieved. Finally, two simulation examples illustrate the effectiveness of the presented control methods.     

基于二维广义Logistic映射的图像加密算法

提出一个基于广义Logistic方程构造的二维混沌映射,采用Feigenbaum常数、相图、分岔图分析的方法,研究该混沌映射的非线性动态特性;然后利用该方程生成混沌序列,将混沌序列进行优化改进,生成密钥序列;最后采用输出反馈的加密方式,改变图像的像素值,达到加密的目的。实验仿真表明,该加密算法对密钥非常敏感,有效地提高了抵御选择明文攻击的能力,密文图像信息熵为7.996 94,非常接近理想值8。加密图像像素值具有类随机均匀分布特性,加密图像和明文图像之间的相关性非常接近于0,相邻像素具有零相关特性,而且密钥空间达到299比特。加密方法大大改变了明文图像的像素值,使得密文能够抵御统计攻击。     

二维线性系统特征结构配置的两种新算法

本文提出了二维线性系统特征结构配置的两种新算法,并给出了状态反馈矩阵存在的充分条件。     

系统的增速依赖于不可测状态非线性系统全局输出反馈渐近镇定

研究了一类非线性增长速度依赖于不可测状态且有稳定零动态非线性系统的全局输出反馈渐近稳定控制问题. 因所研究的系统隐含有零动态, 所以首先定义了一系列新的线性变换, 从而成功地分离出原系统的零动态, 得到了便于输出反馈设计的新系统. 然后给出了变换后系统的较为简洁的输出反馈控制设计过程, 并且, 闭环系统的渐近稳定性可由所导出矩阵的正定性来保证. 最后, 仿真算例验证了文中理论结果的正确性.     

具有多项式型非线性项的大规模随机非线性系统的分散输出反馈镇定

基于齐次占优方法研究 具有多项式型非线性项的大规模随机非线性系统的分散输出反馈镇定问题. 本文的主要贡献在于利用高增益齐次占优方法来解决大规模随机非线性系统的分散控制问题. 这种方法能彻底地去掉传统结果中所要求的线性增长条件, 不仅推广了以前的结果还得到了一些新的结果. 仿真验证了输出反馈控制器的有效性.     

随机非线性系统的输出反馈控制

针对满足线性增长条件的一类随机非线性系统, 本文研究了输出反馈镇定问题. 然而不同于现有的所有文献, 由于线性增长条件中含有不可量测的状态, 引入了一个待定的高增益观测器. 利用反推设计技术, 构造性地给出了一个输出反馈控制器的设计, 通过适当地选取高增益参数, 保证了闭环系统的零解是概率意义下全局渐近稳定的, 输出几乎处处调节于零.     

A compact design scheme of adaptive output-feedback control for uncertain nonlinear systems

Multiple uncertainties/noises, frequently exist in a plant, usually require multiple compensation techniques, which renders feedback controllers highly dynamic and nonlinear. This motivates us to search for a compact design scheme of compensation to reduce the complexity of controllers. In this paper, global output-feedback stabilisation is investigated for a class of uncertain nonlinear systems with unknown unmeasured states-dependent growth and input matching uncertainty. To solve the problem, a compact scheme is proposed to design a global adaptive output-feedback controller, which combines the technique of dynamic gain and extended state observer together. Particularly, only one dynamic gain, rather than two dynamic gains, is introduced to deal with the unknown polynomial-of-output growth rate, which makes our controller to have lower dynamics than those in the related works. Moreover, the input matching uncertainty is asymptotically estimated by the extended state observer, and thus its effect is well counteracted. It is shown that, under the designed controller, the system states globally converge to zero. A simulation example on non-zero set-point regulation demonstrates the effectiveness of the proposed approach.     

LMI-based H ∞-optimal control with transients

In this article, the worst-case norm of the regulated output over all exogenous signals and initial states as a performance measure of the system is characterised in terms of linear matrix inequalities (LMIs). Optimal time-invariant state- and output-feedback controllers are synthesised as minimising this performance measure. The essential role in this synthesis plays a weighting matrix reflecting the relative importance of the uncertainty in the initial state contrary to the uncertainty in the exogenous signal. H _∞-optimal control with transients is shown to be actually a trade-off between H _∞-control, being optimal under unknown exogenous disturbances and zero initial state, and γ-control, being optimal under zero exogenous signal and unknown initial conditions, if and only if the weighting matrix satisfies a fundamental inequality. If this inequality is met, the performance measure is achieved and the explicit formulae for the worst-case disturbance and initial state are provided. If this inequality fails, the performance measure coincides with the H _∞-norm and the trade-off gets broken.