基于预设性能的非仿射非线性系统自适应有限时间控制

针对一类具有死区的非仿射非线性系统,将预设性能控制与有限时间控制相结合,提出一种具有预设性能的自适应有限时间跟踪控制方法.基于Backstepping技术、模糊逻辑系统及有限时间Lyapunov稳定理论,给出使系统半全局实际有限时间稳定(semi-globally practically finite-time stable,SGPFS)的充分条件和设计步骤.该控制策略不仅使系统的输出误差在有限时间内收敛到一个预先设定区域,同时保证其收敛速度、最大超调量和稳态误差均满足预先设定的性能要求.最后通过仿真示例验证了所提出设计方法的有效性.     

Observer backstepping control of DFIG-Generators for wind turbines variable-speed: FPGA-based implementation

In this paper, we present a new contribution for the control of Wind-turbine energy systems, a nonlinear robust control of active and reactive power by the use of the Adaptative Backstepping approach based in double-fed asynchronous generator (DFIG-Generator).Initially, a control strategy of the MPPT for extraction of maximum power of the turbine generator is presented. Thereafter, a new control technique for wind systems is presented. This control system is based on an adaptive pole placement control approach integrated to a Backstepping control system. The stability of the system is shown using Lyapunov technique. Using the FPGA to implement the order gives us a better rapidity. A Benchmark was realized by a prototyping platform based on DFIG-generator, FPGA and wind-turbine; the experimental results obtained show the effectiveness and the benefit of our contribution.     

Multivariable adaptive control: A survey

Adaptive control is a control methodology capable of dealing with uncertain systems to ensure desired control performance. This paper provides an overview of some fundamental theoretical aspects and technical issues of multivariable adaptive control, and a thorough presentation of various adaptive control schemes for multi-input–multi-output systems, literature reviews on adaptive control foundations and multivariable adaptive control methods, and related technical problems. It covers some basic concepts and issues such as certainty equivalence, stability, tracking, robustness, and parameter convergence. It discusses some of the most important topics of adaptive control: plant uncertainty parametrization, stable controller adaptation, and design conditions for different adaptive control schemes. The paper also presents a detailed study of well-developed multivariable model reference adaptive control theory and design techniques. It provides an introduction to multivariable adaptive pole placement and adaptive nonlinear control, and it concludes by identifying some open research problems.     

Adaptive L2 control of nonlinear systems using neural networks

An adaptive neural network controller is developed to achieve output-tracking of a class of nonlinear systems. The global L2 stability of the closed-loop system is established. The proposed control design overcomes the limitation of the conventional adaptive neural control design where the modeling error brought by neural networks is assumed to be bounded over a compact set.Moreover,the generalized matching conditions are also relaxed in the proposed L2 control design as the gains for the external disturbances entering the system are allowed to have unknown upper bounds.     

Control of discrete time nonlinear systems with a time-varying structure

In this paper, we present a control methodology for a class of discrete time nonlinear systems that depend on a possibly exogenous scheduling variable. This class of systems consists of an interpolation of nonlinear dynamic equations in strict feedback form, and it may represent systems with a time-varying nonlinear structure. Moreover, this class of systems is able to represent some cases of gain scheduling control, Takagi-Sugeno fuzzy systems, as well as input-output realizations of nonlinear systems which are approximated via localized linearizations. We present two control theorems, one using what we call a “global” approach (akin to traditional backstepping), and a “local” approach, our main result, where backstepping is again used but the control law is an interpolation of local control terms. An aircraft wing rock regulation problem with varying angle of attack is used to illustrate and compare the two approaches.     

自适应反推神经网络的转台鲁棒控制器设计

针对未知非线性、外界干扰和参数摄动等不确定因素对实际转台控制系统的影响,提出了自适应反推神经网络的转台鲁棒控制器设计.首先给出自适应Backstepping控制器的设计方法及步骤,接着采用RBF神经网络对转台对象参数的不确定因素上界值加以辨识,实现转台系统的鲁棒控制.其中Backstepping鲁棒控制作为主控制器,RBF神经网络实现了不确定上界值的在线辨识.仿真结果表明,自适应Backstepping神经网络控制很好地克服了对象的不确定性,实现控制系统的较强鲁棒性,适于高精度飞行仿真转台系统的实时鲁棒控制.     

基于自适应神经网络Backstepping 空中加油编队飞行控制

为解决无人机自主空中加油过程加油机和受油机编队飞行控制的问题,提出了对受油机采用导引回路和 姿态回路分离控制策略,采用L1 导引算法设计一种基于自适应神经网络Backstepping 飞行控制器。将改进的L1 导 引算法应用于受油机的横向和纵向的导引,使用自适应神经网络补偿受油机受到的外界的干扰和系统模型误差,神 经网络权值矩阵通过自适应律在线更新,并结合Backstepping 控制方法设计受油机的控制律。仿真结果表明：在受 油机与加油机编队飞行过程中,该设计方法能有效提高受油机的跟踪精度和抗干扰能力,解决空中加油编队飞行控 制问题。     

新能源汽车电静液制动系统抗饱和反演控制

对电静液制动系统存在的未知非线性及参数时变特点,提出一种控制输入受限条件下的反演控制策略。建立电静液作动器的状态空间模型并表达为严格反馈形式,将建模误差及未建模动态视为未知干扰,结合反演步骤设计参数自适应律,实时估计未知扰动。设计抗饱和补偿器,将控制器输出与被控对象输入信号之差作为抗饱和补偿器的输入,构成反馈回路消除饱和现象。应用Lyapunov方法证明闭环系统的渐近稳定性,闭环系统信号一致有界,压力跟踪误差可通过改变补偿器的参数进行调节。仿真及试验结果表明,所提控制方法具有较强的自适应能力,与传统反演方法相比,鲁棒性和控制性能得到显著提高。     

基于反步法的一类非线性切换系统控制器设计

研究一类子系统是纯反馈非线性系统的切换系统控制器设计问题．基于多Lyapunov函数和反步法,提出一种状态反馈控制律及切换规则设计方法。通过递推设计可得到使非线性切换系统稳定的反馈控制律和切换规则．将所提出的方法应用于该类非线性切换系统含有不确定性的情形,仿真算例结果表明了所提出方法的有效性．     

Stochastic nonlinear stabilization — II: Inverse optimality

After considering the stabilization of a specific class of stochastic nonlinear systems in a companion paper, in this second part, we address the classical question of when is a stabilizing (in probability) controller optimal and show that for every system with a stochastic control Lyapunov function it is possible to construct a controller which is optimal with respect to a meaningful cost functional. Then we return to the problem from Part I and design an optimal backstepping controller whose cost functional includes penalty on control effort and which has an infinite gain margin.