基于自适应反演的导弹滑模控制

阐述了在导弹系统存在不确定性情况下,基于自适应反演控制技术和模糊神经网络理论,提出了一种导弹滑模控制系统设计方法。设计过程中将不确定性对系统的影响合成为一项,然后应用模糊神经逼近器来逼近系统的不确定项;考虑了已知信息,利用自适应模糊神经控制理论和滑模控制设计了控制器,应用Lypunov稳定性理论保证了闭环系统的稳定性,并推导出模糊神经逼近器各参数的自适应调节律。     

基于RBF网络辨识的航空发动机模糊神经网络解耦控制

对航空发动机的多变量解耦控制方法进行了研究,提出了一种基于RBF网络辨识的航空发动机模糊神经网络解耦控制方法。该方法利用RBF网络辨识航空发动机的实时模型,为模糊神经网络控制器参数的调整提供了Jacobian信息,解决了模糊神经网络自适应控制器在被控对象不能精确建模情况下应用的问题。仿真结果表明,系统鲁棒性强．在设计点和偏离设计点处,均具有良好的动态特性和解耦特性。     

无刷直流电机网络伺服控制技术研究

针对直流电机网络控制的问题,考虑反馈通道和前向通道均存在延时的状况,设计了直流电机网络伺服控制器。该控制器采用基于模糊自适应调节的广义预测控制算法。模糊自适应调节方法对广义预测控制算法的输出进行调节,从而克服系统参数的不确定性对控制性能的影响。仿真实验结果表明该控制方案能够减小网络延时对系统控制特性的影响,改善系统的动态特性。     

基于模糊神经网络的涡扇发动机加速控制

采用模糊神经网络对某型涡扇发动机加速过程进行自适应控制，为了进一步提高系统性能，采用遗传算法和BP算法相结合的方式，对系统分别进行了离线和在线优化。仿真结果表明，设计的模糊神经网络控制器具有良好的性能。     

导弹自适应模糊控制方法研究

用两种方法对导弹纵向通道过载控制系统进行了设计方法一将导弹过载模型转化为严格反馈形式,基于反演设计的思想分别对对象的各个子系统进行了自适应模糊控制器的没计,利用李雅普诺夫稳定性理论进行了控制器稳定性分析并得到了模糊参数的自适应调节律;方法二利用滑模控制的思想,通过建立的控制量动态滑而与状态量的动态滑而之间的关系,对典型的二阶过载系统进行了自适应模糊滑模控制器的设计,得到了具有滑模特点的参数自适应调节律.最后通过仿真对控制器的设计进行了验证.     

有输入未建模动态和不确定性导弹控制器设计

在考虑导弹控制系统同时存在输入未建模动态和参数不确定性的情况下,提出了一种基于RBF神经网络和反演技术的鲁棒自适应控制器的设计方法。首先用两个RBF神经网络对未建模动态进行了补偿,然后利用反演技术设计了鲁棒自适应控制器,对不确定性进行了处理,并用Lyapunov稳定性理论推导出RBF神经网络的权重矩阵调节率以及相关的自适应律,证明了系统的全局稳定性,最后通过仿真计算验证了设计的正确性。     

高超声速飞行器强鲁棒自适应控制器设计新方法

针对高超声速飞行器飞行过程系统参数大范围剧烈变化以及存在严重不确定性的特点,同时考虑外界环境干扰复杂,内部干扰严重的特殊问题,提出了一种新型强鲁棒自适应控制器构型。该新型强鲁棒自适应控制器将控制器分为标称控制器和补偿控制器。标称控制器可采用成熟的控制理论来设计,主要考虑闭环系统的性能;采用合适的手段估计系统参数大范围剧烈变化、系统的不确定性以及内、外部干扰等“系统扰动”作为补偿控制器的输入,通过设计强鲁棒补偿控制器对“系统扰动”进行补偿,使整个闭环控制系统对“系统扰动”具有强鲁棒性。将新型强鲁棒自适应控制器应用于高超声速飞行器的姿态控制系统的设计,大大提高了高超声速飞行器控制系统对内、外部干扰的抑制和对系统参数大范围剧烈变化以及严重不确定性的适应能力,可满足高超声速飞行器飞行控制的需求。     

航空发动机T-S分布式系统自适应鲁棒滑模控制

为提高航空发动机非线性不确定分布式控制系统的鲁棒性,考虑参数摄动,外部干扰,随机时延的影响,采用一种基于飞行包线划分的航空发动机T-S模糊模型,进行了鲁棒自适应滑模控制方法的研究。基于鲁棒H∞理论,针对模糊规则的状态空间模型,推导了滑模运动渐进稳定的充分条件,设计具有扰动抑制性能的鲁棒滑模面;基于并行分布补偿技术,采用与T-S模型相同的模糊规则,确定全局模糊滑模控制器设计策略,在此基础上,采用自适应技术估计未知干扰上界,设计了自适应滑模控制器,并证明了系统在控制器作用下的到达性。仿真结果表明该方法能够保证系统渐进稳定,对所考虑的不确定性因素鲁棒性较好,有效削弱了抖振,对不同工作点和干扰条件具有良好的适应性。     

简化的鲁棒自适应模糊动态面控制及其应用

 针对不确定严格反馈块控非线性系统,提出了一种简化的鲁棒自适应模糊动态面控制方法。利用T S模糊系统在线逼近系统的不确定及外界干扰,简化自适应参数调整方法,解决了动态面控制自适应参数过多的问题。基于Lyapunov方法及小增益理论证明了闭环系统半全局一致最终有界。该方法设计的控制器复杂性低且具有最少的在线调整自适应参数,易于工程实现。最后对所提出的方法进行了某推力矢量战斗机的Herbst机动仿真,结果表明了方法的有效性。     

基于滑模神经网络的自主飞艇姿态控制

针对自主飞艇飞行环境的不确定性,提出了一种基于自适应滑模神经网络的姿态控制系统.平流层高空飞行环境对飞艇控制产生了许多不确定性因素,利用自适应变结构控制和神经网络方法设计了飞艇的俯仰通道控制器.非线性仿真结果表明:控制器能够适应对象结构参数及外部扰动的大范围变化,满足姿态控制稳定性要求,同时也消除了变结构控制系统的抖振,具有良好的鲁棒性和动态性能.     

Modified unscented Kalman filtering and its application in autonomous satellite navigation

In this paper, a modified unscented Kalman filter (UKF) for nonlinear stochastic systems is proposed, and it is applied to autonomous orbit determination for Earth satellites. Based on some standard results about the boundedness of stochastic processes and a new formulation of the unscented transformation (UT), it is demonstrated that the design of the noise covariance matrix plays an important role in enhancing the filter stability. Furthermore, a particular design of the noise covariance matrix is proposed as a modification of the UKF. The modified UKF is less sensitive to the initial error than the usual one. High performance of the modified UKF is illustrated in comparison with the usual one by using the real data obtained from an Earth sensor.     

Observer-based adaptive sliding mode backstepping output-feedback DSC for spin-stabilized canard-controlled projectiles

This article presents a complete nonlinear controller design for a class of spin-stabilized canard-controlled projectiles.Uniformly ultimate boundedness and tracking are achieved,exploiting a heavily coupled,bounded uncertain and highly nonlinear model of longitudinal and lateral dynamics.In order to estimate unmeasurable states,an observer is proposed for an augmented multiple-input-multiple-output(MIMO) nonlinear system with an adaptive sliding mode term against the disturbances.Under the frame of a backstepping design,an adaptive sliding mode output-feedback dynamic surface control(DSC) approach is derived recursively by virtue of the estimated states.The DSC technique is adopted to overcome the problem of ‘‘explosion of complexity" and relieve the stress of the guidance loop.It is proven that all signals of the MIMO closed-loop system,including the observer and controller,are uniformly ultimately bounded,and the tracking errors converge to an arbitrarily small neighborhood of the origin.Simulation results for the observer and controller are provided to illustrate the feasibility and effectiveness of the proposed approach.     

Adaptive Integral-type Sliding Mode Control for Spacecraft Attitude Maneuvering Under Actuator Stuck Failures

A fault tolerant control (FTC) design technique against actuator stuck faults is investigated using integral-type sliding mode control (ISMC) with application to spacecraft attitude maneuvering control system. The principle of the proposed FTC scheme is to design an integral-type sliding mode attitude controller using on-line parameter adaptive updating law to compensate for the effects of stuck actuators. This adaptive law also provides both the estimates of the system parameters and external disturbances such that a prior knowledge of the spacecraft inertia or boundedness of disturbances is not required. Moreover, by including the integral feedback term, the designed controller can not only tolerate actuator stuck faults, but also compensate the disturbances with constant components. For the synthesis of controller, the fault time, patterns and values are unknown in advance, as motivated from a practical spacecraft control application. Complete stability and performance analysis are presented and illustrative simulation results of application to a spacecraft show that high precise attitude control with zero steady-error is successfully achieved using various scenarios of stuck failures in actuators.     

Robust decentralized control design for aircraft engines: A fractional type

A new decentralized control for aircraft engines is proposed. In the proposed control approach, aircraft engines are considered as uncertain large-scale systems composed of interconnected uncertain subsystems. For each subsystem, the time-varying uncertainty, including parameter disturbances and interconnections in/between subsystems, is depicted by a class of general nonlinear functions. A fractional robust decentralized control with two parts, the nominal one and the fractional one, is presented. The nominal control guarantees the asymptotical stability of the engine system without uncertainty. The fractional part aims at overcoming the influences of uncertainty. Compared to the previous studies, the presented control provides not only an extra flexibility for the system performance tuning by the fraction-type gain but also a facility for the control input calculation. The proposed control approach is applied to a turbofan engine with two subsystems. The computer simulation shows that, in the flight envelope, the fractional control not only guarantees the closed-loop system uniform boundedness and ultimate uniform boundedness but also shows good economy.     

Stochastic stability theory for systems containing intervalmatrices

Known conditions for the stability of stochastic, linear time-varying (LTV) dynamical systems based on Liapunov theory are applied to LTV dynamical systems containing interval matrices; both discrete and continuous time processes are considered. These conditions are sufficient for stability with probability 1 (wp1) and, in the case of discrete time, also necessary for stability in m.s. They lead to a simple, noniterative technique that involves the computation of eigenvalues of matrices whose elements often consist of first- and/or second-order moments. The results are useful in areas such as robust design, feedback control, perturbation analysis, and fault tolerant systems     

A novel imprecise stochastic process model for time-variant or dynamic uncertainty quantification

This paper proposes a novel model named as “imprecise stochastic process model” to handle the dynamic uncertainty with insufficient sample information in real-world problems. In the imprecise stochastic process model, the imprecise probabilistic model rather than a precise probability distribution function is employed to characterize the uncertainty at each time point for a time-variant parameter, which provides an effective tool for problems with limited experimental samples. The linear correlation between variables at different time points for imprecise stochastic processes is described by defining the auto-correlation coefficient function and the cross-correlation coefficient function. For the convenience of analysis, this paper gives the definition of the P-box-based imprecise stochastic process and categorizes it into two classes: parameterized and non-parameterized P-box-based imprecise stochastic processes. Besides, a time-variant reliability analysis approach is developed based on the P-box-based imprecise stochastic process model, through which the interval of dynamic reliability for a structure under uncertain dynamic excitations or time-variant factors can be obtained. Finally, the effectiveness of the proposed method is verified by investigating three numerical examples.     

Disturbance estimation and compensation in linear systems

Discrete-time estimation and compensation are discussed as a solution to the problems encountered when disturbances are present and degrade the performance of continuous automatic control systems. The method described, under a mild set of conditions, allows the designer to locate the closed-loop system poles substantially anywhere and provide disturbance rejection as large as desired by increasing the dimensions of the disturbance estimator. This estimation/compensation scheme results in a gain characteristic, below some frequency (ω_c ), with a slope of q×20 dB/decade. The value of q and ω_c can be chosen by the designer, within the physical limitations of the problem, so that the system error resulting from either deterministic or stochastic disturbances is sufficiently reduced. The method has been investigated for use with known linear, time-invariant systems     

用随机模型改进作战效能分析的纯概率模型

无论对战斗机还是对武器系统,沿任意一条航迹试图突破防区目标的生存概率或伤亡概率都是重要的效能指标。利用随机服务系统理论改进了这些效能指标的计算模型,改进后效能指标计算的准确程度显著地提高了。     

Sliding mode tracking control for miniature unmanned helicopters

A sliding mode control design for a miniature unmanned helicopter is presented. The control objective is to let the helicopter track some predefined velocity and yaw trajectories. A new sliding mode control design method is developed based on a linearized dynamic model. In order to facilitate the control design, the helicopter’s dynamic model is divided into two subsystems,such as the longitudinal-lateral and the heading-heave subsystem. The proposed controller employs sliding mode control technique to compensate for the immeasurable flapping angles’ dynamic effects and external disturbances. The global asymptotic stability(GAS) of the closed-loop system is proved by the Lyapunov based stability analysis. Numerical simulations demonstrate that the proposed controller can achieve superior tracking performance compared with the proportionalintegral-derivative(PID) and linear-quadratic regulator(LQR) cascaded controller in the presence of wind gust disturbances.