Multimodel-based flight control system reconfiguration control in the presence of input constraints

In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model switching.The μ-modification is introduced in the model reference architecture to construct the adaptive controller.The proof of stability is based on the candidate Lyapunov function,while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals.Simulation results illustrate the efficiency of the proposed method.

New model reference adaptive control with input constraints

A new scheme of adaptive control is proposed for a class of linear time-invariant（ LTI） dynamical systems,especially in aerospace,with matched parametric uncertainties and input constraints. Based on a typical and conventional direct model reference adaptive control scheme,various modifications have been employed to achieve the goal. ＂C omposite model reference adaptive control＂of higher performance is seam-lessly combined with ＂positive μ-mod＂,which consequently results in a smooth tracking trajectory despite of the input constraints. In addition,bounded-gain forgetting is utilized to facilitate faster convergence of parameter estimates. The stability of the closed-loop systemcan be guaranteed by using Lyapunov theory.The merits and effectiveness of the proposed method are illustrated by a numerical example of the longitudinal dynamical systems of a fixed-wing airplane.

Optimal Neuro-Control Strategy for Nonlinear Systems With Asymmetric Input Constraints

In this paper,we present an optimal neuro-control scheme for continuous-time(CT)nonlinear systems with asymmetric input constraints.Initially,we introduce a discounted cost function for the CT nonlinear systems in order to handle the asymmetric input constraints.Then,we develop a Hamilton-Jacobi-Bellman equation(HJBE),which arises in the discounted cost optimal control problem.To obtain the optimal neurocontroller,we utilize a critic neural network(CNN)to solve the HJBE under the framework of reinforcement learning.The CNN's weight vector is tuned via the gradient descent approach.Based on the Lyapunov method,we prove that uniform ultimate boundedness of the CNN's weight vector and the closed-loop system is guaranteed.Finally,we verify the effectiveness of the present optimal neuro-control strategy through performing simulations of two examples.

Control Design for Harmonic Disturbance Rejection for Robot Manipulators with Bounded Inputs

Currently most of control methods are of one degree of freedom（1-DOF） control structure for the robot systems which are affected by unmeasurable harmonic disturbances,at the same time in order to obtain perfect disturbance attenuation level,the controller gain must be increased.In practice,however,for robotic actuators,there are physical constraints that limit the amplitude of the available torques.This paper considers the problem of tracking control under input constraints for robot manipulators which are affected by unmeasurable harmonic disturbances.A new control scheme is proposed for the problem,which is composed of a parameter-dependent nonlinear observer and a tracking controller.The parameter-dependent nonlinear observer,designed based on the internal model principle,can achieve an estimation and compensation of a class of harmonic disturbances with unknown frequencies.The tracking controller,designed via adaptive control techniques,can make the systems asymptotically track the desired trajectories.In the control design,the continuous piecewise differentiable increasing function is used to limit control input amplitude,such that the control input saturation is avoided.The Lyapunov stability of closed loop systems is analyzed.To validate proposed control scheme,simulation results are provided for a two link horizontal robot manipulator.The simulation results show that the proposed control scheme ensures asymptotic tracking in presence of an uncertain external disturbance acting on the system.An important feature of the methodology consists of the fact that the designed controller is of 2-DOF control structure,namely,it has the ability to overcome the conflict between controller gain and robustness against external disturbances in the traditional 1 -DOF control structure framework.

On guaranteed cost control of linear systems with input saturation

This work studies the problem of control design for linear systems with input saturation.It is well known that integral quadratic constraints(IQC) can be used to describe input saturation and that the use of IQC in analysis can lead to less conservative performance bound and larger domain of attraction.In this work,it is shown that a class of commonly used IQCs may not help in control synthesis.That is,the use of these IQCs does not enlarge the guaranteed domain of performance for synthesis.

具有H_(∞)性能的输入受限三级倒立摆控制器设计

【目的】针对直线三级倒立摆控制输入受限下的稳摆控制问题,设计了具有H_(∞)性能的输入受限三级倒立摆控制器。【方法】利用线性矩阵不等式(LMI)方法,将控制输入约束问题转化为利用线性矩阵不等式求解优化值的优化问题,经Lyapunov稳定性推导出符合条件的LMI,利用MATLAB中的LMI工具箱求解得到有效的K。进一步在控制输入受限下引入H_(∞)性能指标,使所设计的控制输入受限下的算法具有H_(∞)性能。【结果】仿真结果表明,该控制器对于控制输入受限下的复杂高阶次不稳定系统具有很好的控制效果。【结论】控制输入信号在给定的受限范围内实现了三级倒立摆的稳摆要求,且引入H_(∞)性能指标后控制输入和摆杆波动更小。

输入时滞多智能体系统的输入受限一致性控制

针对具有输入幅值约束的输入时滞机械系统,研究基于有向图拓扑的多智能体系统(multi-agent system,MAS)的一致性控制问题。对于只有部分智能体能够接收到理想信号的情况,提出一种观测器,使每个智能体都能观测到理想信号,并证明观测误差的指数收敛性。在控制器设计中,通过将系统的输入积分反馈到控制器中,消除输入延迟的影响;结合输入积分法和双曲正切函数,在解决输入延迟问题的同时限制控制输入幅值,并利用FMINCON函数优化控制参数。最后,利用李雅普诺夫方法证明控制目标的实现和闭环系统的渐近稳定性。仿真结果表明,所提出的观测器和控制器能够有效实现系统的一致性控制。

控制方向未知的受限多智能体系统的预设时间模糊控制

综合考虑受控制方向未知、输入受限和状态时延影响的有领导者多智能体系统的编队控制问题,设计基于模糊逻辑系统的预设时间一致性控制策略.为了保证编队输出误差在预设时间内满足预定的约束范围要求,引入预设时间性能函数,构造Lyapunov-Krasovskii(L-K)泛函解决状态时延问题,将外界干扰和L-K泛函的导数中的部分项定义为未知非线性函数,并利用模糊逻辑系统对其进行估计,利用Nussbaum函数和均值定理分别处理控制方向未知和输入受限问题,基于以上设计,提出预设时间模糊控制策略,并通过Lyapunov稳定性理论,分析闭环系统的有界稳定性,数值对比仿真和两级化学反应器应用仿真说明控制方法的有效性.

输入受限乘波体飞行器无估计预设性能控制

针对输入受限的乘波体飞行器跟踪控制问题,提出了一种无估计的预设性能控制方法。首先,针对可能发生的执行器饱和问题,设计了一种新型抗饱和补偿系统。然后,利用补偿系统状态构造新的预设性能转换误差,基于预设性能控制与反演控制的方法,为速度子系统与高度子系统设计了无需估计的低复杂度控制器。该设计方法的优越性在于无需状态估计与神经逼近,显著降低了控制的复杂度与计算量。基于Lyapunov稳定理论证明了所有转换误差与跟踪误差最终一致有界。最后,通过数值仿真验证了所提方法的有效性。

考虑输出约束和输入饱和的弹仓自适应控制

针对存在参数不确定性和外部扰动的某链式回转弹仓的位置跟踪控制问题,提出一种自适应控制方案。该方案基于神经模糊系统(Neuro-Fuzzy System,NFS)、非线性扰动观测器(Nonlinear Disturbance Observer,NDO)及障碍Lyapunov函数(Barrier Lyapunov Function,BLF),在实现高精度控制的同时满足输出约束和输入饱和的工程条件。通过BLF的设计,保证弹仓的位移跟踪误差约束在给定范围内。结合神经网络的函数逼近能力和模糊逻辑系统的推理能力,用于估计系统中的不确定性,减小对模型的依赖;两者结合的NFS作为NDO的一部分,进一步补偿估计误差和外部扰动,提高控制性能。此外,控制器设计考虑了运动过程中的执行器输入饱和问题。仿真结果表明,所设计的控制器在3种典型工况以及系统参数变化情况下均可实现弹仓的高精度位置跟踪控制,满足系统的约束条件。

一种控制输入约束下的不确定离散系统非脆弱保性能控制器设计

【目的】针对考虑扰动及摄动情况下的控制输入约束的不确定离散系统,提出了非脆弱保性能的控制方法。【方法】首先,以最小化目标函数为性能指标、控制输入饱和范围为约束条件,从而推导出约束状态下的非脆弱保性能控制律。其次,使用李雅普诺夫方程来构造非线性矩阵不等式。再次,结合Schur补定理和布谷鸟群智能优化算法对不等式进行求解,得到控制输入约束下的非脆弱保性能控制律的参数。最后,通过Quanser三自由度陀螺仪平台进行试验验证。【结果】试验结果表明,本研究所提出方法的稳态误差浮动不超过0.07、跟踪误差不超过0.15。【结论】该方法在面对扰动及摄动情况时具有更高的鲁棒性,对提升三自由度陀螺仪的稳定性及控制精度具有重要意义。

四旋翼无人机鲁棒嵌套饱和控制

针对四旋翼无人机在执行任务中,动力系统受到输入饱和限制,传统嵌套饱和控制算法收敛条件严格、收敛速度慢、稳态精度不高、鲁棒性不强等问题,提出了一种四旋翼无人机鲁棒嵌套饱和控制算法。首先,设计了条件相对放松的嵌套饱和控制算法,降低了设计难度,并且给出了闭环系统的稳定性证明。其次,基于条件放松的嵌套饱和控制算法,引入自耦PD和补偿函数观测器,提升系统的响应速度,稳态精度以及鲁棒性,并利用Lyapunov稳定性原理证明了闭环系统的全局稳定性。最后,将所提算法与传统嵌套饱和控制算法和PD控制算法进行了仿真对比,结果表明:本文提出的算法在暂态性能、稳态性能和鲁棒性上都是最优。

考虑推力陷阱的组合动力飞行器切换控制方法研究

针对组合动力飞行器动力切换时的推力陷阱问题,提出了一种考虑推力陷阱的组合动力飞行器切换控制方法。首先,针对组合动力飞行器正常飞行控制需求,基于超螺旋滑模控制方法,设计了正常飞行控制器,从而实现飞行器受扰动情况下的有限时间稳定跟踪参考指令;其次考虑组合动力飞行器动力切换引起的推力损失带来的控制约束,设计了输入约束控制器,实现模态转换阶段跟踪误差有限时间收敛的同时减小推力需求;然后考虑正常情况及模态转换阶段控制器的结合,设计了组合发动机软切换控制方法;最后通过仿真验证了所设计的控制器能够有效规避推力损失现象,并且能够有效保障切换控制系统的稳定性及鲁棒性。

未知不匹配互联系统的非对称输入约束分散控制器设计

基于自适应动态规划算法研究了具有未知不匹配互联和非对称输入约束的连续时间非线性系统分散控制问题.首先,根据孤立子系统的局部状态和耦合子系统的参考状态,采用径向基函数神经网络近似未知互连项,从而消除了互联项满足匹配条件且存在上界的常见假设.然后,基于自适应评判框架,将分散最优控制器设计问题转化为一系列子系统非对称约束下局部最优控制器设计问题.利用Lyapunov稳定性定理,证明了不对称输入约束控制器能够迅速地镇定大规模分散系统.其中,引入状态观测器估计大规模非线性互联子系统状态并保证了观测误差满足一致最终有界.另外,利用评判神经网络近似改进后的代价函数,以近似求解Hamilton–Jacobi–Bellman方程,获得满足非对称输入约束的最优分散控制策略.基于评判网络权值更新规则,通过选择合适的Lyapunov函数保证了权值近似误差满足一致最终有界.最后,通过仿真实例验证了该算法的有效性,并通过与未改进代价函数的传统方法对比,体现了该方法的先进性.

输入受限下自主水下航行器路径跟踪控制

针对欠驱动式自主水下航行器AUV的三维路径跟踪控制问题,设计了一种按设定误差性能指标函数收敛的反演滑模控制器。首先基于航行器的运动学和动力学模型将路径跟踪问题转化为非线性系统的镇定问题;然后,利用反演法和李雅普诺夫稳定性理论逐步修正虚拟控制律,从而使控制器能够保证系统的稳定性和鲁棒性。为解决舵执行器的速率受限的问题,通过调整跟踪误差的收敛过程速度的方法设计了控制器。仿真结果表明,上述控制器能够实现对三维直线路径的精确跟踪,而且明显降低了控制系统执行器的速率要求。

具有输入约束和扰动补偿的四旋翼无人机姿态稳定模型预测控制

为了解决四旋翼无人机飞行过程中的姿态控制问题,考虑受到执行器输入约束和外界未知扰动影响,设计了一种具有输入约束和扰动补偿的四旋翼无人机姿态稳定模型预测控制方法;设计简化的四旋翼无人机姿态动力学模型,降低控制器设计的复杂程度,设计带有输入约束的控制器,模拟饱和输入现象,实现饱和输入下的四旋翼姿态稳定控制;设计风扰观测器,实现对外部未知扰动的估计,有效跟踪外部持续扰动,并由此设计扰动补偿律;围绕代价函数设计带有扰动补偿律的最优控制律,作用于四旋翼姿态系统,实现四旋翼无人机姿态的稳定控制;最后进行数值仿真,设置风扰观测器参数λ_(i)为0.25,预测时域N_(p)为10,控制时域N_(c)为9,仿真测试文章方法与不带观测器的非线性预测控制方法(NMPC),验证文章控制方法的有效性和优越性。

Time-varying Sliding Mode Controls in Rigid Spacecraft Attitude Tracking

To solve the problem of attitude tracking of a rigid spacecraft with an either known or measurable desired attitude trajectory, three types of time-varying sliding mode controls are introduced under consideration of control input constraints. The sliding surfaces of the three types initially pass arbitrary initial values of the system, and then shift or rotate to reach predetermined ones. This way, the system trajectories are always on the sliding surfaces, and the system work is guaranteed to have robustness against parameter uncertainty and external disturbances all the time. The controller parameters are optimized by means of genetic algorithm to minimize the index consisting of the weighted index of squared error （ISE） of the system and the weighted penalty term of violation of control input constraint. The stability is verified with Lyapunov method. Compared with the conventional sliding mode control, simulation results show the proposed algorithm having better robustness against inertia matrix uncertainty and external disturbance torques.

Constrained predictive control based on T-S fuzzy model for nonlinear systems

A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.

一种独立光储发电系统用宽输入范围非隔离三端口变换器

针对独立光储发电系统中光伏电池输入电压的不稳定性,提出一种能够在大于和小于储能端口电压范围内工作的宽输入范围非隔离三端口变换器,可以实现光伏电池、蓄电池和负载之间的能量流动和功率平衡。该文所提变换器是由传统的Buck、Boost和Buck-Boost变换器分别与双向升降压四开关Buck-Boost(FSBB)变换器进行组合得到。FSBB用于连接光伏电池和蓄电池端口,可以削弱储能端口电压对光伏端口电压的约束,满足光伏端口宽电压输入的应用需求,增加了系统的稳定性。该文以Boost变换器和FSBB变换器进行组合为例设计实验样机,通过实验验证了该变换器理论分析的正确性和所提控制策略的可行性。

输入约束下的欧拉-伯努利梁的鲁棒自适应边界振动控制

为抑制系统的振动,该文针对边界扰动下的欧拉-伯努利梁系统,提出了输入约束下的鲁棒自适应边界振动控制器。建立了1个基于双曲正切函数以及Nussbaum函数的辅助系统来处理系统的输入约束问题。在此基础上,针对系统参数的不确定性,提出了系统参数的自适应方法以解决该问题。通过Lyapunov理论,证明了系统在所提出的饱和控制器以及自适应律下的稳定性。最后结合1个仿真实例来说明该文控制策略的优越性。