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.

Multivariable Decoupling Predictive Control with Input Constraints and Its Application on Chemical Process

A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.

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.

Robust predictive control of uncertain intergrating linear systems with input constraints

This paper presents a two-stage robust model predictive control (RMPC) aigorithm named as IRMFC for uncertain linear integrating plants described by a state-space model with input constraints. The global convergence of the resulted closed loop system is guaranteed under mild assumption. The simulation example shows its validity and better performance than conventional Min-Max RMPC strategies.

Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

In this paper, a study of control for an uncertain2-degree of freedom(DOF) helicopter system is given. The2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function(IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.

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.

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。

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。

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。

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。

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。

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。

控制输入受限无人机轨迹跟踪与防撞一体化导引控制

无人机在进行轨迹跟踪时需要考虑未知障碍物防撞约束和控制信号幅值约束等一系列问题以保障飞行安全。针对无人机平台在控制输入约束下轨迹跟踪与防撞一体化控制问题,以商业四旋翼平台为开发对象,采用非线性模型预测控制(NMPC)方法设计了轨迹跟踪导引控制器并进行改进以满足一体化控制需求。首先,通过对轨迹跟踪NMPC优化问题施加基于势场函数的惩罚项,实现防撞功能扩展。然后,利用反步法设计基于Lyapunov函数的收缩项作为NMPC优化问题的附加约束项以保障控制的稳定性,并在此基础上引入动态调整预测时域机制,在保证稳定的轨迹跟踪前提下缓解计算负载。对比仿真结果表明,引入的稳定性约束项和防撞惩罚项有效地改善了NMPC控制性能。本文所设计的轨迹跟踪与防撞一体化控制器考虑了控制输入约束问题,具有实际工程应用价值。

基于齐次系统理论的多船有限时间饱和包容控制

针对多领导者船舶编队控制问题,本文基于齐次系统有限时间稳定性理论,利用特定饱和约束函数,提出一种数学形式简化的有限时间饱和包容控制策略,实现船舶在有限时间内收敛于由虚拟领航船张成的凸包内,并保持其姿态镇定。将船舶模型化为积分形式,利用自身状态信息以及其他船舶的状态信息,提出一种饱和包容控制律;考虑闭环系统的非齐次性,综合图论知识、Lyapunov稳定性理论以及齐次系统稳定性理论等方面内容,从理论上证明编队系统中所有状态变量能够在有限时间内收敛;仿真结果证明了所提控制策略的有效性。

输入受限下的无人机编队控制方法研究

针对多旋翼飞行器在轨迹跟踪过程中控制输入受限的问题,设计了一种控制输入受限下的四旋翼无人机协同编队分布式控制算法。首先,对于外环位置子系统,基于线性矩阵不等式的方法,设计了输入受限下的多旋翼飞行器的编队控制律,使得所有无人机的位置收敛到所需的编队模式。其次,对于内环姿态子系统,采用基于双曲正切的方法设计控制输入受限的控制律,使多无人机的姿态趋于一致。最后,基于Lyapunov稳定性理论,证明了系统的稳定性,仿真结果表明,所设计的控制器能够达到良好的协同跟踪控制效果。

部分约束下空中目标拦截制导控制一体化方法

针对拦截空中目标的场景,提出一种考虑攻击角度的制导控制一体化(integrated guidance and control,IGC)方法,同时考虑输入饱和以及攻角的约束问题。首先,在俯仰平面下对包含不确定性的系统进行了建模。基于反演法和指令滤波器,处理了攻击角度约束问题和执行器机械限制的输入饱和问题,设计了含误差积分反馈的补偿项以处理跟踪误差,引入了障碍Lyapunov函数将攻角约束在预设区间。基于Lyapunov理论证明了闭环系统的稳定性和变量的有界性,以及各约束条件的成立性。仿真实验表明,方法能够以预设角度有效拦截目标,过程满足对输入以及攻角的约束条件,同时具备较强的鲁棒性。