惯性平台稳定回路基于Backstepping的动态滑模控制(英文)

针对带不匹配不确定非线性干扰的惯性平台稳定回路跟踪控制问题,提出了基于backstepping的动态滑模控制方法。首先,建立了惯性平台稳定回路的等价模型,该模型由一个线性模型加上一个不确定的非线性函数组成。然后,基于backstepping方法设计了带渐近稳定滑模面的动态滑模控制器,解决了模型不匹配的问题,并提高了系统的鲁棒性。进而应用Lyapunov稳定性理论证明了所设计的控制器不仅能保证闭环系统的稳定性,而且可以通过选择适当的控制器参数来调整跟踪误差的收敛率。最后,仿真结果表明,基于backstepping的动态滑模控制方法与PID控制方法相比,提高了系统的跟踪精度,增强了鲁棒性。     

航天器有限时间饱和姿态跟踪控制

针对刚体航天器系统,对存在模型不确定性、外界干扰力矩和控制器饱和等条件下的姿态跟踪控制问题进行了研究。首先,考虑未知模型不确定性和外界干扰,且总干扰上界为未知常数,结合快速非奇异终端滑模、快速终端滑模趋近律以及辅助系统构造了基本的鲁棒有限时间饱和控制器,并通过辅助系统直接补偿了控制器饱和;其次,针对系统总干扰具有多项式上界的情形,进一步结合自适应控制算法,对其上界函数中的未知参数进行在线估计,并设计了自适应有限时间饱和控制器。同时,基于Lyapunov稳定性理论证明了所提出控制算法的有限时间收敛特性。最后,通过数值仿真验证所提出控制算法的控制效果,在两种控制器作用下姿态的跟踪精度分别为5×10-5和1×10-5,证明了所提出控制算法的有效性。     

模糊控制理论在有源磁悬浮系统中的应用

建立了陀螺浮子磁悬浮数学模型,对该系统进行了MATLAB仿真并分析了该系统的刚度和阻尼特性。在此基础上,计算了满足刚度和阻尼特性的PID控制器参数,仿真结果表明,基于刚度和阻尼特性的PID控制器能够基本满足系统的要求,但响应时间过长。因此进行了模糊控制理论在磁悬浮系统中的研究,提出了模糊-PID控制。以经典PID控制器参数为参考,建立了模糊控制隶属度函数,设计了模糊控制表格,并对模糊-PID控制器在该系统中的应用进行了仿真研究,仿真结果表明,建立在经典PID控制器基础上的模糊-PID控制能够得到较好的稳态特性和动态性能,鲁棒性也得到增强,能够在一定程度上提高液浮陀螺仪的精度和抗干扰能力。     

滑模/无模型自适应控制方法及在离心-振动试验系统中的应用

针对滑模控制对非匹配不确定性系统的控制问题,提出了滑模/无模型自适应控制算法。通过滑模控制方法对系统的标称部分及匹配部分进行控制,对于非匹配不确定性部分,以滑模控制器的输出与系统期望输出的误差作为无模型自适应控制器的输入,通过无模型自适应方法来减少未建模不确定性对系统控制的影响。该方法对系统中匹配和非匹配不确定项均具有鲁棒性,比传统滑模控制及无模型自适应方法具有更快的收敛速度。理论分析证明了系统的稳定性,仿真结果验证了该方法的有效性。     

结构振动的滑模变结构半主动控制

研究应用磁流变阻尼器(MRD)对结构振动半主动控制的算法和原理。研制并对磁流变阻尼器进行了阻尼特性实验，采用非线性滞回双粘性模型描述磁流变阻尼器的阻尼特性，模型结果与实验结果非常一致。采用滑模控制算法和趋近律方法设计了半主动控制器。利用滑模控制方法所建立的控制器，本文给出了地震激励下结构振动半主动控制算例。计算分析表明，半主动滑模控制具有控制效果明显、鲁棒性好等优点，是一种非常有发展前途的控制方法。     

四旋翼飞行器分散PID神经元网络控制

针对四旋翼飞行器的非线性控制问题,提出了一种分散PID神经元网络(PIDNN)控制方法。首先通过牛顿—欧拉方程建立了四旋翼飞行器的动力学模型。其次,提出了一种嵌套控制器,内环基于分散PIDNN方法以实现姿态控制,外环采用经典的PID控制方法,PIDNN控制器的在线学习通过误差反向传播法实现。搭建了自主研制的四旋翼飞行器系统,并通过实验的方式研究了控制器的控制性能。实验结果表明控制器具有较强的控制稳定性、机动性和鲁棒性。     

抑制线振动台扰动的鲁棒自适应重复控制

针对线振动台系统中存在的非线性动态摩擦力及周期性纹波推力扰动,为获得线振动台较高的跟踪精度及鲁棒性能,提出了鲁棒自适应重复控制方法.该方法的控制律包含参数自适应控制、等效PID控制、重复空制和滑模控制.滑模控制用来镇定不确定性系统和保证自适应重复学习过程收敛,参数自适应律用来估计未知模型参数并予以补偿,重复控制用来抑制周期性扰动,提高周期性位置信号的跟踪性能.Lyapunov理论证明该控制律保证了闭环系统渐近稳定性.通过对线振动台系统仿真研究表明该控制律的有效性.     

低轨纳卫星质量矩姿态控制技术研究

针对气动力矩严重影响低轨纳卫星姿态控制效果的问题,创新性地提出了利用质量矩技术将气动干扰转化为控制力矩的解决方法.由于气动力矩矢量垂直于大气来流速度方向,因而采用质量矩与磁力矩相结合的方式三轴全驱动控制卫星姿态,从而避免系统欠驱动. 建立双执行机构控制方式的姿态动力学模型,并根据各干扰项的影响简化了控制方程.针对气动力不确定、星体参数误差、未知环境影响等复杂干扰,设计了针对理想控制力矩基于干扰观测器的滑模控制器. 为减小滑块附加干扰力矩,研究了理想控制力矩的最优分配策略. 最后, 为双执行机构搭建了半物理仿真平台,结果表明: 姿态机动过程中, 与滑块加速度相关的附加惯性力矩远大于其他干扰项,最优力矩分配策略能够大幅减小快时变的附加干扰, 优化效果明显; 姿态保持过程中,干扰观测器能有效观测系统慢时变干扰, 提高滑模控制律的姿态控制精度,姿态角收敛误差小于$\pm $0.1$^\circ$.最终验证了在低轨纳卫星上利用质量矩技术控制姿态的可行性.      

基于最优PID和LQG算法的空间望远镜大口径快摆机构控制系统仿真

空间望远镜在观测时会受到不确定性扰动,这些扰动的特性为幅值小,频带宽,控制难,而且望远镜平台的振动成分大部分在10 Hz以内。为了减小这些低频振动造成的干扰,对空间望远镜的大口径FSM系统进行控制器设计使其能够对低频扰动具有良好的抑制作用,选择的控制算法为在ITAE指标最优情况下的PID算法和带有积分作用的LQG算法。利用Simulink对系统搭建模型,仿真结果表明:FSM系统在PID控制器作用下的响应时间为0.4 s,在LQG控制器作用的响应时间为0.04 s,且都无稳态误差。利用OICETS卫星的振动功率谱密度数据对系统的抑制能力进行验证,在低频段0~10Hz范围内:跟踪模式时,系统在PID控制器作用下,抑制能力为14.5 d B,系统在LQG控制器作用下,抑制能力为32.5 d B;瞄准模式时,系统在PID控制器作用下,抑制能力为10.3 d B,系统在LQG控制器作用下,抑制能力为23.6 d B。经过比较,该大口径FSM系统在LQG控制器作用下的系统性能明显优于在最优PID控制器作用下。     

基于双驱动智能悬臂梁的自校正PID振动控制研究

随着科技不断进步,智能结构的振动控制在航天航空、机械制造、车辆与船舶等领域得到了广泛应用。由于多输入多输出存在多样性和复杂性,严重威胁系统稳定性。为了解决这一问题,针对两输入单输出的双驱动智能悬臂梁系统提出一种自适应控制策略,首先基于压电线性本构方程,应用假设模态方法建立双驱动智能悬臂梁的力学模型,得到了基于闭环控制系统的状态方程,同时利用递推最小二乘法在线辨识系统参数设计比例积分微分(proportional–integral–derivative, PID)控制器实现自校正PID控制。通过数值仿真对比在有无PID控制下两输入单输出双驱动智能悬臂梁系统的振动情况,分析自校正PID控制的控制效果。通过实验验证自校正PID控制对双输入单输出的双驱动智能悬臂梁系统的控制效果;再设置两组不同的单输入单输出自校正PID控制实验作对比。结果表明:自校正PID控制方法可以较为有效地抑制智能悬臂梁的自由振动,相比单输入单输出的两组,两输入单输出自校正PID控制的效果更为明显和有效。     

惯性稳定平台扩张状态观测器/PD复合控制

为提高惯性稳定平台控制系统的稳定精度,在常规PID控制的基础上提出了一种扩张状态观测器与PID相结合的复合控制算法。利用扩张状态观测器将惯性稳定平台的各种内部扰动和外部扰动都视为总和扰动并观测出来,然后通过PID控制器进行误差反馈控制,从而提高控制系统的扰动抑制能力与稳定精度。以Lu Gre摩擦模型加入控制模型进行仿真分析,并通过北航自研的惯性稳定平台进行实验验证。结果表明:扩张状态观测器/PD复合控制方法具有高的扰动抑制能力,可显著提高稳定平台稳定精度。相比常规PID方法,扩张状态观测器/PD复合控制使横滚框和俯仰框的稳定精度分别提高了33.23%和55.01%。     

Adaptive robust control of Mecanum-wheeled mobile robot with uncertainties

This paper presents a novel implementation of an adaptive robust second-order sliding mode control (ARSSMC) on a mobile robot with four Mecanum wheels. Each wheel of the mobile robot is actuated by separate motors. It is the first time that higher-order sliding mode control method is implemented for the trajectory tracking control of Mecanum-wheeled mobile robot. Kinematic and dynamic modeling of the robot is done to derive an equation of motion in the presence of friction, external force disturbance, and uncertainties. In order to make the system robust, second-order sliding mode control law is derived. Further, adaptive laws are defined for adaptive estimation of switching gains. To check the tracking performance of the proposed controller, simulations are performed and comparisons of the obtained results are made with adaptive robust sliding mode control (ARSMC) and PID controller. In addition, a new and low-cost experimental approach is proposed to implement the proposed control law on a real robot. Experimental results prove that without compromising on the dynamics of the robot real-time implementation is possible in less computational time. The simulation and experimental results obtained confirms the superiority of ARSSMC over ARSMC and PID controller in terms of integral square error (ISE), integral absolute error (IAE), and integral time-weighted absolute error (ITAE), control energy and total variance (TV).     

基于干扰观测器的柔性关节空间机器人退步自适应控制

研究了载体位置、姿态均不受控的情况下,系统参数不确定的柔性关节空间机器人轨迹跟踪的控制问题.结合系统动量、动量矩守恒关系,利用拉格朗日法推导出系统的动力学模型.为减小系统柔性关节对系统控制精度的影响,采用关节柔性补偿器来等效降低系统关节的柔性.再借助奇异摄动法,针对系统参数不确定的情况,设计了柔性关节空间机器人基于干扰观测器的退步自适应滑模控制方案.该方案不需要对系统惯性参数进行线性化处理,控制器结构简单,且实现了空间机器人期望轨迹的精确跟踪控制.通过平面两杆空间机器人的数值仿真证明了该方法的有效性.     

Dynamic controller design for a class of nonlinear uncertain systems subjected to time-varying disturbance

Inheriting advantages of both proportional-integral-derivative controller and standard sliding mode control theory, a synthetic controller design for a class of nonlinear system is presented. Regarding the architecture of the developed controller, it does not include model-based nominal control term so that the method eliminates complicated processes for system parameters identification and design of extra compensators. With simple gain tuning rules, the proposed control algorithm provides global asymptotical stability and is capable of alleviating discontinuous control switching considerably. A self-sustained oscillations phenomenon caused by the proposed control configuration is also further addressed. Simulations and experiments are conducted to verify the feasibility and applicability of the proposed approach.     

A switching fractional calculus-based controller for normal non-linear dynamical systems

In this paper, a fractional calculus-based terminal sliding mode controller is introduced for finite-time control of non-autonomous non-linear dynamical systems in the canonical form. A fractional terminal switching manifold which is appropriate for canonical integer-order systems is firstly designed. Then some conditions are provided to avoid the inherent singularities of the conventional terminal sliding manifolds. A non-smooth Lyapunov function is adopted to prove the finite time stability and convergence of the sliding mode dynamics. Afterward, based on the sliding mode control theory, an equivalent control and a discontinuous control law are designed to guarantee the occurrence of the sliding motion in finite time. The proposed control scheme uses only one control input to stabilize the system. The proposed controller is also robust against system uncertainties and external disturbances. Two illustrative examples show the effectiveness and applicability of the proposed fractional finite-time control strategy. It is worth noting that the proposed sliding mode controller can be applied for control and stabilization of a large class of non-autonomous non-linear uncertain canonical systems.     

基于浸入与不变原理的四旋翼姿态系统反步滑模控制

针对较大幅度外部不确定扰动下的四旋翼姿态稳定问题,设计了一种基于浸入与不变原理(ⅠⅠ)的自适应反步滑模控制器(ABSMC)。首先建立了未知大扰动下四旋翼姿态系统动力学模型,然后以横滚角子系统搭建为例,设计并应用了反步法和基于趋进率的滑模控制策略。在扰动估计误差流型设计中,融合了ⅠⅠ原理,即自适应率的选取实现了误差流型的不变和吸引,确保估计误差收敛到0。最后,对系统进行了稳定性分析和数字仿真。结果表明,在较大未知扰动情况下,融合ⅠⅠ原理方法后,经10 s所测跟踪误差平方的累加和仅为传统ABSMC方法的11.2%,控制精度大幅提高。     

A particle swarm optimization approach for fuzzy sliding mode control for tracking the robot manipulator

In this paper, an optimal fuzzy sliding mode controller is used for tracking the position of robot manipulator, is presented. In the proposed control, initially by using inverse dynamic method, the known sections of a robot manipulator’s dynamic are eliminated. This elimination is done due to reduction over structured and unstructured uncertainties boundaries. In order to overcome against existing uncertainties for the tracking position of a robot manipulator, a classic sliding mode control is designed. The mathematical proof shows the closed-loop system in the presence of this controller has the global asymptotic stability. Then, by applying the rules that are obtained from the design of classic sliding mode control and TS fuzzy model, a fuzzy sliding mode control is designed that is free of undesirable phenomena of chattering. Eventually, by applying the PSO optimization algorithm, the existing membership functions are adjusted in the way that the error tracking robot manipulator position is converged toward zero. In order to illustrate the performance of the proposed controller, a two degree-of-freedom robot manipulator is used as the case study. The simulation results confirm desirable performance of optimal fuzzy sliding mode control.     

Adaptive interval type-2 fuzzy sliding mode control for unknown chaotic system

In this paper, a novel adaptive interval type-2 fuzzy sliding mode control (AIT2FSMC) methodology is proposed based on the integration of sliding mode control and adaptive interval type-2 fuzzy control for chaotic system. The AIT2FSMC system is comprised of a fuzzy control design and a hitting control design. In the fuzzy control design, an interval type-2 fuzzy controller is designed to mimic a feedback linearization (FL) control law. In the hitting control design, a hitting controller is designed to compensate the approximation error between the FL control law and the interval type-2 fuzzy controller. The parameters of the interval type-2 fuzzy controller, as well as the uncertainty bound of the approximation error, are tuned adaptively. The adaptive laws are derived in the sense of Lyapunov stability theorem, thus the stability of the system can be guaranteed. The proposed control system compared to adaptive fuzzy sliding mode control (AFSMC). Simulation results show that the proposed control systems can achieve favorable performance and robust with respect to system uncertainties and external disturbances.     

Adaptive sliding mode control of dynamic system using RBF neural network

This paper presents a robust adaptive sliding mode control strategy using radial basis function (RBF) neural network (NN) for a class of time varying system in the presence of model uncertainties and external disturbance. Adaptive RBF neural network controller that can learn the unknown upper bound of model uncertainties and external disturbances is incorporated into the adaptive sliding mode control system in the same Lyapunov framework. The proposed adaptive sliding mode controller can on line update the estimates of system dynamics. The asymptotical stability of the closed-loop system, the convergence of the neural network weight-updating process, and the boundedness of the neural network weight estimation errors can be strictly guaranteed. Numerical simulation for a MEMS triaxial angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive RBF sliding mode control scheme.