猎雷声纳基阵姿态稳定系统自适应反步法控制

针对带非线性摩擦力矩和负载扰动的高精度猎雷声纳基阵姿态稳定系统,提出了一种基于神经网络的自适应反步法控制方法。其中神经网络用于估计未知非线性摩擦力矩,进而设计反步法控制器和参数自适应律来对神经网络估计误差和负载扰动进行补偿。最后应用Lyapunov方法证明了所提出的自适应控制器能保证闭环系统的稳定性,并且可以通过选择适当的控制器参数来调整收敛率。仿真结果表明,基于神经网络的自适应反步法控制方法与PID控制相比,系统的动、静态性能指标及鲁棒性得到了全面的改善,与双闭环PID控制相比,跟踪精度提高了3倍多。     

静电陀螺支承控制的自适应逆控制分析

静电陀螺的支承控制系统中由于不可避免地存在建模不准确及对象扰动,传统的控制器设计只能在系统动态控制与对象扰动消除之间折衷。根据自适应逆控制的结构,利用模糊径向基函数神经网络进行对象建模、逆对象建模和扰动消除建模,设计了带扰动消除的自适应逆控制的八电极静电陀螺支承控制器。仿真表明,该控制器可以同时提高控制的精度和鲁棒性,在保证支承系统动态性能的同时,大大抵消对象扰动的影响,克服传统控制方法的折衷缺陷,对静电陀螺的自适应逆控制器的工程实现具有重要意义。     

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。     

基于扰动观测器的卫星姿态控制系统自适应有限时间容错控制

针对具有状态时变时滞、系统不确定性、可建模扰动、运行噪声和执行器故障的卫星姿态控制系统,提出一种基于扰动观测器的自适应有限时间复合主动容错控制策略。针对可建模扰动设计扰动观测器,然后基于扰动估计误差设计了主动容错控制器。该时滞依赖控制器包含反馈控制项、扰动补偿项和快速自适应故障补偿项。提出的容错控制策略不仅保证闭环系统动态方程的有限时间有界性,而且保证闭环测量输出对于系统不确定性、运行噪声、执行器故障等的鲁棒性。给出控制器增益限制矩阵存在的充分条件及其线性矩阵不等式形式,进而给出仿真算例。仿真结果表明,基于扰动观测器方法,设计的自适应有限时间容错控制器能够快速估计可建模扰动,进而有效地实现系统的闭环容错控制。相较于基于非复合的自适应有限容错控制器,提出的方法对于状态变量的估计均方根误差分别降低了28.9%、4.7%和36.0%;对于可建模扰动估计的均方根误差降低了38.8%。仿真验证了所提方法的有效性。     

导弹编队协同攻击分布式鲁棒自适应控制

在有向通信拓扑下研究了导弹编队的鲁棒自适应协同跟踪控制问题。针对导弹编队系统中队形跟踪、外部扰动和模型不确定性的情况,通过选取包含位置跟踪误差和速度跟踪误差的辅助变量,提出了一种基于有向通信拓扑的鲁棒自适应编队控制策略。提出了自适应律对未知参数进行估计,并且利用Lyapunov稳定性理论分析了闭环系统的渐近稳定性。进一步,对于通信时滞的情况,给出了系统渐近稳定所需要满足的条件。与滑模控制等传统鲁棒控制不同,所设计的鲁棒自适应控制器是连续的,更便于导弹编队系统的实现。数值仿真结果表明,队形跟踪误差小于0.03 m,队形保持误差小于0.07 m,所设计的控制器能实现高精度的编队跟踪控制。     

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。     

一种新型稳定平台伺服控制系统

深入研究了三自由度并串联混合机构稳定平台,设计了一个非线性自适应控制器。考虑到实际系统工作中存在摩擦、负载扰动和动力学参数误差,分离出动力学模型中的未建模动力学参数、摩擦力参数和负载扰动,建立了关于待辨识参数的线性动力学模型。运用Lyapunov方法设计了一个非线性自适应控制器。构建了并串联光电稳定平台伺服系统实验平台。分别将所设计的控制器与计算力矩控制器分别在高速和低速扰动情况进行了实验,实验表明所提出非线性自适应控制器在低速0.006(°)/s时,跟踪精度分别为滚转轴0.071°、俯仰轴0.064°、偏转轴0.038°,在20(°)/s高速状态下,跟踪精度分别为滚转轴0.045°、俯仰轴0.042°、偏转轴0.029°,其控制效果明显好于传统控制。     

带有轴间动力学解耦的三轴转台自适应控制

针对某型号转台存在的轴间非线性耦合，运用非线性解耦的方法进行了解耦设计，并基于解耦后的系统提出用超稳定性和正性的方法进行自适应模型跟随控制器设计，来解决系统在运行过程中参数发生变化对控制性能影响的问题。仿真结果表明，当系统参数在一定范围变化甚至被控对象模型阶次发生变化时，所设计的自适应模型跟随控制器仍能保证系统具有良好的控制性能。     

基于状态观测的双臂空间机器人分散容错控制

针对关节执行器发生部分失效故障的双臂空间机器人系统的控制问题,设计了一种基于状态观测器的自适应分散神经网络容错控制器。结合拉格朗日第二类方法建立了空间机器人系统的动力学方程。根据分散理论将空间机器人执行器故障的容错问题转化为参数不确定的非线性交联系统的自适应控制问题。利用状态观测器得到了系统的角速度信号,通过自适应分散神经网络对系统的不确定项与交联项进行估计。基于Lyapunov函数法给出了观测器与控制器的稳定性判据。数值仿真表明,无论执行器是否发生故障,该控制器均可以在2 s内实现高精度的轨迹跟踪控制,且观测器均能精准地估计关节的实际角速度信号,从而验证了理论分析的正确性与算法的可行性。     

多储液腔航天器刚液耦合动力学与复合控制

采用复合控制方法对充液航天器的姿态和轨道机动进行高精度控制.通过傅里叶-贝塞尔级数展开法,将低重力环境下液体的弯曲自由表面的动态边界条件转化为简单的微分方程,其中耦合液体晃动方程的状态向量由相对势函数的模态坐标和波高的模态坐标组成.通过广义准坐标下的拉格朗日方程得到航天器刚体部分运动和液体燃料晃动的耦合动力学方程,提出了自适应快速终端滑模策略和输入整形技术相结合的复合控制器,并分别用于控制携带有一个燃料腔和四个燃料腔航天器的轨道机动和姿态机动.通过数值模拟来验证控制器的效率和精度.结果表明,对于多储液腔航天器,如果在设计航天器的姿态和轨道控制器时没有充分考虑燃料晃动效应,那么在受控航天器系统中将会出现刚-液-控耦合问题并导致航天器姿态不稳定.而本研究中的复合自适应终端滑模控制器可以实现航天器机动的高精度控制并有效抑制液体燃料晃动.     

Backstepping control for the optoelectronic stabilized platform based on adaptive fuzzy logic system and nonlinear disturbance observer

A composite controller based on a backstepping controller with an adaptive fuzzy logic system and a nonlinear disturbance observer is proposed in this paper to address the disturbance and uncertainty issues in the control of the optoelectronic stabilized platform. The matched and unmatched disturbances and system uncertainty are included in the stabilized platform model. The system's uncertainty and disturbance are approximated and estimated using an adaptive fuzzy logic system and a nonlinear disturbance observer. Moreover, the backstepping control algorithm is utilized to control the system. The simulations are performed in four states to confirm the viability of the proposed control technique. The proportional integral controller, proportional integral-disturbance observer controller, and fuzzy backstepping controller are contrasted with the proposed controller. It has been noted that the proposed controller's instantaneous disturbance's highest value is 5.1°/s. The maximal value of the coupling output for the two gimbals utilizing the proposed controller, however, is 0.0008°/s and 0.0018°/s, respectively. The findings presented here demonstrate that the backstepping controller, which is based on an adaptive fuzzy logic system and a nonlinear disturbance observer, is capable of precise tracking and dynamic tracking of a stabilized platform under disturbance and uncertainty.

     

Adaptive sliding mode control of a chaotic nonsmooth-air-gap permanent magnet synchronous motor with uncertainties

Using the sliding mode control approach, a simple adaptive controller design method is proposed for a chaotic nonsmooth-air-gap permanent magnet synchronous motor (PMSM). The proposed method does not require the restrictive assumption that accurate information on the PMSM parameter and load torque values is available, thus it has robustness to model uncertainties. This paper analyzes the stability and convergence of the closed-loop control system, and this paper gives a discretized control algorithm for DSP implementation. Finally, this paper presents some simulation results to illuminate that the proposed method can effectively handle the controller design problem for a chaotic nonsmooth-air-gap PMSM under inaccurate information on the PMSM parameter and load torque values.     

Model-free chaos control based on AHGSA for a vibro-impact system

For a vibro-impact system with clearance, the model-free chaos control method based on adaptive hybrid gravitational search algorithm (or AHGSA algorithm for short) is proposed. Nonparametric time-varying dynamic linear model based on pseudo-partial-derivative is established using input/output data of the controlled system, and on this basis, the optimal controller is designed according to the quadratic performance index, and the controller parameters is optimized using AHGSA algorithm. By combining the artificial bee colony search operator and chaos optimization strategy, gravitational search algorithm (or GSA algorithm for short) is improved from three aspects (i.e., population initialization, velocity and position update, gravity coefficient adjustment) to achieve a balance between the global detection ability and the local development ability. AHGSA algorithm has good optimization accuracy and efficiency: The arbitrariness is avoided in controller parameters selection, and the quality of the chaos control is ensured as well. In simulation experiment, the model-free controller optimized is used to control the chaotic motion of a single-degree-of-freedom vibro-impact system with clearance to verify the validity and feasibility of the proposed chaos control method. The simulation results show that the control effect is good, and the proposed chaos control method has the following advantages: the proposed chaos control method does not depend on the precise model of the controlled system, and the controller is easy to be designed and implemented.     

Chaos synchronization of coupled neurons under electrical stimulation via robust adaptive fuzzy control

This paper presents a robust adaptive fuzzy controller to synchronize two gap junction coupled chaotic FitzHugh–Nagumo (FHN) neurons under external electrical stimulation. A variable universe adaptive fuzzy approximator is used to approximate the nonlinear uncertain function of the synchronization error system. Based on the Lyapunov stability theory, the obtained adaptive laws of fuzzy algorithm not only guarantee the stability of the closed loop error system, but also attenuate the influence of matching error and external disturbance on synchronization error to an arbitrarily desired level. Chaos synchronization is obtained by proper choice of the control parameters. The simulation results demonstrate the effectiveness of the proposed control method.     

Decentralized adaptive neural control of nonlinear systems with unknown time delays

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear time-delay systems. RBF neural networks (NNs) are used to tackle unknown nonlinear functions, then the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique along with the minimal-learning-parameters (MLP) algorithm. The proposed controller guarantees semi-global uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop large-scale system, while the tracking errors converge to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters for each subsystem is reduced to one, and three problems of “computational explosion,” “dimension curse” and “controller singularity” are solved, respectively. Finally, a numerical simulation is presented to demonstrate the effectiveness and performance of the proposed scheme.     

Adaptive control of a chaotic permanent magnet synchronous motor

This paper proposes a simple adaptive controller design method for a chaotic permanent magnet synchronous motor (PMSM) based on the sliding mode control theory which has given an effective means to design robust controllers for nonlinear systems with bounded uncertainties. The proposed sliding mode adaptive controller does not require any information on the PMSM parameter and load torque values, thus it is insensitive to model parameter and load torque variations. Simulation results are given to verify that the proposed method can be successfully used to control a chaotic PMSM under model parameter and load torque variations.     

Adaptive inverse control of variable speed wind turbine

The efficiency and reliability of wind power has been shown to be depending on the applied control strategy of the wind turbine. In this paper, an adaptive control strategy is proposed for variable speed wind turbine (VSWT), producing energy limitation above rated wind speed. In the proposed control strategy, the process is modeled using a neural networks based identifier, providing the sensitivity information of the process to the control input. Another neural networks is employed as an inverse model controller established via inverse system method. These two neural networks are off-line learned firstly and are on-line updated using the back propagation algorithm. Simulation results have shown the effectiveness of the proposed adaptive control strategy.     

Sliding mode control with adaptive fuzzy dead-zone compensation for uncertain chaotic systems

The dead-zone nonlinearity is frequently encountered in many industrial automation equipments and its presence can severely compromise control system performance. In this work, an adaptive variable structure controller is proposed to deal with a class of uncertain nonlinear systems subject to an unknown dead-zone input. The adopted approach is primarily based on the sliding mode control methodology but enhanced by an adaptive fuzzy algorithm to compensate the dead-zone. Using Lyapunov stability theory and Barbalat??s lemma, the convergence properties of the closed-loop system are analytically proven. In order to illustrate the controller design methodology, an application of the proposed scheme to a chaotic pendulum is introduced. A comparison between the stabilization of general orbits and unstable periodic orbits embedded in chaotic attractor is carried out showing that the chaos control can confer flexibility to the system by changing the response with low power consumption.     

温控箱数学模型的建立及其自适应PID控制

一本文针对用于惯性元件测试的数字式温度控制系统，从传热学原理出发，推导出了该系统温控箱的差分模型结构，并通过大量的实验确定了模型系数波动范围及均值；由此提出了一种较实用的自适应ＰＩＤ方案，改进了原有的常规ＰＩＤ控制器。仿真结果表明，改进的温控器较原控制器有较强的适应性。