基于最小模型组最优模型扩展的机动目标跟踪方法

针对变结构多模型算法中模型集自适应较复杂,且模型扩展受模型结构限制等问题,提出了一种最小模型组最优模型扩展的机动目标跟踪算法。该算法以最小模型组作为基础有效模型集,采用模型组切换方法进行模型组自适应;并根据Kullback-Leibler距离准则在连续的模型空间中对基础模型组进行最优模型扩展。因此,该算法具有模型集自适应简单、模型激活不受模型结构的限制等优点。多组实验仿真结果表明:该算法既可以对相同结构的模型进行激活,也可以对不同结构的模型进行激活;在没有明显增加计算量的同时,提高了目标的跟踪精度,具有较好的跟踪效果。     

模型参数自适应的低复杂度ATPM-VSIMM算法

在机动目标跟踪中,针对交互式多模型算法使用固定模型集和固定转移概率矩阵导致跟踪精度下降的问题,提出模型参数自适应更新的低复杂度ATPM-VSIMM算法。所提算法根据系统新息变化情况来判断目标是否出现机动,从而调整模型集的状态噪声,实现模型集的自适应更新;然后,根据模型后验概率变化情况和模型间的相互切换关系,准确地计算出转移概率矩阵,从而提高系统运动模型和目标运动轨迹的匹配程度,保证跟踪系统具有滤波精度高和响应速度快的优点。从模型后验概率初值、转移概率矩阵初值和状态噪声三方面验证了所提算法的有效性。仿真结果表明,ATPM-VSIMM算法的空间位置跟踪精度比现有算法提高了8%左右。     

改进型BP神经网络的非线性多模型自适应控制

针对一类参数未知的非线性离散系统,提出一种基于改进型BP神经网络的多模型控制方法.首先将非线性系统表示为线性部分和非线性部分.当非线性部分对系统影响较小时,则直接采用基于固定模型和自适应模型而设计的鲁棒控制器对系统进行控制;而当非线性部分对系统影响较大时,则采用基于改进的BP神经网络的自适应控制.其次,利用切换准则对控制输入进行平滑切换并给出了稳定性证明.最后,仿真结果表明所提方法能提高系统控制品质、减少控制信号的振荡.     

马尔可夫参数自适应IFIMM算法研究

针对新息滤波交互式多模型(IFIMM)算法中切换过程模型概率滞后的问题,提出了模型概率转移矩阵马尔可夫参数自适应的新息滤波多模型算法(AMP-IFIMM),该方法采用后验信息修正不准确的先验信息,自适应的调整马尔可夫转移矩阵的参数.切换时刻较多地遗忘非匹配模型的信息,放大匹配模型的信息,在保证滤波精度的同时,大大提高了模型间切换速度.将该算法应用到CA,CV两模型组合导航系统取得了良好的效果.     

基于两种参数估计算法的离散时间确定系统的多模型自适应控制研究

对于参数存在较大变动的系统或时变系统(如系统出现局部故障),多模型自适应控制系统的过渡误差小于单一的自适应控制系统。基于多种参数估计算法的多模型自适应控制方法用于系统的参数估计和控制,参数收敛速度上优于基于一种参数估计算法的多模型自适应控制方法。本文研究了离散时间确定系统的多模型自适应控制问题,并介绍了两种递推参数估计算法及其收敛性质,最后给出仿真例子说明了结论的正确性。     

封闭空间内的多模型自适应有源前馈噪声控制

误差通道建模是实现自适应有源噪声控制算法的重要环节.由于次级通路建模误差对整个系统稳定性有重要影响,利用Modified FXLMS算法结合多模型自适应控制与封闭空间误差通道特性,提出一种针对封闭空间自适应有源控制的多模型算法,完成了该算法的性能分析和计算机仿真.     

基于角速度修正的变结构多模型目标跟踪算法

为提高强机动目标的跟踪精度,提出一种基于角速度估计值自适应修正的变结构多模型算法。将角速度估计应用于基于有向图切换的变结构多模型目标跟踪算法。通过引入改进的角速度估计方法,提高了角速度的估计精度。在有向图切换的基础上,实时估计角速度,并根据角速度估计值修正有向图,增强了变结构多模型目标跟踪算法的机动适应性。仿真结果表明,该方法在对强机动目标的跟踪性能上有明显提高。     

光电跟踪系统捕获跟踪切换的平滑调节方法

光电跟踪系统中,通常需要应用多个控制器完成搜索、捕获和跟踪.在多控制器之间直接切换可能会产生大的瞬态误差,导致目标脱离视场.提出了基于单个实参数控制的系统频响特性对称调节方法,实现了捕获和跟踪控制的平滑转换;给出了对称调节器的标准形式,可通过频域上的转换得到实际的调节控制器.实验结果表明:采用单参数对称调节的捕获跟踪控制模式转换平滑、快速、瞬态响应快、超调量小.     

多旋翼无人飞行器机载云台的复合稳定控制方法

针对多旋翼无人飞行器机载云台的稳定控制要求,提出一种采用双速度环控制结构的带有模糊切换条件的模糊自适应PID复合稳定控制方法。在深入分析控制结构扰动抑制能力的基础上,通过模糊自适应控制中自调整因子的引入和控制规则的在线修正,提高系统的快速响应能力;利用变速积分PID控制保证系统的高稳定精度,模糊切换条件实现复合控制的平稳切换。动态响应和稳态精度实验表明,系统的调节时间约为20 ms,稳定精度为0.13mrad。该方法有效地实现了机载云台的稳定控制,完全满足了多旋翼无人飞行器的应用需求。     

基于非线性滤波的自适应交互式多模型算法

研究了基于非线性条件下的自适应交互多模型算法,并将EKF及UKF引入自适应交互多模型算法(AIMM).交互多模型算法(IMM)是机动目标跟踪中比较有效的方法,然而传统IMM算法中的滤波参数完全是人为先验确定的,并没有利用当前时刻量测中的信息,文中给出基于后验概率的模型转移概率自适应交互多模型算法.最后通过一个仿真实例比较了AIMM中EKF方法与UKF方法及传统IMM方法的优劣,并分析了结论.     

液压伺服系统自适应模糊变结构控制

结合变结构控制、自适应控制和模糊技术等特点,提出一种自适应模糊变结构控制方法.首先,设计一个带积分开关平面函数的变结构控制器,并构造一个二维模糊边界层宽度调节器以削弱抖振.其次,基于Lyapunov稳定性理论,引入一自适应算法,自适应调节变结构控制参数.应用于液压伺服系统的控制实验结果表明,所提出的控制方法能削弱抖振,改善液压伺服系统稳态控制精度,具有较强的鲁棒自适应综合性能.     

Trajectory Switching Control of Robotic Manipulators Based on RBF Neural Networks

In this paper, we discuss the trajectory switching neural control problem for the switching model of a serial n-joint robotic manipulator. The key feature of this paper is to provide the dual design of the control law for the developed adaptive switching neural controller and the associated robust compensation control law. RBF Neural Networks (NNs) are employed to approximate unknown functions of robotic manipulators and a robust controller is designed to compensate the approximation errors of the neural networks and external disturbance. Via switched multiple Lyapunov function method, the adaptive updated laws and the admissible switching signals have been developed to guarantee that the resulting closed-loop system is asymptotically Lyapunov stable such that the joint position follows any given bounded desired output signal. Finally, we give a simulation example of a two-joint robotic manipulator to demonstrate the proposed methods and make a comparative analysis.     

Implementation of LLCC-resonant driving circuit and adaptive CMAC neural network control for linear piezoelectric ceramic motor

In this paper, an adaptive cerebellar-model articulation computer (CMAC) neural network (NN) control system is developed for a linear piezoelectric ceramic motor (LPCM) that is driven by an LLCC-resonant inverter. The motor structure and LLCC-resonant driving circuit of an LPCM are introduced initially. The LLCC-resonant driving circuit is designed to operate at an optimal switching frequency such that the output voltage will not be influenced by the variation of quality factor. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive CMAC NN control system is designed without mathematical dynamic model to control the position of the moving table of the LPCM drive system to achieve high-precision position control with robustness. In the proposed control scheme, the dynamic backpropagation algorithm is adopted to train the CMAC NN online. Moreover, to guarantee the convergence of output tracking error for periodic commands tracking, analytical methods based on a discrete-type Lyapunov function are utilized to determine the optimal learning-rate parameters of the CMAC NN. The effectiveness of the proposed driving circuit and control system is verified by experimental results in the presence of uncertainties, and the advantages of the proposed control system are indicated in comparison with a traditional integral-proportional position control system. Accurate tracking response and superior dynamic performance can be obtained due to the powerful online learning capability of the CMAC NN with optimal learning-rate parameters.     

基于脉冲控制的时延异构多智能体编队控制

为提高编队系统鲁棒性,降低系统对通信条件的要求,针对异构多智能体系统的编队控制问题,提出一种基于脉冲控制的编队控制方法.考虑异构多智能体的切换拓扑与变时延情况,根据一致性理论,提出基于领航跟随者模型的脉冲控制协议.利用矩阵分析方法将异构多智能体编队系统的变时延问题转化为时延上界问题,通过构造Lyapunov函数并利用Lyapunov稳定理论得到系统实现编队控制一致性的充分条件.同时,为比较不同控制协议的优劣性,提出切换拓扑下多智能体编队控制的平均通信代价指标.仿真结果验证了所提脉冲控制方法的有效性和优越性.     

Adaptive Neuro-Wavelet Control for Switching Power Supplies

The switching power supplies can convert one level of electrical voltage into another level by switching action. They are very popular because of their high efficiency and small size. This paper proposes an adaptive neuro-wavelet (ANW) control system for the switching power supplies. In the ANW control system, a neural controller is the main controller used to mimic an ideal controller and a compensated controller is designed to recover the residual of the approximation error. In this study, an online adaptive law with a variable optimal learning-rate is derived based on the Lyapunov stability theorem, so that not only the stability of the system can be guaranteed but also the convergence of controller parameters can be speeded up. Then, the proposed ANW control system is applied to control a forward switching power supply. Experimental results show that the proposed ANW controller can achieve favorable regulation performance for the switching power supply even under input voltage and load resistance variations     

Neural Network Based Adaptive Tracking of Nonlinear Multi-Agent System

In this paper, the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered. An adaptive control strategy is proposed to smooth the agent’s trajectory, and the neural network is constructed to estimate the system’s unknown components. The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties. Then, the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’ models. Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control. Finally, the theoretical results are verified by numerical simulations, and a comparative experiment is conducted, showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster.     

An adaptive switching learning control method for trajectory tracking of robot manipulators

In this paper, a new adaptive switching learning control approach, called adaptive switching learning PD control (ASL-PD), is proposed for trajectory tracking of robot manipulators in an iterative operation mode. The ASL-PD control method is a combination of the feedback PD control law with a gain switching technique and the feedforward learning control law with the input torque profile. The torque profile is updated by the previous torque profile (which makes sense for learning). Furthermore, in this new control method, the switching control scheme is integrated into the iterative learning procedure; as such, the trajectory tracking converges very fast. The ASL-PD method achieves the asymptotical convergence based on the Lyapunov’s method. The ASL-PD method possesses both adaptive and learning capabilities with a simple control structure. The simulation study validates this new method. In particular, both position and velocity tracking errors monotonically decrease with the increase of the number of iterations. The convergence rate with the ASL-PD method is faster than that of the adaptive iterative learning control method proposed by others in literature.     

Adaptive Control with Guaranteed Contraction Rate for Systems with Actuator Saturation

The problem of adaptive control of linear discrete-time systems with actuator saturation and unknown parameters is investigated. A novel optimal control method is presented first for linear systems with known parameters and constant actuator saturation by introducing a Lyapunov function and a performance cost function that are both dependent on a contraction rate parameter. Based on the obtained guaranteed contraction-rate control method, an adaptive control algorithm is derived for systems containing unknown system parameters and time-varying actuator saturation. To show that the closed-loop system is stable and that the adaptive control algorithm is convergent, the Lyapunov function is supplemented by an additional part defined by the trace of a quadratic function of the controller gain. The effectiveness and potential of the presented method is demonstrated by a numerical example.     

Unfalsified Adaptive PID Control for Time-Varying Systems Using a Fading Memory Cost Function

In this paper, we present a new cost function based on fading memory and time-window in order to decrease the influence of old data in unfalsified adaptive control applications, where the plant varies slowly or changes suddenly with time. Based on the unfalsified adaptive PID control, and the linear increasing cost-level algorithm (LICLA) switching algorithm, the new cost function can guarantee that the switching will stop and the system is stable. A systematic analysis of the system stabilization has been given. The simulation results show that without any prior knowledge of the system plant, when the current controller inserted in the system cannot guarantee the stability of the system, the cost function with a fading memory can detect the instability more quickly and then switch into a new stabilizing controller faster than the original cost function.     

Partial Stability and Adaptive Control of Switched Nonlinear Systems

Partial stability and the adaptive control problem are studied for switched nonlinear systems in this paper. A sufficient condition for partial stability of switched systems is presented using multiple Lyapunov functions. Then, for a switched system with uncertain parameters, a design method is proposed to design adaptive controllers for subsystems and a switching law. Asymptotical stability is achieved even though all the subsystems are unstabilizable, which covers the classical adaptive control of non-switched systems as a special case. The proposed theory and method are validated by an example.