共查询到20条相似文献,搜索用时 15 毫秒
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针对轮式移动机器人参数摄动和内外部扰动等问题,提出一种新型的基于自适应扩张状态观测器的滑模控制算法。采用自适应虚拟速度控制器估计系统未知参数,滑模控制器抑制参数摄动和内外部扰动,非线性扩张状态观测器观测系统扰动并减小控制输入的抖振,实现了轨迹跟踪误差的快速收敛。利用Lyapunov稳定性理论证明了控制算法的稳定收敛性。将所提算法与传统自适应反演滑模算法进行对比,对比结果表明了所提算法的有效性和鲁棒性。 相似文献
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针对连续平压热压机中执行机构存在的非线性问题、参数不确定和因板坯粘弹性对执行元件反作用力引起的外界干扰问题,采用了自适应滑模反步控制方法。对执行机构建立数学模型后,结合自适应滑模控制和反步法,有效降低了参数不确定、非线性和外界干扰对系统的影响,大大改善了控制系统的准确性。利用控制系统非线性数学模型的等价变换并选择合适的Lyapunov函数和中间虚拟量,完成了对系统稳定性的证明。仿真研究结果表明,所设计的自适应滑模反步控制器输出平稳,达到了快速准确的跟踪目的,同时对参数的变化具有较强鲁棒性。 相似文献
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对于不确定的机械手系统,提出一种鲁棒自适应控制方法,用自适应控制来估计系统的未知参数,用终端滑模控制来减少不确定因素的影响,为了避免因干扰的存在使自适应的估计参数发生漂移,引入死区自适应控制.仿真表明,滑模控制不仅抑制了误差,而且消除了死区自适应算法的局限性,该算法在取得较好控制效果的同时,具有很强的鲁棒性. 相似文献
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针对移动机器人滑模跟踪控制器参数较多,采用人为设定控制器参数工作量大,控制精度低等问题,在对一类移动机器人滑模跟踪控制器控制性能分析的基础上,根据移动机器人跟踪控制过程的非线性特性,为控制移动机器人位置精度,提出了一种基于蚁群方法的非线性参数滑模跟踪控制器.在无先验知识的前提下,根据系统的输入输出数据对,利用蚁群算法自动提取控制器参数的模糊规则基.为克服传统蚁群算法容易陷入局部收敛的缺点,对蚁群算法进行了改进,设计出一种自适应调节因子,随着蚁群的收敛过程的进行,提高状态转移的随机性,减少局部收敛的可能.经实验验证了方法的有效性. 相似文献
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含有非线性不确定参数的电液系统滑模自适应控制 总被引:3,自引:1,他引:2
针对含有非线性不确定参数的电液控制系统, 提出了一种滑模自适应控制方法. 该控制方法主要是为了解决由于初始控制容积的不确定性而引起的, 非线性不确定参数自适应律设计的难题. 其主要特点为, 通过定义一个新型的特Lyapunov 函数, 进而构建系统的自适应控制器及参数自适应律, 并结合滑模控制方法及一种简单的鲁棒设计方法, 给出整个电液系统的滑模自适应控制器, 及所有不确定参数的自适应律. 试验结果表明, 采用该控制方法能够取得良好的性能, 尤其可以补偿非线性不确定参数对系统的影响. 相似文献
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针对复杂海况下船舶航向控制中的模型非线性、参数不确定和海浪扰动问题,提出了一种基于反步法的非线性自适应输出反馈控制算法.首先基于无源理论设计了一种状态观测器以实现海浪滤波和状态估计,这种观测器无需海浪扰动的方差信息从而减少了观测器参数数量.然后假定系统模型参数未知,基于反步法给出了非线性控制律和参数自适应律.利用Lyapunov理论证明了这种自适应输出反馈控制系统的稳定性.仿真结果表明本文所提控制器具有较好的控制性能,对不确定性模型参数具有良好的自适应性. 相似文献
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本文针对考虑不确定性的飞行模拟转台伺服系统,提出了一种基于非线性干扰观测器的反步全局滑模补偿控制方法。该方法采用反步控制方法设计转速期望虚拟控制,然后利用非线性干扰观测器观测系统不确定干扰,进而对引入非线性干扰观测器的系统设计自适应全局滑模控制器,实现了飞行模拟转台伺服系统期望转角信号的鲁棒跟踪控制,仿真结果表明,该方法控制效果良好,具有很好的工程应用价值。 相似文献
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M. DE LA SEN 《International journal of systems science》2013,44(11):1609-1634
A technique to improve the adaptation transient in discrete-time model reference adaptive control (MRAC) systems is given on the basis of applying classical optimization techniques to an equivalent near-linear system to the whole adaptive scheme. The resulting optimization is finally translated into modifications of one of the parameters entering the adaptation algorithm. This permits computation of a posteriori values of the adaptive controller parameters before generation of the input to the plant. 相似文献
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考虑粒子群优化算法在不确定系统的自适应控制中的应用。神经网络在不确定系统的自适应控制中起着重要作用。但传统的梯度下降法训练神经网络时收敛速度慢,容易陷入局部极小,且对网络的初始权值等参数极为敏感。为了克服这些缺点,提出了一种基于粒子群算法优化的RBF神经网络整定PID的控制策略。首先,根据粒子群算法的基本原理提出了优化得到RBF神经网络输出权、节点中心和节点基宽参数的初值的算法。其次,再利用梯度下降法对控制器参数进一步调节。将传统的神经网络控制与基于粒子群优化的神经网络控制进行了对比,结果表明,后者有更好逼近精度。以PID控制器参数整定为例,对一类非线性控制系统进行了仿真。仿真结果表明基于粒子群优化的神经网络控制具有较强的鲁棒性和自适应能力。 相似文献
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The presence of parametric uncertainties decreases the performance in controlling dynamic systems such as the DC motor. In this work, an adaptive control strategy is proposed to deal with parametric uncertainties in the speed regulation task of the DC motor. This adaptive strategy is based on a bio-inspired optimization approach, where an optimization problem is stated and solved online by using a modification of the differential evolution optimizer. This modification includes a mechanism that promotes the exploration in the early generations and takes advantage of the exploitation power of the DE/best class in the last generations of the algorithm to find suitable optimal control parameters to control the DC motor speed efficiently. Comparative statistical analysis with other bio-inspired adaptive strategies and with linear, adaptive and robust controllers shows the effectiveness of the proposed bio-inspired adaptive control approach both in simulation and experimentation. 相似文献
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G. K. KEL'MANS A. S. POZNYAK A. V. CHERNITSER 《International journal of systems science》2013,44(2):235-254
The paper is concerned with adaptive control of a dynamic process. It is required to choose the best sequence of control signals with the process parameters unknown. The control algorithms are developed by the ‘ decomposition principle ” and local optimization. The properties of an adaptive closed-loop system aro established ; its performance is estimated and parameter identifiability in tho control process is established. 相似文献
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A Fuzzy Adaptive Differential Evolution Algorithm 总被引:8,自引:5,他引:8
J. Liu J. Lampinen 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2005,9(6):448-462
The differential evolution algorithm is a floating-point encoded evolutionary algorithm for global optimization over continuous spaces. The algorithm has so far used empirically chosen values for its search parameters that are kept fixed through an optimization process. The objective of this paper is to introduce a new version of the Differential Evolution algorithm with adaptive control parameters – the fuzzy adaptive differential evolution algorithm, which uses fuzzy logic controllers to adapt the search parameters for the mutation operation and crossover operation. The control inputs incorporate the relative objective function values and individuals of the successive generations. The emphasis of this paper is analysis of the dynamics and behavior of the algorithm. Experimental results, provided by the proposed algorithm for a set of standard test functions, outperformed those of the standard differential evolution algorithm for optimization problems with higher dimensionality. 相似文献
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As a kind of manufacturing system with a flexible grinder, the material removal of a robot belt grinding system is related to a variety of factors, such as workpiece shape, contact force, robot velocity, and belt wear. Some factors of the grinding process are time-variant. Therefore, it is a challenge to control grinding removal precisely for free-formed surfaces. To develop a high-quality robot grinding system, an off-line planning method for the control parameters of the grinding robot based on an adaptive modeling method is proposed in this paper. First, we built an adaptive model based on statistic machine learning. By transferring the old samples into the new samples space formed by the in-situ measurement data, the adaptive model can track the dynamic working conditions more rapidly. Based on the adaptive model the robot control parameters are calculated using the cooperative particle swarm optimization in this paper. The optimization method aims to smoothen the trajectories of the control parameters of the robot and shorten the response time in the transition process. The results of the blade grinding experiments demonstrate that this approach can control the material removal of the grinding system effectively. 相似文献
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针对自适应无限冲激响应(infinite impulse response,IIR)数字滤波器的设计实质上是一个多参数优化问题,提出了一种用粒子群优化算法(particle swarm optimization,PSO)设计IIR数字滤波器的方法.将滤波器的设计问题转化为滤波器参数的优化问题,利用粒子群优化算法对整个参数空间进行高效并行搜索以获得参数的最优化,基于多个典型系统的随机数值仿真以及与最小二乘方法的比较研究,验证了该方法的有效性、全局性和对初值的鲁棒性. 相似文献
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In this paper, an L1 adaptive output‐feedback controller is developed for multivariable nonlinear systems subject to constraints using online optimization. In the L1 adaptive architecture, an adaptive law will update the adaptive parameters that represent the nonlinear uncertainties such that the estimation error between the predicted state and the real state is driven to zero at every integration time step. Of course, neglection of the unknowns for solving the error dynamic equations will introduce an estimation error in the adaptive parameters. The magnitude of this error can be lessened by choosing a proper sampling time step. A control law is designed to compensate the nonlinear uncertainties and deliver a good tracking performance with guaranteed robustness. Model predictive control is introduced to solve a receding horizon optimization problem with various constraints maintained. Numerical examples are given to illustrate the design procedures, and the simulation results demonstrate the availability and feasibility of the developed framework. 相似文献