可以证明多模型自适应控制器能够保证闭环系统是输入输出有界稳定的。
The closed-loop system is proved to be bounded-input and bounded-output stable when the multiple model adaptive controller is used.
针对混沌系统的控制问题，提出了一种基于神经气网络的模糊多模型自适应控制方法。
Aiming at chaotic system, this paper proposes a multi-model adaptive control strategy based on a neural-gas network with fuzzy logic.
采用了具有积分性质的切换指标函数作为切换法则和最小方差的控制方法构成了多模型自适应控制器。
A switching function with integral property and minimum variance algorithm are used to set up the multiple model adaptive controller.
给定一个指标切换函数，基于多个自适应模型控制器和给定的指标切换函数构成多模型自适应控制器。
Based on the multiple controllers, a multiple model adaptive controller can be formed by using an index switching function.
同时证明，对于已知有界参考输入，多模型自适应控制可以保证闭环系统输入输出稳定，输出渐进跟踪设定值。
To the known bounded reference input trajectory, it can be proved the closed-loop is BIBO stable, and the output will track the reference input asymptotically.
针对非线性时变系统在自适应控制过程中瞬态响应差的问题，提出了一种基于多模型自适应控制的模型切换算法。
A model switching algorithm based on multi-model adaptive control is presented to solve the problem of poor transient response in the adaptive control of nonlinear time-varying system.
给出了非线性系统多模型自适应控制算法的优化模型集建立方法，解决了多模型自适应控制模型多、计算量大的问题。
By using PWL method an optimal model set of nonlinear system multiple model adaptive control algorithm is given, which reduces the heavy computation burden in multiple model adaptive control.
针对多模型方法中子模型数量过多的问题，提出一种基于模型集在线优化方案的多模型自适应控制算法，并对其实时性进行讨论。
Focus on the question of many sub models in traditional SMM, a SMM adaptive control based on online optimization method of model set is proposed.
针对多模型控制方法中模型数目巨大，计算时间长等问题，提出了分层递阶结构多模型自适应前馈解耦控制器。
To solve such problem as too many models and long computing time, a hierarchical multiple model adaptive decoupling controller is designed.
摘要：针对多水下机器人编队问题，提出一种基于相位耦合振子模型的自适应控制算法。
Abstract: An adaptive formation control algorithm was proposed for multiple autonomous underwater vehicle systems (MAUVS) based on the coupled phase oscillators model.
针对一类不确定的离散时间非线性非最小相位动态系统，提出了一种基于神经网络和多模型的直接自适应控制方法。
A direct adaptive control approach is proposed for a class of uncertain discrete time nonlinear non-minimum phase dynamical systems.
该算法将预测控制与多模型思想结合，通过模糊自适应加权算法计算权重，采用动态矩阵控制优化控制器参数。
The weights are calculated by fuzzy adaptive weight algorithm and the controllers are optimized by dynamic matrix control algorithm.
针对多轴转台中负载转动惯量和干扰力矩的不确定性，提出了一种离散的模型参考自适应控制方法。
In view of the uncertainty in the load moment of inertia and the disturbance of test table with three or more axes, a discrete time model reference adaptive control is presented in this paper.
仿真表明， 多模型自适应内模控制较之单内模 控制和常规PID 控制，在稳定性、鲁棒性和响应速度等方面，均具有明显的优越性。
Simulation result of given plant showed that the proposed MMA-IMC design method is superior to its conventional counterpart and PID in performance of stability and robustness and response speed.
仿真表明， 多模型自适应内模控制较之单内模 控制和常规PID 控制，在稳定性、鲁棒性和响应速度等方面，均具有明显的优越性。
Simulation result of given plant showed that the proposed MMA-IMC design method is superior to its conventional counterpart and PID in performance of stability and robustness and response speed.
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