After introducing the unified model structure and applying stochastic approximation principle , the general recursive identification algorithm of several on - line identification methods is developped 在引進統(tǒng)一的模型結(jié)構(gòu)以后,利用隨機逼近原理,提出了各種在線識別算法的一般遞推形式。
This dissertation presented two new methods of robust adaptive track control for a class of mimo strong nonlinear system with external disturbance . one method makes use of taylor approximation principle to linearize the mimo strong nonlinear system at the ideal equilibrium point , meanwhile external disturbance is considered , and then designs two on - line neural network controller respectively , which can dynamically compensate the high order items of taylor series and the control signals at ideal equilibrium point under the drive of state error between linear and nonlinear system . a linear feedback controller obtained by pole assignment and two on - line neural network act on the practical mimo high nonlinear system together , guaranteeing the whole system robust stable and tracking the specified signal ; the other method designs three on - line neural networks for this class of system 本文對于一類含有外部擾動的多輸入多輸出( mimo )強非線性系統(tǒng),提出了兩種新的魯棒自適應跟蹤控制方法,第一種利用了taylor近似的原理,在考慮了外部擾動的情況下,將mimo強非線性系統(tǒng)在理想平衡點處線性化,分別設(shè)計了兩個在線神經(jīng)網(wǎng)絡(luò)控制器,在線性和非線性系統(tǒng)之間的狀態(tài)誤差驅(qū)動下動態(tài)補償系統(tǒng)的taylor近似高階項及理想平衡點處的控制信號,滿足極點配置方法的線性反饋控制器和兩個在線神經(jīng)網(wǎng)絡(luò)聯(lián)合作用于實際的被控mimo強非線性系統(tǒng),在保證整個系統(tǒng)魯棒穩(wěn)定性的情況下,能夠跟蹤給定的指令信號;另一種方法是針對這類系統(tǒng)設(shè)計了3個在線神經(jīng)網(wǎng)絡(luò),分別實時抵消這類非線性系統(tǒng)中的非線性部分、與控制量耦合的非線性項以及外部擾動,使得受控系統(tǒng)的輸出可以完全跟蹤給定輸入?yún)⒖夹盘枴?