【摘要】 讨论了一类放射非线性系统的自适应控制问题。首先对于利用方向基神经网络(DBFNN)对系统的不确定性进行建模。所得到的系统模型作为对象的数学模型用来设计控制器。首先设计了反馈线性化控制器,为了克服建模误差的影响又引入了内模控制机制。证明了只要网络的学习精度足够高,所设计的闭环系统是最终一致有界的。把所设计的控制策略用于CSTR的控制中,仿真结果表明了所设计的控制器有效性。更多还原

【Abstract】 Adaptive control using DBFNN(Direction Basis Function Neural Network)of a class of nonlinear systems is discussed.First,DBFNN is used to model the uncertainties of nonlinear systems.Then the acquired NN model is used as system model to and a feedback linearization controller is designed based the trained well network.In order to overcome the model mismatch the internal model control(IMC)mechanism is introduced in.It is proved the closed-loop system is uniformly ultimately(UUB).The proposed control strategy is application one CSTR(continuous stirred tank reactor)system.Simulations show the proposed strategy is effective.更多还原