控制增益为未知函数的不确定系统预设性能反演控制

对一类控制增益为未知函数的不确定严格反馈系统的预设性能反演控制进行研究.首先,提出一种新的变参数约束方案,放宽了对初始跟踪误差已知的限制,并通过误差转化将不等 式约束的受限系统转化为非受限系统.随后,通过引入积分型Lyapunov函数,避免了因控制增益未知而引起的系统奇异问题.最后,综合应用自适应技术、径向基函数(Radial basis function,RBF)神经网络和反演控制技术完成了控制器的设计,系统中的未知函数利用RBF神经网络直接进行逼近.所设计的控制器能够满足预设性能的要求,且保证闭环系统所有的状态量有界.仿真研究证明了控制器设计方法的有效性.

Prescribed Performance Backstepping Control of Uncertain Systems with Unknown Control Gains

We investigate the prescribed performance backstepping control problem for a class of uncertain strict-feedback nonlinear systems whose control gains are unknown functions. Firstly, a novel error transformation is proposed to transform the original constrained system into an equivalent unconstrained one, which eliminates the limitation that initial error must be known. Subsequently, integral Lyapunov functions are introduced to avoid the possible controller singularity problem usually met in feedback linearization design. Finally, adaptive technique, radial basis function (RBF) neural networks and backstepping technique are combined to design the controller, and the unknown functions are approximated by RBF neural networks directly. The controller guarantees that the prescribed transient and steady state error bounds are satisfied and all state variables are bounded. The effectiveness of the proposed scheme is validated by simulation.