Barrier Lyapunov function-based adaptive control for uncertain linear motor systems

In this work, the tracking problem for permanent magnetic synchronous linear motor systems is studied. We develop a novel barrier Lyapunov function-based adaptive control scheme for linear motor systems with system constraints. The filtering-error is constrained by using a new type of BLF so as to simplify design, which is different from the existing results. The time-varying boundary layer technique is introduced to reduce the difficulty of choosing the barrier parameter. Also, a neural network is adopted for dealing with nonparametric uncertainties. In the end, a simulation example is presented to demonstrate the effectiveness of our barrier adaptive control algorithm against traditional barrier-free adaptive control algorithm.