Adaptive Neural Network Control of MIMO Nonlinear Systems With Discontinuous Output Constraints

As an important tool for handling output constraints of Multi-Input Multi-Output (MIMO) systems, the Barrier Lyapunov Function (BLF) method has achieved remarkable progress in the field of nonlinear control in recent years, but this approach has substantial difficulty in directly dealing with MIMO nonlinear systems with discontinuous constraints. To address the discontinuous output constraint problem of MIMO systems, this paper presents a method capable of dealing with MIMO systems that contain unknown functions and discontinuous output constraints. Firstly, by making use of the link function, the discontinuous constraint function is transformed into a smooth and continuous constraint function. Secondly, through the application of some transformation functions, the issue of discontinuous output constraints is converted into the stabilization problem of the barrier function. Then, the Radial Basis Function Neural Network (RBFNN) is employed to tackle the system unknown terms. Utilizing the backstepping technique, an adaptive neural network control (ANNC) scheme with discontinuous output constraints is constructed. Finally, by selecting appropriate design parameters, the system output can be made to track the reference signal and can always remain within the constraint conditions. The results of a simulation example demonstrate the effectiveness of the proposed method.