Robust satisfaction of nonlinear performance constraints using barrier-based model predictive control

Efficient control of disturbed industrial systems requires methods to handle complex and nondifferentiable performance criteria given by customers directly in the design process. In laws, our works evaluates nonlinear criterion for subject additive disturbances. Model Predictive Control using barrier functions is proposed. First all, stability method proven linear case Lyapunov function invariant set theories. The presented law also improved considering robust tube-based then extended that neural networks can model when knowledge-based unknown. not proven, but has shown its efficiency different applications.