This paper is concerned with the application of quadratic optimization for motion control to feedback control of robotic systems using cerebellar model arithmetic computer (CMAC) neural networks. Explicit solutions to the Hamilton–Jacobi–Bellman (H–J–B) equation for optimal control of robotic systems are found by solving an algebraic Riccati equation. It is shown how the CMAC’s can cope with no...

A numerical method for solving the H∞ synthesis problem is presented. The problem is posed as an unconstrained, nonsmooth, nonconvex minimization problem. The optimization variables consist solely of the entries of the output feedback matrix. No additional variables, such as Lyapunov variables, need to be introduced. The optimization procedure uses a line search mechanism where the descent dire...

A numerical method for solving the H∞ synthesis problem is presented. The problem is posed as an unconstrained, nonsmooth, nonconvex minimization problem. The optimization variables consist solely of the entries of the output feedback matrix. No additional variables, such as Lyapunov variables, need to be introduced. The optimization procedure uses a line search mechanism where the descent dire...

This paper presents an approach by multiobjective optimization of the output feedback design in discrete time. The objective is to search a controller stabilizing the system with schedules charges temporal or frequential constraints. This is achieved by using the Youla parametrization based on initial corrector H2, combined with different Lyapunov functions; via LMI (Linear Matrix Inequality) o...

We establish a relationship between general constrained pseudoconvex optimization problems and globally projected dynamical systems. A corresponding novel neural network model, which is globally convergent and stable in the sense of Lyapunov, is proposed. Both theoretical and numerical approaches are considered. Numerical simulations for three constrained nonlinear optimization problems are giv...