Nonlinear control systems: an introduction (2nd ed.)
Nonlinear control systems: an introduction (2nd ed.)
Nonlinear dynamical control systems
Nonlinear dynamical control systems
Algebraic criteria for global stability analysis of non-linear systems
Systems Analysis Modelling Simulation
Nonlinear control design: geometric, adaptive and robust
Nonlinear control design: geometric, adaptive and robust
Approximate state-feedback linearization using spline functions
Automatica (Journal of IFAC)
Mathematical control theory: deterministic finite dimensional systems (2nd ed.)
Mathematical control theory: deterministic finite dimensional systems (2nd ed.)
Stochastic Simulation in Physics
Stochastic Simulation in Physics
Journal of Global Optimization
Design and Analysis of Randomized Algorithms: Introduction to Design Paradigms (Texts in Theoretical Computer Science. An EATCS Series)
Stabilizing a Class of Nonlinear Systems Based on Approximate Feedback Linearization*
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Deterministic Global Optimization: Theory, Methods and (NONCONVEX OPTIMIZATION AND ITS APPLICATIONS Volume 37) (Nonconvex Optimization and Its Applications)
Stochastic and Global Optimization (Nonconvex Optimization and Its Applications)
Stochastic and Global Optimization (Nonconvex Optimization and Its Applications)
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
On the Synthesis of a Novel Nonlinear Feedback Control for Nonlinear Input-Affine Systems
CSSIM '09 Proceedings of the 2009 International Conference on Computational Intelligence, Modelling and Simulation
CONTROL'10 Proceedings of the 6th WSEAS international conference on Dynamical systems and control
Survey Approximate linearization via feedback - an overview
Automatica (Journal of IFAC)
| Hi-index | 0.00 |
In the present work, we propose a novel polynomial approach to approximate the Input-State feedback linearization control. The aim of this new method is to simplify the implementation complexity of the exact Input-State feedback linearization. Indeed, the present approach leads to an analytical control law via analytical nonlinear transformations without need to resolve a set of partial differential equations. In fact, the analytical control law, determined via the proposed work, is dependent to an arbitrary choice of some parameters. So and in order to ensure a satisfactory evolution of the control input, we resort to optimization methods to have the optimal values of parameters. A study simulation is presented to show the effectiveness of the proposed approach.