Parametric uncertainty handling of under-actuated nonlinear systems using an online optimal input-output feedback linearization controller

• Published in: Systems science & control engineering Vol. 9; no. 1; pp. 209 - 218
• Main Authors: Mahmoodabadi, M. J., Andalib Sahnehsaraei, M.
• Format: Journal Article
• Language: English
• Published: Macclesfield Taylor & Francis 01.01.2021; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Approximation; cart-pole nonlinear system; Control systems design; Control theory; Controllers; Design parameters; Feedback linearization; Genetic algorithms; input-output feedback linearization; multi-crossover; Nonlinear systems; Nonlinearity; Online optimal control; Open access publishing; Optimal control; Optimization; Output feedback; parametric uncertainty; Systems science; Uncertainty
• ISSN: 2164-2583; 2164-2583
• DOI: 10.1080/21642583.2021.1891993

This research introduces a new online optimal control based on the input-output feedback linearization and a multi-crossover genetic algorithm for under-actuated nonlinear systems having parametric uncertainties. At first, the input-output feedback linearization method is successfully implemented to derive the control law for a two degrees of freedom cart-pole nonlinear system. Then, the regarded optimization algorithm is applied to find the design parameters of the controller for different values of the uncertain variables. Next, an approximation function is suggested to calculate the optimum gains of the controller in the presence of the uncertainties in the system parameters. The simulation results are illustrated to prove the effectiveness and adeptness of the introduced scenario to overcome some common issues in the actual systems, i.e. under-actuating nonlinearities and uncertainties.