In this paper, a cart-type inverted pendulum is controlled using combining of two methods of approximate feedback linearization and sliding mode control. Both position of the cart and angular position of the pendulum are stabilized. Obtained control gains are optimized by a hybrid algorithm based on the particle swarm optimization and genetic algorithm.
In this paper, a robust adaptive control method for a class of fourth-order systems is proposed. The used structure for this controller is a combination of decoupled sliding mode approach and feedback linearization technique. The decoupled sliding mode is applied to guarantee the sliding condition, and by applying the feedback linearization method, a linear control law with adaptive coefficients is employed. The final control effort is defined as the weighting summation of the decoupled sliding mode and feedback linearization controllers. Then, the controller coefficients are optimized using the multi-objective genetic algorithm. Finally, to show effectiveness of the proposed approach, it is applied to handle the cart-pole, ball-beam, and ball-wheel systems and the results are compared with those reported in the literature.
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.
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