Robust stabilisation of rotary inverted pendulum using intelligently optimised nonlinear self-adaptive dual fractional-order PD controllers. (English) Zbl 1483.93513
Summary: This article presents an intelligently optimised self-tuning fractional-order control scheme to improve the attitude-stabilisation of an inverted pendulum. Primarily, the scheme employs two fractional-order proportional-derivative (FPD) controllers acting concurrently on the system to minimise the deviations in its state-trajectories. Wherein, one FPD controller compensates the variations in pendulum-angle and its fractional-order derivative to vertically balance the pendulum, where as the other FPD controller acts as a position controller and regulates the variations in arm-angle and its fractional-order derivative. The integration of fractional calculus with conventional PD controllers optimises the reference-tracking performance of the control scheme by increasing its degrees-of-freedom and design flexibility. In order to further improve the system’s immunity against exogenous disturbances, the PD gains of each controller are dynamically adjusted after each sampling interval using piecewise nonlinear functions of their respective state-variations. The hyper-parameters of the nonlinear gain-adjustment functions as well as the fractional-number power of the derivative-operator of each controller are selected via particle swarm optimisation (PSO) algorithm. The proposed adaptive control scheme is tested on the QNET rotary inverted pendulum setup via ‘hardware-in-the-loop’ experiments. The optimality and robustness of the proposed control scheme are validated by comparing its performance with PSO-based fixed-gain dual-PD and dual-FPD control schemes.
MSC:
| 93D21 | Adaptive or robust stabilization |
| 93C10 | Nonlinear systems in control theory |
| 26A33 | Fractional derivatives and integrals |
| 90C59 | Approximation methods and heuristics in mathematical programming |
Keywords:
rotary inverted pendulum; fractional calculus; proportional-derivative control; self-tuning control; particle swarm optimisationReferences:
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