In this work, the angle tracking algorithm for a pneumatic artificial muscle-actuated mechanism is studied. The control system is subject to both multiplicative and additive actuator faults. Lyapunov synthesis is used to design controller. The uncertainties and external disturbances are dealt with according to robust adaptive strategy. The filtering error of closed-loop system may converge to the small neighour of origin even if both multiplicative and additive actuator faults happen.
KEYWORDS: Control systems, Computing systems, Numerical simulations, Complex systems, Systems modeling, Computer science, Information science, Nonlinear control, Light sources and illumination, Direct methods
An iterative learning controller is presented for nonlinearly parameterized uncertain systems with unknown control direction. By using the parameter separation technique and the signal replacement mechanism, the system equation is reconstructed. By using the Nussbaum function, the proposed control laws can guarantee that all the signals in the closed-loop are bounded and the tracking error converges to zero over the entire interval. The numerical simulation is carried out to demonstrate effectiveness of the presented learning schemes.
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