SUMMARY

Heavy-lifting machines, such as cranes and aerial lifts, degrade their operation efficiencies due to the inherent flexible dynamics of the systems. The problem is further complicated by complex nonlinear dynamics, time-varying parameters, and lack of the full state information. This thesis investigates on developing a simple and robust control method that improves the operation of flexible systems even in the absence of an accurate system model and sensing. This goal is achieved via the combination of input shaping and model reference control. The benefits of the proposed controller design include the increased shaper robustness against plant uncertainties and parameter estimation errors, while achieving good vibration suppression and control effort reduction. The proposed controller design is tested on a single- and doublependulum payload crane. The single- and double-pendulum dynamics are explained and the state space representation of the reference model and the plant are derived. The input-shaped model reference control scheme is presented. The Lyapunov control law with asymptotic stability requirement and the input shaper design to formulate the control signal are derived. Simulations reveal that input shaped model reference control contributes to reducing the control effort magnitude for large ranges of system parameter values and the parameter variances. The robustness of the proposed controller in state tracking and oscillation suppression performances are analyzed and verified via numerical simulations and experiments.