In control theory, traditional methods are basically relied on the mathematical model of a plant to design suitable control schemes. First, the model has to be successfully developed which reflects precisely the system dynamic behaviors within certain operating conditions. Theoretically, based on the true assumed plant model, the controller design and system stability analysis can be carried out. On the other hand, since the last few decades an alternative control strategy, which only utilizes the available input-output information from the closed-loop system to analyze and design controllers, has been proposed. This novel data-driven or model-free control method can reduce efforts spending on the system modeling tasks. In addition, by using directly the updated system data the unknown time-varying parameters of the given system/process and design controller are estimated and corrected continuously at each operating point. These updated parameters are necessary to determine the required control input energy.

In this thesis, a recently developed data-driven control method called model-free adaptive control (MFAC) will be intensively investigated to acquire further control performance improvements by applying the method to the field of vibration reduction. The main principle of MFAC is replacement of the unknown complicated dynamical characteristics of the initial (nonlinear) system by an equivalent linearized model based on the on-line updated system input-output data. Hence, the assumed system model is built up at each discrete-time instant during the system operation. To design control, the identified parameters from the local dynamic model should be utilized explicitly. This research will develop different modified/improved MFAC strategies which can be effectively applied to a class of complex mechanical systems for vibration reduction purpose.
 
Traditional MFAC often uses conventional projection algorithm to estimate and update the unknown system parameters of the linearized data model. To improve on-line estimation accuracy, in this thesis, recursive least-squares algorithm (RLSA) will be applied. Furthermore, the tracking control performance of MFAC can be improved by minimizing not only the current output error amplitudes, but also the error variations within a fixed-length of time window from the past. As a result, a modified control input law will be generated. 

In addition, compact-form dynamic linearization (CFDL) has been considered in MFAC design as a simplified technique for system linearization. In this work, the CFDL concept will be applied not only to the unknown (nonlinear) plant but also to an assumed nonlinear controller. Subsequently, a linearized controller structure is derived, in which a matrix of unknown controller parameters needs to be estimated. By proposing a modified objective function of the controller parameter matrix, an improved estimation algorithm for updating these parameters on-line is introduced. Moreover, based on the fundamentals of MFAC and generalized model predictive control, modified model-free adaptive predictive control programs are proposed, in which RLSA and its modification can be implemented for parameter estimation instead of using traditional projection algorithm. 

Another dynamic linearization technique called partial-form dynamic linearization (PFDL) is implemented to the MFAC design for multivariable systems. In this contribution, an improved PFDL-based data-driven control strategy will be developed. A partial-form data model of the original system is constructed locally which contains a set of unknown parameter matrices namely pseudo-jacobian matrix. These matrices are recursively updated by using the measured system input-output signals. In addition to known approaches, in this study, on-line parameter estimation based on the recursive least-squares method is applied to the PFDL model. For control realization, a modified PFDL-based control input equation is proposed by considering minimization of the tracking error differences. 

To verify control effectiveness, the proposed controllers will be executed to reduce the free-vibrations of an elastic ship-mounted crane due to the non-zero initial excitation of the payload. The crane is represented as a typical complex and flexible system, in which the in-plane oscillations of the elastic boom and the payload must be reduced or eliminated to increase the crane safety operation. Simulation results demonstrate that, the angular displacements of the output signals as well as the payload are reduced significantly within a short length of time by using the modified model-free controllers. Additionally, the proposed MFAC programs work effectively and better control results are obtained when varying several design controller parameters in comparison with conventional methods.