Modelling and control of flexible manipulators in 3D motion

This thesis presents investigations into modelling and control of flexible manipulators in 3D motion. A finite difference (FD) simulation environment characterising a single-link manipulator in horizontal plane motion and corresponding experimental rig is considered as test bed in this work. The dynamic model of the system is derived using the Lagrange equation and discretised using the FD method. Mathematical models are developed based on physical laws and first principles to represent the dynamics of a physical system well. The simulation algorithm is extended by deriving mathematical model of a single-link flexible manipulator in vertical plane motion with inclusion of gravity. The dynamic behaviour of the system for vertical motion is formulated. The response of the manipulator is assessed in comparison to the corresponding theoretical ones in time and the frequency domains to verify the accuracy of the model in characterizing the behaviour of the flexible manipulator system. However, for systems with complex behaviour and non-linear dynamics, modelling based on first principles is often a formidable and undesirable task, so to solve. these problems, system identification proven as an excellent tool to model such complex systems, is adopted. The objective of system identification is to find exact or approximate model of a dynamic system based on observed system input and output. Linear and non-linear models for the flexible manipulator system are developed using system identification techniques. Linear parametric models, characterising the flexible manipulator system are obtained using the potential of recursive least squares (RLS) estimation, genetic algorithms (GAs) and particle swarm optimisation (PSO) techniques, and combination of estimation and optimisation methods with GARLS. Moreover, the development of PSO with spread factor (PSOSF) and momentum factor (PSOMF) are used to develop suitable model of the flexible manipulator. Furthermore, nonlinear models using multi-layer perceptron (MLP) neural networks (NNs) are developed. These comprise combination GA with inverse NN (GAINN) and PSO with inverse NN (PSOINN). Data from an experimental flexible manipulator system is used to model the system using auto regressive moving average (ARMA) model structure with one-step-ahead prediction.