The research results described present the performance of the Generalized Predictive Control (GPC) algorithm with a changing estimator and predictor model order for a specific application. The application is a hydraulically actuated heavy duty manipulator. Hydraulically actuated robotic manipulators, used in the large resource based industries, have a complex dynamic response in which, primarily due to the hydraulic actuator subsystems, the order of the dynamic model is not initially known and can change as the manipulator is operated. A nonlinear simulation model of the manipulator system is utilized in the work and the GPC controller is implemented with a CARIMA estimator together with an on-line, gradient based estimator model order determination technique. The results given show that with proper use of the order determination technique cost function and tuning of the GPC parameters, good performance and stability can be achieved.

This paper presents the implementation of an explicit model reference adaptive control (MRAC) for position tracking of a dynamically unknown robot. An auto regressive exogenous (ARX) model is chosen to define the plant model and the control input is optimised in a H2 norm to reduce computational time and to simplify the algorithm. The theory of MRAC falls into a description of the various forms of controllers and parameter estimation techniques, therefore, applications may require very complicated solution methods depending on the selected laws. However, in this study, the proposed MRAC shows that applications may be as easy as classical control methods, such as PID, by guaranteeing the stability and achieving the convergency of the plant parameters. Despite the selected simple control model, simple optimisation method and drawbacks of the robot the experimental results show that MRAC provides an excellent position tracking compared with conventional control (PID). Many experimental implementations have been done on the robot and one of them is included in the paper.