Neural and Adaptive Control Strategies for a Rigid Link Manipulator

The control of robotic manipulators has become important due to the development of the flexible automation. Requirements such as the high speed and high precision trajectory tracking make the modern control indispensable for versatile applications of manipulators (Middleton & Goodwin, 1998; Ortega & Spong, 1999; Popescu et al., 2008). Rigid robot systems are subjects of the research in both robotic and control fields. The reported research leads to a variety of control methods for such rigid robot systems (Ortega & Spong, 1999; Raimondi et al., 2004; Bobaşu & Popescu, 2006; Dinh et al., 2008). Conventional controllers for robotic structures are based on independent control schemes in which each joint is controlled separately by a simple servo loop. This classical control scheme (for example a PD control) is inadequate for precise trajectory tracking. The imposed performance for industrial applications requires the consideration of the complete dynamics of the manipulator. Moreover, in real-time applications, the ignoring parts of the robot dynamics or errors in the parameters of the robotic manipulator may cause the inefficiency of this classical control. An alternative solution to PD control is the computed torque technique. This classical method is in fact a nonlinear technique that takes account of the dynamic coupling between the robot links. The main disadvantage of this structure is the assumption of an exactly known dynamic model. However, the basic idea of this method remains important and it is the base of the neural and adaptive control structures (Gupta & Rao, 1994; Pham & Oh, 1994; Dumbravă & Olah, 1997; Ortega & Spong, 1999; Aoughellanet et al., 2005; Popescu et al. 2008). Industrial robotic manipulators are exposed to structured and unstructured uncertainties. Structured uncertainties are characterized by having a correct model but with parameter uncertainty (unknown loads and friction coefficients, imprecision of the manipulator link properties, etc.). Unstructured uncertainties are characterized by unmodelled dynamics. Generally speaking, two classes of strategies have been developed to maintain performance in the presence of the parameter uncertainties: robust control and adaptive control. The adaptive controllers can provide good performances in face of very large load variation. Therefore the adaptive approach is intuitively superior to robust approach in this type of application. When the dynamic model of the system is not known a priori (or is not 11