A novel controller for nonlinear uncertain systems using a combination of SDRE and function approximation technique: Regulation and tracking of flexible-joint manipulators. Issue 10 (July 2021)

• Record Type: Journal Article
• Authors: Nasiri, Neda; Fakharian, Ahmad; Menhaj, Mohammad Bagher
• Is Part Of: Journal of the Franklin Institute. Volume 358:Issue 10(2021)
• Page Start: 5185
• Page End: 5212
• Publication Date: 2021-07
• Subjects: Robust adaptive control -- Optimal control -- State Dependent Ricatti Equation (SDRE) -- Function Approximation Technique (FAT) -- Flexible joint manipulators; Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2021.04.037
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: This paper presents a novel combined State Dependent Riccati Equation (SDRE) / Function Approximation Technique (FAT)-based control design for nonlinear uncertain systems. The SDRE is employed to construct an optimal controller and the function approximation technique is utilized to estimate time-varying disturbances and uncertainties. Moreover, a robust term in the proposed control law compensates for the truncation error. The closed-loop stability and boundedness of the tracking error and FAT weights approximation error are proved in the sense of Lyapunov, with consideration of truncation error. Due to the great importance of the adequate performance of transient response from practical point of view, performance evaluation has been accomplished. The proposed scheme is computationally simple due to utilizing the FAT to represent uncertainties and disturbances as a function of time. Compared with the SDRE based SMC, the proposed controller is superior in terms of capability to track a fast and highly complicated trajectory and no need to determine time-varying disturbances and uncertainties bounds. The case study is a Selective Compliant Articulated Robot for Assembly (SCARA) flexible joint manipulator as a representative of highly nonlinear, coupled, large robotic systems. Simulation results easily verify the effectiveness and superiority of the proposed controller.