Real-time regulated sliding mode controller design of multiple manipulator space free-flying robot

Abstract

The problem of controlling Space Free-flying Robots (SFFRs), which have many degrees of freedom caused by their mechanical manipulators, is challenging because of the strong nonlinearities and their heavy computational burden for the implementation of modelbased control algorithms. In this paper, a chattering avoidance sliding mode controller is developed for SFFR as highly nonlinear-coupled systems. To fulfill stability requirements, robustness properties, and chattering elimination, a regulating routine is proposed to determine the proper positive values for the coefficient of sliding condition. To solve the run-time problem, an explicit direct relationship between the SFFR's output of actuators (force/torque) and the measurement of distances from the corresponding sliding surfaces is also assumed. To reach perfect performance, the parameters are estimated recursively using the Kalman filter as a parameter estimator. The explicit dynamics of a 14-DOF SFFR is derived using SPACEMAPLE, and the recursive prediction error method (RPEM) is used to parameterize the SFFR model. To alleviate the chattering trend, a multi-input sliding mode control law is proposed and applied to the given SFFR based on the online estimated dynamics to control its orientation and position to catch a moving target. To evaluate the new proposed algorithm in a more complicated condition, only on-off actuators are assumed for controlling the base of SFFR because it is the case in real systems. The obtained results show that the proposed regulated sliding mode controller can significantly reduce the chattering trend. Consequently, energy consumption will be substantially decreased, and running the control algorithm will be within a reasonable time duration.

Structure singular value theory based robust motion control of live maintenance robot with reconfigurable terminal function for high voltage transmission line

“…The structure of synthesis problem is shown in Figure 6(a), P is the generalized controlled object, K is the controller, D is the block diagonal matrix which contains a variety of structure perturbations and uncertainty, y is the system output, and z and w are input and output of uncertain block, respectively. d is the disturbance input, e is the error, P and K constitute the closed-loop transfer function M, the structured singular value describes the robust stability margin of the feedback system under uncertainty structure perturbation, and m(M) is defined as equation (17). The smaller the (M), the greater the uncertainty of the system.…”

Adaptive trajectory tracking control of a free-flying space robot subject to input nonlinearities

“…[15][16][17], several adaptive tracking controllers were proposed for a free-floating space robot by using passivity-based adaptation, prediction error-based adaptation, and adaptive inverse dynamics, respectively. Khaloozadeh and Homaeinejad [18] designed a real-time regulated sliding mode controller for a free-flying space robot equipped with multiple manipulators. Pazelli et al [19] investigated the applications of various nonlinear H ∞ control methods to a free-floating space robot with experimental verification.…”

Robust motion control of live-line maintenance robot mechanical arm for high-voltage transmission line based on H∞ theory

“…Through selecting reasonable controller parameters, it may eliminate some non-linear terms, to avoid the singular value problems in controller design using the traditional backstepping method. Considering trajectory tracking control, Khaloozadeh and Homaeinejad (2014) proposed a variable structure compensation controller based on bounded uncertainties and equivalent control law, which gave a real-time solution to the tracking problem, Bouakrif and Zasadzinski (2016) studied trajectory tracking control of perturbed robot manipulators using the iterative learning method and the effectiveness of the proposed method was verified mainly by simulation analysis. Galeani et al (2012), Huang and Chiang (2015), Kayacan et al (2015) and Lai et al (2016), proposed different manipulator robust tracking control methods on different types of robots under different uncertain factors.…”