Coupled Dynamic Modeling and Control of Aerial Continuum Manipulation Systems

Abstract

Aerial continuum manipulation systems (ACMSs) were newly introduced by integrating a continuum robot (CR) into an aerial vehicle to address a few issues of conventional aerial manipulation systems such as safety, dexterity, flexibility and compatibility with objects. Despite the earlier work on decoupled dynamic modeling of ACMSs, their coupled dynamic modeling still remains intact. Nonlinearity and complexity of CR modeling make it difficult to design a coupled ACMS model suitable for practical applications. This paper presents a coupled dynamic modeling for ACMSs based on the Euler–Lagrange formulation to deal with CR and the aerial vehicle as a unified system. For this purpose, a general vertical take-off and landing vehicle equipped with a tendon-driven continuum arm is considered to increase the dexterity and compliance of interactions with the environment. The presented model is independent of the motor’s configuration and tilt angles and can be applied to model any under/fully actuated ACMS. The modeling approach is complemented with a Lyapunov-wise stable adaptive sliding mode control technique to demonstrate the validity of the proposed method for such a complex system. Simulation results in free flight motion scenarios are reported to verify the effectiveness of the proposed modeling and control techniques.

Distilled neural state-dependent Riccati equation feedback controller for dynamic control of a cable-driven continuum robot

“…Simulation was used to assess the efficiency of the proposed neural-based SDRE control approach for tendon-driven CRs with the exact specification as Samadikhoshkho et al 13 Several networks were trained and compared to determine the best neural controller structure for this CR.…”

“…The relation between the control signal and tendons tensions F ten = [ F 1 F 2 F 3 false] T is expressed as equation (19) (as stated in Samadikhoshkho et al 13 )…”

“…Dynamic modeling of a tendon-driven CR, shown in Figure 1, is provided in this section by adapting from Samadikhoshkho et al 13 and focusing exclusively on the CR described in that work.…”

“…12 The Lagrangian technique is used for dynamic modeling of the CR in this work, because of its computational efficiency and ease of real-time implementation. 13 However, the Lagrangian approach described in He et al 9 lacked a general formulation and relied predominantly on Taylor series expansion and several curve fittings to simplify the CR equations. For these reasons, an appropriate technique (adapted from Samadikhoshkho et al 13 ) is required to handle CR modeling issues in the initial stage of designing the controller.…”

Robust control and recursive modelling approach for hexarotor manipulator

“…In this part, we compare the outcomes of our control with those of the approach established in the publication, 30 which is based on classical SMC and uncertainty estimates and computes using the adaptation law by updating the dynamic model of an aerial manipulator system.…”