Experimental Application of Robust and Converse Dynamic Control for Rotary Flexible Joint Manipulator System

Abstract

Performance evaluation of trajectory tracking for a rotary flexible joint system is demonstrated in this paper. The robust and converse dynamic (RCD) technique is proposed and implemented for this evaluation. This control methodology is of the left inversion type, i.e., the control inputs are obtained by means of plant output error feedback. RCD control encompasses the baseline inverse (BI) control and sliding mode control-based discontinuous control element. The baseline inverse controller enforces the prescribed servo (virtual) constraints that represent the control objectives. The control objectives of the baseline inverse controller are enclosed in the form of servo (virtual) constraints which are inverted using Moore–Penrose Generalized Inverse (MPGI) to solve for the baseline control law. To boost the robust attributes against parametric uncertainties and disturbances, a discontinuous control function is augmented with baseline controller such that semiglobal practical stability is guaranteed in the sense of Lyapunov. To exhibit the effectiveness of RCD control in terms of tracking performance, computer simulations are conducted in Simulink/Matlab environment. Furthermore, the practical implementation is also investigated through a real-time experiment on Quanser’s rotary flexible joint manipulator system. The experimental results obtained by RCD are compared to the conventional sliding mode and fractional-order control techniques.

Barrier Function-Based Integral Sliding Mode Controller Design for a Single-Link Rotary Flexible Joint Robot

“…Nevertheless, its impact on the tracking position of the tip of the FJR system is not substantial. Finally, a comparative study is conducted between the proposed method and other controllers with similar structures as in multiple researches in the literature [17, [20][21][22]27]. Clearly, it is shown from the zoomed version of (α) and (e 1 ) in Figs.…”

“…To minimize vibrations and to optimize the performance of the position tracking of the FJR system, Dharavath and Ohri [20] designed two conatrollers, namely SFC with pole-placement and Linear Quadratic Regulator (LQR) by utilizing the Riccati equation. Robust and converse dynamic (RCD) control was proposed by Iskanderani and Mehedi [21] to solve the control problem of an FJR under parametric uncertainty and perform control trajectory tracking. Because SMC has many benefits, including high accuracy and insensitivity to the system perturbations, guaranteed stability, and robustness to system uncertainties and disturbances, various SMC schemes have been applied to the FJR, such as cascaded extended state observer (CESO) [22].…”

“…In a comparative study against controllers with similar structures [17, [20][21][22]27], the proposed ISMCbased control method outperformed other, producing smoother deflection angles (α) and reduced tracking errors (e 1 ). This superior performance was attributed to the elimination of chattering in the control signal without compromising tracking precision, ensuring better end-effector trajectory adherence.…”

Intelligent Dynamic Inversion Controller Design for Ball and Beam System

“…The coefficients c 1 , c 2 , and c 3 are selected such that the characteristic polynomials are given by Eqs. (13), and ( 14) is Hurwitz, such that ζ θ l → 0 and ζ ω l → 0 as t → ∞ [19]. Computing and substituting the time derivatives in the constraint dynamics given by Eqs.…”

“…Recently a control law focused on the inversion principle is proposed known as Generalized Dynamic Inversion (GDI), which addresses the limitations of the classical NDI approach [19]. The control objectives of GDI are defined in the form of differential state constraints which are calculated along the solution paths of the system dynamics.…”