Robust Multi-Performances Control for Four-Link Manipulator Arm

Robust control of the four-link manipulator arm (FLMA) is an important subject for many industrial applications such as COVID-19 prevention robotics, lower limb rehabilitation robotics and underwater robotics. This study uses the feedback linearized approach to stabilize the complex nonlinear FLMA without applying nonlinear approximator that includes the fuzzy approach and the neural network optimal approach. The article proposes a new approach based on the “first” derived nonlinear convergence rate formula of FLMA to control the highly nonlinear dynamics. The linear quadratic regulator (LQR) method is often applied in the balance controlling space of the underactuated manipulator. This proposed approach takes the place of LQR approach without the necessary trial and error operations. The implications of proposed approach are “globally” valid, whereas the Jacobian linearized approach is “locally” valid. In addition, the main innovation of the proposed method is to perform “simultaneously” additional performances including the almost disturbance decoupling performance that takes the place of the traditional posture-energy approach and avoids some torque chattering behaviour in the swing-up space, and globally exponential stable performance without solving the Hamilton-Jacobin equation. Comparative examples show that the proposed controller is superior to the singular perturbation and the fuzzy approaches.