Harmonic drives, commonly utilised in space manipulators for high transmission ratios and efficient energy transfer, introduce joint flexibility that leads to issues such as hysteresis, vibration and nonlinear coupling. An adaptive control approach with friction compensation is proposed for flexible joint space manipulators to mitigate joint flexibility and address nonlinear friction issues. According to singular perturbation decoupling, the dynamic system of space manipulators is decomposed into two second-order subsystems with distinct time scales. By introducing the concept of an integral manifold, the two subsystems of fast and slow are refactored into two distinct tracking systems. For the slow subsystem, an adaptive control scheme with friction compensator is designed, employing an enhanced linear parameterisation expression. The friction compensator is designed with a decomposition-based compensation control scheme. Meanwhile, a proportional-derivative (PD) controller is provided for the refactored fast subsystem, which avoids incorporating second-order derivative terms of elastic torques into the proposed controller, thereby enhancing computational efficiency and engineering practicability. Approximate differential filters are utilised to estimate the joint velocities of the links and the differential terms of joint elastic torques, which reduces noise disturbances and improves the robustness of the control system. Notably, a modified compensation scheme is proposed to solve the problem that the conventional singular perturbation method is limited to be utilised in space manipulators with weak joint flexibility. Furthermore, the stability proof of the entire system is conducted according to the Lyapunov stability theorem. Ultimately, simulations and analyses demonstrate that the provided strategy is effective.

In this paper, an online adaptive super twisting sliding mode controller is proposed for a non-linear system. The adaptive controller has been designed in order to deal with the unknown dynamic uncertainties and give the best trajectory tracking. The adaptation is based on an optimal Particle Swarm Optimization (PSO) algorithm whose goal is online tuning the parameters through focusing on decreasing the objective function. The novelty of this study is online handling parameters setting in the conventional super twisting algorithm, bypass heavy offline calculation, and also avoid the instability and abrupt changing of the controller’s parameters for better actuators lifetime. This novel approach has been applied on an upper limb exoskeleton robot for arm rehabilitation. Despite the changes of the dynamic model of the system which defers from one patient to another due to the direct interactions between the wearer and the exoskeleton, this control technique preserves its robustness with respect to bounded external disturbances. The effectiveness of the proposed adaptive controller has been proved in simulation and then in real-time experiment with two human subjects. A comparison between the proposed approach and classic super twisting algorithm has been conducted. The obtained results show the performance and efficiency of the proposed controller.

In this paper, different adaptive control algorithms will be experimentally tested on a two axis SCARA type direct drive robot arm, and the performance of these algorithms will be compared. Being a direct drive system, the nonlinear effects, arising from the dynamics of the manipulator under high velocities, are directly reflected in the control of the manipulator. This makes the manipulator a more efficient test bed for testing the efficiency of the proposed adaptive schemes. In the experiments, we used fast trajectories rather than slow ones to observe how the proposed controllers compensate the dynamic nonlinear effects of manipulator dynamics. We will test some known adaptive control algorithms given in the literature along with our proposed adaptive control scheme which makes use of multiple models.