In various fields, planar 2R underactuated robot have garnered significant attention due to their numerous applications. To guarantee the stable control of these robots, a simple control strategy is presented in this paper, and we utilize the intelligent optimization algorithm to enhance the controller parameters. Initially, a comprehensive dynamic model is developed for the robot with its control properties described. Subsequently, we design a PD controller to control the movement of the planar 2R underactuated robot. Utilizing the differential evolution algorithm (DEA) with the considering the underactuated constraint to enhance the PD controller parameters to get the best control effect, which make all links reach the goal state. The findings from the simulation demonstrate the efficacy of the approach, and the designed strategy shows higher stability and convergence rate, highlighting its important contribution in the field of underactuated robots.

Planar underactuated manipulators are widely used in aerospace and deep-sea exploration. Due to their passive joints without actuation, it is difficult to perform trajectory planning and direct control. In this paper, an angular velocity trajectory planning method is proposed, aiming to calculate the velocity trajectory by using a superposition of basis functions and transforming the planning problem into an optimization problem by using a metaheuristic algorithm. According to the underactuated constraint relationship, the planning trajectory of the passive joint is obtained, which ensures that the passive joint can be indirectly controlled to reach the target angle while tracking the trajectory of the active joint. For the tracking control problem, a sliding mode controller with an exponential convergence rate is used to track the trajectory. Lastly, three sets of simulations are run to demonstrate the efficacy of the suggested approach.

In various fields, planar 2R underactuated robots have garnered significant attention due to their numerous applications. To guarantee the stable control of these robots, a simple control strategy is presented in this paper, and we utilize the intelligent optimization algorithm to enhance the controller parameters. Initially, a comprehensive dynamic model is developed for the robot with its control properties described. Subsequently, we design a PD controller to control the movement of the planar 2R underactuated robot. The differential evolution algorithm (DEA) is used to optimize the parameters of the PD controller to obtain the best control effect and make each link reach the target state. The findings from the simulation demonstrate the efficacy of the approach, and the designed strategy shows a higher stability and convergence rate, highlighting its important contribution to the field of underactuated robots.

Planar underactuated robots are mainly applied in the microgravity field, such as deep sea and deep space. The system modeling and stability control of planar underactuated robots are prerequisites to ensure successful task completion. The planar prismatic–prismatic–rotational (PPR) underactuated robot is one type of planar underactuated robot structure. In this paper, the dynamic system model of planar PPR underactuated robots is built, and the switching-control strategy is designed. In the first phase, an improved PD controller based on the linkage coupling of the PPR model is designed to adjust the state quantities of the three linkages and stabilize the first two linkages to the target state. This controller has certain robustness and rapidity. In the second phase, the PPR model is downgraded to the PR model, an open-loop iterative controller is designed, and the third underactuated link is stabilized to the target state through oscillation convergence. Finally, the effectiveness and applicability of the proposed strategy were verified through the comparison of setting torque interference and the simulation of the initial velocity of the link.