In an unstructured environment, the arm can perform complicated tasks with rapidity, flexibility, and robustness. It is difficult to configure multiple artificial muscles similar to an arm in the compact space of a robotic arm. When muscle tension is transferred, mechanisms like tendon-sheath/tendon-pulley may be installed in a compact space to develop musculoskeletal robots that are closer to the arm. However, handling variable frictional nonlinearity and elastic cable deformation is necessary for transmission stability. In this study, the modular artificial muscle system (MAMS), including motor cable artificial muscle and tendon sheath–pulley system (TSPS), that can be installed remotely and transmit muscle tension in narrow paths, is designed. The feed-forward multi-layer neural network (FF-MNN) approach is utilized to discuss the relationship between the measurable input tension of TSPS and the unmeasurable output tension and cable elongation. Subsequently, the lightweight musculoskeletal arm (LM-Arm) is built to verify the validity of MAMS. Through trials, the experiments of MAMS after friction compensating and the LM-Arm’s end-point 3D trajectory tracking are investigated. The results show that average errors of the active and passive muscles tension are 3.87 N and 3.51 N, respectively, under conditions of larger load and higher contraction velocity. The average muscle length error of trajectory tracking is 0.00078 m (0.72%). The suggested MAMS may successfully build a musculoskeletal robot that has similar flexibility and morphology to the arm. It can also be utilized to power various pieces of machinery, such as rescue robot, invasive surgical robots, dexterous hands, and wearable exoskeletons.

Friction compensation techniques are studied for control of a flexible-link robot based on the LuGre friction model. To overcome the problem of uncertain parameters in the friction model, adaptive control schemes are used for two different types of parametric uncertainties. A novel dual-observer technique is proposed to estimate the internal state inside the friction model. A distributed-parameter dynamic model is used for the flexible arm to design the controllers. The Lyapunov stability theorem is used to guarantee the global asymptotic stability of the controllers. The performance of position tracking and link vibration attenuation is verified through experimental results. The results also confirm the effectiveness of the proposed friction compensation schemes.

A major technical challenge in controlling modular and reconfigurable robots is associated with the kinematics and dynamic model uncertainties caused by reconfiguration. In parallel, conventional model uncertainties such as uncompensated joint friction still persist. This paper presents a modular distributed control technique for modular and reconfigurable robots that can instantly adapt to robot reconfigurations. Under the proposed control method that is based on joint torque sensing, a modular and reconfigurable robot is stabilized joint by joint, and modules can be added or removed without the need to adjust control parameters of the other modules of the robot. Model uncertainties associated with link and payload masses are compensated using joint torque sensor measurement. The remaining model uncertainties, including uncompensated dynamic coupling and joint friction, are compensated by a decomposition-based robust controller. Simulation results have confirmed the effectiveness of the proposed method.