299 research outputs found

    Artificial Muscles for Humanoid Robots

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    Slippage detection for grasping force control of robotic hand using force sensing resistors

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    This paper presents the formulation of a nonlinear adaptive backstepping force control in grasping weight-varying objects using robotic hand driven by Pneumatic Artificial Muscle (PAM). The modelling and control problems arise from the high nonlinear PAM dynamics and the inherent hysteresis leading to a lack of robustness in the hand’s performance. The robotic finger and the PAM actuator been mathematically modelled as a nonlinear second order system based on an empirical approach. An adaptive backstepping controller has been designed for force control of the pneumatic hand. The estimator of the system uncertainty is incorporated into the proposed control law and a slip detection strategy is introduced to grasp objects with changing weights. The simulation and experimental results show that the robotic hand can maintain grasping an object and stop further slippage when its weight is increased up to 500 g by detecting the slip signal from the force sensor. The results also have proven that the adaptive backstepping controller is capable to compensate the uncertain coulomb friction force of PAM actuator with maximum hysteresis error 0.18◦

    Sliding mode control of robotics systems actuated by pneumatic muscles.

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    This dissertation is concerned with investigating robust approaches for the control of pneumatic muscle systems. Pneumatic muscle is a novel type of actuator. Besides having a high ratio of power to weight and flexible control of movement, it also exhibits many analogical behaviors to natural skeletal muscle, which makes them the ideal candidate for applications of anthropomorphic robotic systems. In this dissertation, a new phenomenological model of pneumatic muscle developed in the Human Sensory Feedback Laboratory at Wright Patterson Air Force Base is investigated. The closed loop stability of a one-link planar arm actuated by two pneumatic muscles using linear state feedback is proved. Robotic systems actuated by pneumatic muscles are time-varying and nonlinear due to load variations and uncertainties of system parameters caused by the effects of heat. Sliding mode control has the advantage that it can provide robust control performance in the presence of model uncertainties. Therefore, it is mainly utilized and further complemented with other control methods in this dissertation to design the appropriate controller to perform the tasks commanded by system operation. First, a sliding mode controller is successfully proposed to track the elbow angle with bounded error in a one-Joint limb system with pneumatic muscles in bicep/tricep configuration. Secondly, fuzzy control, which aims to dynamically adjust the sliding surface, is used along with sliding mode control. The so-called fuzzy sliding mode control method is applied to control the motion of the end-effector in a two-Joint planar arm actuated by four groups of pneumatic muscles. Through computer simulation, the fuzzy sliding mode control shows very good tracking accuracy superior to nonfuzzy sliding mode control. Finally, a two-joint planar arm actuated by four groups of pneumatic muscles operated in an assumed industrial environment is presented. Based on the model, an integral sliding mode control scheme is proposed as an ultimate solution to the control of systems actuated by pneumatic muscles. As the theoretical proof and computer simulations show, the integral sliding mode controller, with strong robustness to model uncertainties and external perturbations, is superior for performing the commanded control assignment. Based on the investigation in this dissertation, integral sliding mode control proposed here is a very promising robust control approach to handle systems actuated by pneumatic muscles

    Design and Control of the McKibben Artificial Muscles Actuated Humanoid Manipulator

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    The McKibben Pneumatic Artificial Muscles (PAMs) are expected to endow the advanced robots with the ability of coexisting and cooperating with humans. However, the application of PAMs is still severely hindered by some critical issues. Focusing on the bionic design issue, this chapter in detail presents the design of a 7-degree-of-freedom (DOF) human-arm-like manipulator. It takes the antagonized PAMs and Bowden cables to mimic the muscle-tendon-ligament structure of human arm by elaborately configuring the DOFs and flexibly deploying the routing of Bowden cables; as a result, the DOFs of the analog shoulder, elbow, and wrist of the robotic arm intersect at a point respectively and the motion of these DOFs is independent from each other for convenience of human-like motion. The model imprecision caused by the strong nonlinearity is universally acknowledged as a main drawback of the PAM systems. Focusing on this issue, this chapter views the model imprecision as an internal disturbance, and presents an approach that observe these disturbances with extended-state-observer (ESO) and compensate them with full-order-sliding-mode-controller (fSMC), via experiments validated the human-like motion performance with expected robustness and tracking accuracy. Finally, some variants of PAMs for remedying the drawbacks of the PAM systems are discussed

    Adaptive backstepping position control of pneumatic anthropomorphic robotic hand

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    This paper presents a nonlinear adaptive backstepping algorithm for position control of an anthropomorphic robotic hand. The contraction force of PAM actuator has been modeled based on an empirical approach and the overall finger is represented as a nonlinear second order system, taking into account the system uncertainty caused by hysteresis phenomenon in PAM actuators. Adaptive backstepping controller has been developed by formulating the estimator of the system uncertainty. To improve the performance of controller, a cascade control system is developed by combining a conventional PID control, as an inner loop controller, with the adaptive back stepping position control as the outer loop of the controller. Finally, a simulation test is conducted to evaluate the performance of the proposed controller

    Variable stiffness robotic hand for stable grasp and flexible handling

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    Robotic grasping is a challenging area in the field of robotics. When interacting with an object, the dynamic properties of the object will play an important role where a gripper (as a system), which has been shown to be stable as per appropriate stability criteria, can become unstable when coupled to an object. However, including a sufficiently compliant element within the actuation system of the robotic hand can increase the stability of the grasp in the presence of uncertainties. This paper deals with an innovative robotic variable stiffness hand design, VSH1, for industrial applications. The main objective of this work is to realise an affordable, as well as durable, adaptable, and compliant gripper for industrial environments with a larger interval of stiffness variability than similar existing systems. The driving system for the proposed hand consists of two servo motors and one linear spring arranged in a relatively simple fashion. Having just a single spring in the actuation system helps us to achieve a very small hysteresis band and represents a means by which to rapidly control the stiffness. We prove, both mathematically and experimentally, that the proposed model is characterised by a broad range of stiffness. To control the grasp, a first-order sliding mode controller (SMC) is designed and presented. The experimental results provided will show how, despite the relatively simple implementation of our first prototype, the hand performs extremely well in terms of both stiffness variability and force controllability
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