Adaptive sliding mode observer–based integral sliding mode model-free torque control for elastomer series elastic actuator–based manipulator

In this brief, an adaptive nonsingular terminal sliding mode observer–based adaptive integral terminal sliding mode model-free control is proposed for the trajectory tracking control of the output torque of elastomer series elastic actuator–based manipulator. Considering the tip load and its external disturbance, an elastomer series elastic actuator–based manipulator model is established. In order to realize the output torque tracking control of elastomer series elastic actuator–based manipulator, by using the characteristics of elastomer series elastic actuator, the output torque control is transformed into position control. Based on the idea of model-free control, an ultra-local model is applied to approximate the dynamic of the manipulator, and all the model information is considered as an unknown lumped disturbance. The adaptive nonsingular terminal sliding mode observer is designed to estimate the lumped disturbance, and the absolute value of the tracking error is introduced into the sliding surface to make the selection of parameters more flexible. Then, on the basis of adaptive nonsingular terminal sliding mode observer, the adaptive integral terminal sliding mode model-free control is proposed under model-free control framework. The design and analysis of both observer and controller do not rely on accurate model information. Finally, the performance of the proposed method is verified by simulation results.

Hunger improves reinforcement-driven but not planned action

Human decisions can be reflexive or planned, being governed respectively by model-free and model-based learning systems. These two systems might differ in their responsiveness to our needs. Hunger drives us to specifically seek food rewards, but here we ask whether it might have more general effects on these two decision systems. On one hand, the model-based system is often considered flexible and context-sensitive, and might therefore be modulated by metabolic needs. On the other hand, the model-free system’s primitive reinforcement mechanisms may have closer ties to biological drives. Here, we tested participants on a well-established two-stage sequential decision-making task that dissociates the contribution of model-based and model-free control. Hunger enhanced overall performance by increasing model-free control, without affecting model-based control. These results demonstrate a generalized effect of hunger on decision-making that enhances reliance on primitive reinforcement learning, which in some situations translates into adaptive benefits.

A Survey for Machine Learning-Based Control of Continuum Robots

Soft continuum robots have been accepted as a promising category of biomedical robots, accredited to the robots’ inherent compliance that makes them safely interact with their surroundings. In its application of minimally invasive surgery, such a continuum concept shares the same view of robotization for conventional endoscopy/laparoscopy. Different from rigid-link robots with accurate analytical kinematics/dynamics, soft robots encounter modeling uncertainties due to intrinsic and extrinsic factors, which would deteriorate the model-based control performances. However, the trade-off between flexibility and controllability of soft manipulators may not be readily optimized but would be demanded for specific kinds of modeling approaches. To this end, data-driven modeling strategies making use of machine learning algorithms would be an encouraging way out for the control of soft continuum robots. In this article, we attempt to overview the current state of kinematic/dynamic model-free control schemes for continuum manipulators, particularly by learning-based means, and discuss their similarities and differences. Perspectives and trends in the development of new control methods are also investigated through the review of existing limitations and challenges.

Trajectory tracking control of quadrotor under variable payloads with model-free controller and sliding mode control technology

Purpose This study aims to deal with the problem of trajectory tracking control for the quadrotor under external environmental disturbance and variable payloads. Design/methodology/approach In the field of unmanned aerial vehicle (UAV) control, external environmental disturbance and internal variable payloads as two major interference factors lead to control performance degradation or even instability, thus a trajectory tracking controller which innovatively combines sliding mode control technology and model-free control technique is proposed. The proposed controller is constructed with a learning rate-based sliding mode controller and an ultra-local model. Based on the proposed controller, the nonlinear system model of variable load quadrotor is locally estimated and the system’s uncertainties and disturbances can be compensated. Findings The simulation and actual test results demonstrate the satisfactory control performance and the robustness of the proposed controller compared with the PID and Backstepping controller under external environmental disturbance and variable payloads. Moreover, the proposed controller solves the trajectory tracking control problem not only when payloads change at the center of gravity but also when the position of load variation deviates from the center of gravity. Practical implications In both military and civilian domains, the quadrotor may encounter such situations that the payloads change, such as transporting goods, aerial refueling and so on. As a large internal interference factor, variable load tends to lead to unstable control. The research results provide theoretical guidance and technical support for trajectory tracking control of quadrotor under variable payloads. Originality/value The proposed controller combines learning rate-based sliding mode controller and model-free control technique to achieve a more efficient and accurate trajectory control of the quadrotor when considering system uncertainties and the load variation that happens in the unknown location.