A traditional linear extended state observer (LESO) does not have enough capacity to handle the rapidly-varying back electromotive force (EMF). This key weakness limits the application of LESO in the internal permanent magnet synchronous motor (IPMSM) sensorless control system. In this article, an IPMSM sensorless drive method using an improved LESO (ILESO) is proposed. The ILESO consists of a pure integral and rapidly-varying sinusoidal interference estimator. The rapidly-varying sinusoidal interference estimator is added to the unknown interference estimation loop to estimate rapidly-varying back EMF. And the frequency of the rapidly-varying sinusoidal interference estimator changes adaptively with the operating frequency of the motor to guarantee the desired estimation accuracy of back EMF in a wide-speed range. This design makes it possible to observe the back EMF in a relatively low bandwidth, thereby solving the tradeoff between LESO bandwidth and high-frequency noise filtering. Then, an enhanced phase-locked loop is presented to improve the ability of tracking the rapidly-changing speed. The effectiveness of the proposed method is verified at a 2.0-kW IPMSM sensorless drive.

A Rotor Position and Speed Estimation Method Using an Improved Linear Extended State Observer for IPMSM Sensorless Drives

Owing to its potential superiorities in terms of flexibility, compliance, and robustness, the musculoskeletal robotic system has become a promising direction for next-generation robots. However, motion learning and generalization of musculoskeletal systems are still challenging problems. In this article, a muscle-synergies-based neuromuscular control is proposed. First, a new computational model of time-varying muscle synergies is constructed, which utilizes both phasic and tonic muscle synergies to characterize the basic features of muscle excitations more sufficiently. Second, a novel neuromuscular control method is proposed for realizing the motion learning and generalization of musculoskeletal systems. Therein, a radial basis function (RBF) neural network is designed to modulate muscle synergies according to different movement targets. Muscle excitations are computed with the combination of modulated muscle synergies. Covariance matrix adaptation evolutionary strategy is applied to realize the synchronous optimization of muscle synergies and the RBF neural network. In the experiment, a sophisticated musculoskeletal system learns to perform center-out reaching tasks through trial-and-error learning on a few targets. With the muscle synergies and neural modulation learned from a few targets, the musculoskeletal system can also reach many unexperienced targets. The proposed method not only improves the speed and accuracy of motion learning but also enhances motion generalization. This article also promotes the development of the musculoskeletal robotic system and the fusion of neuroscience and robotics.

Muscle-Synergies-Based Neuromuscular Control for Motion Learning and Generalization of a Musculoskeletal System

Trajectory tracking control for double-joint manipulator systems driven by pneumatic artificial muscles based on a nonlinear extended state observer

This article is concerned with the fixed-time active disturbance rejection control approach for nonlinear systems subject to uncertainties and disturbances. First, a new type of extended state observer (ESO) which can attain fixed-time convergence is established to estimate the states and the total disturbance. Then a fixed-time controller based on the above ESO is developed to achieve high precision control performance. Finally, the proposed method is utilized for the wheeled mobile robot where the experiment validates the effectiveness of the theoretical results.

Fixed-Time Active Disturbance Rejection Control and Its Application to Wheeled Mobile Robots

Current robotic studies are focused on the performance of specific tasks. However, such tasks cannot be generalized, and some special tasks, such as compliant and precise manipulation, fast and flexible response, and deep collaboration between humans and robots, cannot be realized. Brain-inspired intelligent robots imitate humans and animals, from inner mechanisms to external structures, through an integration of visual cognition, decision making, motion control, and musculoskeletal systems. This kind of robot is more likely to realize the functions that current robots cannot realize and become human friends. With the focus on the development of brain-inspired intelligent robots, this article reviews cutting-edge research in the areas of brain-inspired visual cognition, decision making, musculoskeletal robots, motion control, and their integration. It aims to provide greater insight into brain-inspired intelligent robots and attracts more attention to this field from the global research community. 

A Survey of Brain-Inspired Intelligent Robots: Integration of Vision, Decision, Motion Control, and Musculoskeletal Systems

This paper considers trajectory tracking control for a one-degree of freedom (DoF) manipulator system driven by pneumatic artificial muscles. A switched sliding mode controller is accordingly designed. In the proposed scheme, a novel extended state observer based on a generalized super-twisting algorithm is first designed to deal with internal uncertainties and external disturbances in the one-DoF manipulator system. Second, the finite time convergence of the closed-loop system with the proposed sliding mode technique is analyzed. Lastly, some experimental results are presented to show the validity and advantages of the proposed approach.

Trajectory Tracking Control of a One Degree of Freedom Manipulator Based on a Switched Sliding Mode Controller With a Novel Extended State Observer Framework

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Trajectory Tracking Control of a One Degree of Freedom Manipulator Based on a Switched Sliding Mode Controller With a Novel Extended State Observer Framework