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Precise grasp force regulation in tendon-driven surgical instruments is fundamentally limited by nonlinear coupling between motor dynamics, transmission compliance, friction, and distal mechanics. Existing solutions typically rely on distal…

Artificial muscle-driven modular soft robots exhibit significant potential for executing complex tasks. However, their broader applicability remains constrained by the lack of dynamic model-based control strategies tailored for…

This paper introduces a new generalized control method designed for multi-degrees-of-freedom devices to help people with limited motion capabilities in their daily activities. The challenge lies in finding the most adapted strategy for the…

Many real-world systems are governed by the time-dependent, nonlinear differential equations. Dynamics of an electrical system are also best described using the very equations. Being one of the preferred machines when using advanced control…

Intelligent manipulation benefits from the capacity to flexibly control an end-effector with high degrees of freedom (DoF) and dynamically react to the environment. However, due to the challenges of collecting effective training data and…

This paper develops a unified frequency-domain framework for the analysis of sliding-mode control systems, encompassing both discontinuous and Lipschitz-continuous implementations. Using describing function (DF) theory, closed-form…

This work presents the coordinated motion control and obstacle-crossing problem for the four wheel-leg independent motor-driven robotic systems via a model predictive control (MPC) approach based on an event-triggering mechanism. The…

The subject of this paper is the optimal design of a parallel mechanism intended for three-axis machining applications. Parallel mechanisms are interesting alternative designs in this context but most of them are designed for three- or…

With the development of state-of-art deep reinforcement learning, we can efficiently tackle continuous control problems. But the deep reinforcement learning method for continuous control is based on historical data, which would make…

In this paper, attitude maneuver control without unwinding phenomenon is investigated for rigid spacecraft. First, a novel switching function is constructed by a hyperbolic sine function. It is shown that the spacecraft system possesses the…

Solving motion tasks autonomously and accurately is a core ability for intelligent real-world systems. To achieve genuine autonomy across multiple systems and tasks, key challenges include coping with unknown dynamics and overcoming the…

While it is expected to build robotic limbs with multiple degrees of freedom (DoF) inspired by nature, a single DoF design remains fundamental, providing benefits that include, but are not limited to, simplicity, robustness,…

The ability of robots to navigate through doors is crucial for their effective operation in indoor environments. Consequently, extensive research has been conducted to develop robots capable of opening specific doors. However, the diverse…

A data-efficient learning-based control design method is proposed in this paper. It is based on learning a system dynamics model that is then leveraged in a two-level procedure. On the higher level, a simple but powerful optimization…

In this paper, we propose a framework for designing sliding mode controllers for a class of mechanical systems with symmetry, both unconstrained and constrained, that evolve on principal fiber bundles. Control laws are developed based on…

We propose and demonstrate a nonlinear control method that can be applied to unknown, complex systems where the controller is based on a type of artificial neural network known as a reservoir computer. In contrast to many modern…

Flexible-joint manipulators are governed by complex nonlinear dynamics, defining a challenging control problem. In this work, we propose an approach to learn an outer-loop joint trajectory tracking controller with deep reinforcement…

To rationalize the relatively high investment that industrial automation systems entail, research in the field of intelligent machines should target high value functions such as fettling, die-finishing, deburring, and fixtureless…

This paper proposes a hybrid force-motion framework that utilizes real-time surface normal updates. The surface normal is estimated via a novel method that leverages force sensing measurements and velocity commands to compensate the…

As autonomous systems become more complex and integral in our society, the need to accurately model and safely control these systems has increased significantly. In the past decade, there has been tremendous success in using deep learning…