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Transportation missions in aerospace are limited to the capability of each aerospace robot and the properties of the target transported object, such as mass, inertia, and grasping locations. We present a novel decentralized adaptive…
In this paper, we introduce an observer-free sliding mode control (SMC) method based on explicit structural compensation via the decomposition \( s = \alpha - \beta \). The proposed formulation eliminates the need for state observers and…
Hybrid model predictive control with both continuous and discrete variables is widely applicable to robotic control tasks, especially those involving contacts with the environment. Due to combinatorial complexity, the solving speed of…
Loss of mobility or balance resulting from neural trauma is a critical consideration in public health. Robotic exoskeletons hold great potential for rehabilitation and assisted movement, yet optimal assist-as-needed (AAN) control remains…
Unmanned aerial vehicle (UAV) with slung load system is a classic air transportation system. In practical applications, the suspension point of the slung load does not always align with the center of mass (CoM) of the UAV due to mission…
Robotic systems operating in unstructured environments require the ability to switch between compliant and rigid states to perform diverse tasks such as adaptive grasping, high-force manipulation, shape holding, and navigation in…
This contribution deals with multi-objective model-predictive control (MPC) of a wave energy converter (WEC) device concept, which can harvest energy from sea waves using a dielectric elastomer generator (DEG) power take-off system. We aim…
This study addresses a flexible holding tool for robotic disassembly. We propose a shell-type soft jig that securely and universally holds objects, mitigating the risk of component damage and adapting to diverse shapes while enabling soft…
A robust model predictive control (MPC) method is presented for linear, time-invariant systems affected by bounded additive disturbances. The main contribution is the offline design of a disturbance-affine feedback gain whereby the…
We propose a simple and computationally efficient approach for designing a robust Model Predictive Controller (MPC) for constrained uncertain linear systems. The uncertainty is modeled as an additive disturbance and an additive error on the…
Locomotion on dynamic rigid surface (i.e., rigid surface accelerating in an inertial frame) presents complex challenges for controller design, which are essential for deploying humanoid robots in dynamic real-world environments such as…
In this paper, we propose and demonstrate an adaptive-sliding mode control for trajectory tracking control of robot manipulators subjected to uncertain dynamics, vibration disturbance, and payload variation disturbance. Throughout this work…
An MPC controller uses a model of the dynamical system to plan an optimal control strategy for a finite horizon, which makes its performance intrinsically tied to the quality of the model. When faults occur, the compromised model will…
We propose a scalable cooperative control approach which coordinates a group of rigidly connected autonomous surface vessels to track desired trajectories in a planar water environment as a single floating modular structure. Our approach…
This paper formally develops a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots, integrating a model predictive control (MPC) algorithm with a gradient-descent-based adaptive updating…
This paper describes an online off-policy data-driven reinforcement learning based-algorithm to regulate and control the relative position of a deputy satellite in an autonomous satellite docking problem. The optimal control policy is…
Sampling-based model-predictive control (MPC) is a promising tool for feedback control of robots with complex, non-smooth dynamics, and cost functions. However, the computationally demanding nature of sampling-based MPC algorithms has been…
In this paper we propose a novel distributed model predictive control (DMPC) based algorithm with a trajectory predictor for a scenario of landing of unmanned aerial vehicles (UAVs) on a moving unmanned surface vehicle (USV). The algorithm…
In this paper, we present a robust adaptive model predictive control (MPC) scheme for linear systems subject to parametric uncertainty and additive disturbances. The proposed approach provides a computationally efficient formulation with…
For safe navigation in dynamic uncertain environments, robotic systems rely on the perception and prediction of other agents. Particularly, in occluded areas where cameras and LiDAR give no data, the robot must be able to reason about…