In this article, the problem of tracking control is considered for a class of uncertain strict-feedback nonlinear systems with deferred asymmetric time-varying full-state constraints. A novel adaptive robust full-state-constrained control scheme is dev…
We study the stabilization problem for nonlinear stochastic systems via an event-triggered impulsive control (ETIC) scheme, where the impulsive control time sequence is generated by the event-triggered mechanism (ETM). Both continuous ETM and periodic …
In this article, we apply a high-order control to a dynamical system with uncertainty. There are two characteristics. First, the uncertain part, which is time-varying but bounded, is described in a fuzzy aspect. Specifically, the uncertainty lies withi…
In this article, the data-driven optimal formation control problem is addressed for a heterogeneous quadrotor team with a virtual leader. Each quadrotor is considered as a highly nonlinear system with six degrees of freedom and the accurate dynamic inf…
Indoor place category recognition for a cleaning robot is a problem in which a cleaning robot predicts the category of the indoor place using images captured by it. This is similar to scene recognition in computer vision as well as semantic mapping in …
Multipoint dynamic aggregation is a meaningful optimization problem due to its important real-world applications, such as post-disaster relief, medical resource scheduling, and bushfire elimination. The problem aims to design the optimal plan for a set…
Domain adaptation aims to facilitate the learning task in an unlabeled target domain by leveraging the auxiliary knowledge in a well-labeled source domain from a different distribution. Almost existing autoencoder-based domain adaptation approaches foc…
This article provides a solution to tube-based output feedback robust model predictive control (RMPC) for discrete-time linear parameter varying (LPV) systems with bounded disturbances and noises. The proposed approach synthesizes an offline optimizati…
Autonomous learning algorithms operate in an online fashion in dealing with data stream mining, where minimum computational complexity is a desirable feature. For such applications, parsimonious learning machines (PALMs) are suitable candidates due to …
Few-shot learning (FSL) for human–object interaction (HOI) aims at recognizing various relationships between human actions and surrounding objects only from a few samples. It is a challenging vision task, in which the diversity and interactivity…