Decentralized Control of Crop Growth Conditions in Vertical Farms Under Dynamic Energy Markets

The growing global population and the increasing scarcity of arable land highlight the urgent need for reliable and efficient food production systems. With their controlled environments, vertical farms (VFs) offer a promising solution for sustainable food security. Nevertheless, their high energy demands call for innovative approaches to optimize energy consumption while maintaining optimal growing conditions.…

Process Optimization of Multi-Stage Continuous Production System Based on Feature Fusion Modeling

Modeling and process optimization of Multi-stage Continuous Production System (MCPS) is an important research topic in the field of intelligent manufacturing today. However, due to the inherent properties of MCPS, existing researches face difficulties in 1) coupling optimization across multiple stages under diverse constraints and objectives, and 2) accurately mapping controllable process variables to critical…

Linear-Parameterization-Based Model Free Adaptive Predictive Control

A novel data-driven predictive control scheme is proposed for unknown nonlinear discrete-time systems. The control increment vector is linearly parameterized through the dynamic linearization (DL) on the ideal controllers along the prediction horizon, leading to a predictive control law. The control gain matrix is adaptively tuned through the recursive least square method. The distinctive predictive…

Adaptive Resilient Event-Triggered Control for T-S Fuzzy Multi-Area Power System With Pumped Storage Hydropower Under Deception Attacks

This paper investigates the load frequency control problem for nonlinear multi-area power system that incorporates pumped storage hydropower. First, a unified Takagi-Sugeno fuzzy model is developed to represent the nonlinear dynamics of power exchange progress in pumped storage hydropower, including uncertainties. Subsequently, a fuzzy logic algorithm is applied by incorporating both system error and its…

Safe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processes

Controller tuning and optimization have long been recognized as fundamental challenges in robotics and mechatronic systems. Traditional controller design techniques are usually model-based, and their closed-loop performance depends on the fidelity of the mathematical model. Subsequent tuning of the controller parameters is frequently carried out via empirical rules, which may still suffer from model inaccuracies.…

ADP: Adaptive Diffusion Policy Energizes Robots Thinking in Both Learning and Practice

Adaptive control policies for robots often require balancing generalization from large offline datasets with efficient adaptation to specific deployment conditions. In this paper, we propose Adaptive Diffusion Policy (ADP), a two-stage framework that integrates temporal-aware diffusion models with parameter-efficient LoRA adaptation. First, in the learning stage, ADP imitates and generates actions based on image and…

Koopman-Based Fractional Predefined-Time Control for Wearable Exoskeleton System

In this paper, an innovative fractional predefined-time control scheme based on the Koopman operator is proposed for the wearable exoskeleton system (WES). Leveraging the nonlinear mapping of the Koopman operator and the prior information of the exoskeleton system, a prior Koopman model (PIKOM) is devised. As a data-driven model, the PIKOM can be constructed through…

Video Friday: A Billion Dollars for Humanoid Robots

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.ACTUATE 2025: 23–24 September 2025, SAN FRANCISCOCoRL 2025: 27–30 September 2025, SEOULIEEE Humanoids: 30 September–2 October…

Adaptive Safety-Critical Control for High-Order Systems: A Real-Time Gaussian Process Approach

This paper proposes a novel adaptive fast variational sparse Gaussian process (AFVSGP) framework to ensure real-time safety for high-order systems under model uncertainties and dynamic obstacle environments. The framework effectively addresses the challenge of maintaining real-time safety guarantees during unknown trajectory transitions in nonstationary environments. To achieve this, the proposed framework incorporates three key innovations.…

Autonomous Dental Surgery for Root Canal Treatment: Compensating for Robot-Patient Misalignment and File Deflection

Robotic technologies are increasingly used in dentistry for their precision in delicate procedures. While most dental robots focus on implant surgery, automating root canal treatment (RCT) remains challenging due to the need to guide a thin, flexible endodontic file through a narrow, curved root canal without causing ledging or file fracture. Patient movements—particularly those that…