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Updated: May 10, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Exploring multi-objective optimization scheduling strategies for power dispatch robots based on an improved particle

Han Yan¹, Min Zhang², Yuqi Zhu²

¹Yunnan Electric Power Dispatching and Control Center, Yunnan Power Grid Co., Ltd., Kunming, 650011, China. yanmi236470658@163.com.

Scientific Reports

|May 8, 2026

Summary

This summary is machine-generated.

This study introduces an improved particle swarm optimization for power dispatch robots, enhancing scheduling efficiency. The new method significantly cuts operating costs and reduces network loss, supporting green energy development.

Keywords:

Constraints Multi-objective optimization Particle swarm optimization Power dispatch Robots Scheduling methods

Related Experiment Videos

Last Updated: May 10, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

Robotics and Automation
Optimization Algorithms
Power Systems Engineering

Background:

Traditional particle swarm optimization (PSO) struggles with local optima in power dispatch scheduling.
Efficient scheduling is crucial for power system cost reduction and environmental impact mitigation.

Purpose of the Study:

To develop advanced multi-objective optimization scheduling strategies for power dispatch robots.
To overcome the limitations of traditional PSO algorithms in achieving globally optimal solutions.

Main Methods:

Implementation of an improved particle swarm optimization (PSO) algorithm framework.
Integration of an adaptive inertia weight adjustment mechanism to enhance search efficiency.
Construction of a multi-objective optimization model for power dispatch robot scheduling.

Main Results:

Significant reduction in total power system operating costs by approximately $19,326.8.
Maintenance of monthly pollutant emissions below 100 tons.
Effective reduction in system network loss.

Conclusions:

The improved PSO algorithm offers superior performance for multi-objective power dispatch scheduling.
This approach enhances power scheduling efficiency and promotes green energy utilization.
The method provides substantial support for the sustainable development of power systems.