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Intelligent Control of Robotic System
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Intelligent Control of Robotic System

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 20 April 2026 | Viewed by 3894

Special Issue Editors

Dr. Jian Chen

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
Interests: UAV; path planning; drones; hydraulic power transmission; CFD
Dr. Yi Zhang

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
Interests: mechanism design; robotic system and technology; biomechatronics; complex system modeling and control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pengfei Qian

E-Mail Website
Guest Editor
School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: robot design and control

Special Issue Information

Dear Colleagues,

The intelligent control of robotic systems enables robots to operate autonomously or semi-autonomously in complex environments. This field integrates various disciplines such as artificial intelligence, machine learning, computer vision, and sensor fusion to achieve sophisticated control over robotic actions. The general idea is to endow robots with the ability to perceive their surroundings, make decisions based on that perception, and execute tasks with a high degree of autonomy. The scope includes the current challenges and future develops in this field.

Technically, one of the main challenges in intelligent control is ensuring that robots can handle uncertainty and variability in real-world conditions. This involves developing algorithms that can process sensory data to understand the environment, predict outcomes, and adapt to changes dynamically. Another significant problem is ensuring the safety and reliability of these systems, especially when they interact with humans or operate in unstructured spaces.

The future trends in the intelligent control of robotic systems are likely to focus on increasing the level of autonomy and the complexity of tasks that robots can perform. This includes advancements in deep learning for improved decision-making, the development of more sophisticated sensors for enhanced environmental awareness, and the integration of swarm intelligence for coordinated multi-robot operations. Additionally, ethical considerations and the development of explainable AI will become increasingly important as robots are entrusted with more critical tasks, ensuring that their actions are transparent and accountable. The future of intelligent control promises a seamless integration of robots into various aspects of society, from manufacturing to healthcare, with a strong emphasis on human–robot collaboration and coexistence.

Dr. Jian Chen
Dr. Yi Zhang
Prof. Dr. Pengfei Qian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent control
  • robotic systems
  • robotic actions
  • human–robot collaboration
  • robot design and control

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Published Papers (4 papers)

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Research

20 pages, 4199 KB  
Article
Parkour Learning for Quadrupeds via Terrain-Conditional Adversarial Motion Priors
by Shuomo Zhang, Wei Zou and Hu Su
Appl. Sci. 2026, 16(7), 3448; https://doi.org/10.3390/app16073448 - 2 Apr 2026
Viewed by 341
Abstract
Agile parkour in unstructured environments poses significant challenges for quadruped robots, requiring both dynamic motion generation and terrain adaptability. Recent advances such as Adversarial Motion Priors (AMP) have shown promise in learning dynamic behaviors through motion imitation, but the resulting policies are typically [...] Read more.
Agile parkour in unstructured environments poses significant challenges for quadruped robots, requiring both dynamic motion generation and terrain adaptability. Recent advances such as Adversarial Motion Priors (AMP) have shown promise in learning dynamic behaviors through motion imitation, but the resulting policies are typically specialized and struggle to generalize across varying terrains. However, existing AMP-based approaches largely lack explicit environmental awareness, leading to limited adaptability and revealing a clear gap in achieving general agile locomotion. To address this limitation, we propose a novel terrain-conditional AMP framework that extends adversarial motion priors by conditioning the discriminator on explicit terrain features, enabling the learning of terrain-aware motion representations adaptable to diverse environments. To improve practical applicability, we further leverage a vision-based policy distillation scheme, where a teacher policy with privileged terrain height information supervises a student policy operating only on forward-looking depth images. This enables agile, perception-driven locomotion in real time. To the best of our knowledge, this is the first work to integrate environmental information into adversarial motion priors and jointly learn a vision-based policy through policy distillation for agile quadruped locomotion. Experiments on terrains such as platforms, gaps, stairs, slopes, and debris show that the proposed method achieves more efficient training convergence and higher success rates compared to pure AMP-based and RL-based methods. These results highlight the effectiveness of the proposed framework and represent a step toward perception-driven agile locomotion for quadruped robots in complex environments. Full article
(This article belongs to the Special Issue Intelligent Control of Robotic System)
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28 pages, 7805 KB  
Article
Balanced Walking Force Control for Walking Excavators Based on Dual Strong Tracking Kalman Filter
by Zhen Liu, Wenjie Yuan, Daqing Zhang, Yi Zhang and Dongliang Chen
Appl. Sci. 2025, 15(23), 12678; https://doi.org/10.3390/app152312678 - 29 Nov 2025
Viewed by 507
Abstract
The operation of walking excavators on rugged terrain often leads to leg lift-off, which can result in uneven force distribution, accelerated structural wear, and even systemic instability. To address these issues, this paper proposes a coordinated control framework comprising three integral components: a [...] Read more.
The operation of walking excavators on rugged terrain often leads to leg lift-off, which can result in uneven force distribution, accelerated structural wear, and even systemic instability. To address these issues, this paper proposes a coordinated control framework comprising three integral components: a Dual Strong Tracking Kalman Filter (DSTKF) for estimating unmeasurable system states—such as joint velocities, external forces, and hydraulic disturbances; a fuzzy adaptive virtual model-based force planner that dynamically adjusts the desired leg forces in real time to minimize support force variations; and a DSTKF-based force controller that precisely regulates the output force of each leg. Simulations and physical experiments demonstrate that the proposed approach effectively achieves autonomous balance of ground contact forces and optimizes force distribution among the legs. This study provides a lightweight, fully closed-loop solution for state estimation and walking force balance in walking excavators equipped with standard proportional valves. Full article
(This article belongs to the Special Issue Intelligent Control of Robotic System)
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26 pages, 2589 KB  
Article
Vision-Based Adaptive Control of Robotic Arm Using MN-MD3+BC
by Xianxia Zhang, Junjie Wu and Chang Zhao
Appl. Sci. 2025, 15(19), 10569; https://doi.org/10.3390/app151910569 - 30 Sep 2025
Cited by 1 | Viewed by 1140
Abstract
Aiming at the problems of traditional calibrated visual servo systems relying on precise model calibration and the high training cost and low efficiency of online reinforcement learning, this paper proposes a Multi-Network Mean Delayed Deep Deterministic Policy Gradient Algorithm with Behavior Cloning (MN-MD3+BC) [...] Read more.
Aiming at the problems of traditional calibrated visual servo systems relying on precise model calibration and the high training cost and low efficiency of online reinforcement learning, this paper proposes a Multi-Network Mean Delayed Deep Deterministic Policy Gradient Algorithm with Behavior Cloning (MN-MD3+BC) for uncalibrated visual adaptive control of robotic arms. The algorithm improves upon the Twin Delayed Deep Deterministic Policy Gradient (TD3) network framework by adopting an architecture with one actor network and three critic networks, along with corresponding target networks. By constructing a multi-critic network integration mechanism, the mean output of the networks is used as the final Q-value estimate, effectively reducing the estimation bias of a single critic network. Meanwhile, a behavior cloning regularization term is introduced to address the common distribution shift problem in offline reinforcement learning. Furthermore, to obtain a high-quality dataset, an innovative data recombination-driven dataset creation method is proposed, which reduces training costs and avoids the risks of real-world exploration. The trained policy network is embedded into the actual system as an adaptive controller, driving the robotic arm to gradually approach the target position through closed-loop control. The algorithm is applied to uncalibrated multi-degree-of-freedom robotic arm visual servo tasks, providing an adaptive and low-dependency solution for dynamic and complex scenarios. MATLAB simulations and experiments on the WPR1 platform demonstrate that, compared to traditional Jacobian matrix-based model-free methods, the proposed approach exhibits advantages in tracking accuracy, error convergence speed, and system stability. Full article
(This article belongs to the Special Issue Intelligent Control of Robotic System)
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18 pages, 1902 KB  
Article
Fuzzy Echo State Network-Based Fault Diagnosis of Remote-Controlled Robotic Arms
by Shurong Peng, Zexiang Guo, Xiaoxu Liu, Tan Zhang and Yunhao Yang
Appl. Sci. 2025, 15(11), 5829; https://doi.org/10.3390/app15115829 - 22 May 2025
Viewed by 1006
Abstract
This paper presents a novel fault diagnosis technique for remote-controlled robotic arm systems, utilizing deep fuzzy echo state networks (DFESNs) and applies the covariance matrix adaptation evolution strategy (CMA-ES) to optimize the hyperparameters of the DFESN model. The developed DFESN model, optimized via [...] Read more.
This paper presents a novel fault diagnosis technique for remote-controlled robotic arm systems, utilizing deep fuzzy echo state networks (DFESNs) and applies the covariance matrix adaptation evolution strategy (CMA-ES) to optimize the hyperparameters of the DFESN model. The developed DFESN model, optimized via CMA-ES, efficiently performs online fault classification through small datasets and training. The method is evaluated through experiments on a leader–follower robotic arm system, demonstrating high accuracy and efficiency. The faults under consideration include leader sensor fault, communication fault, actuator fault, and follower sensor fault. Only follower sensor data are utilized for fault diagnosis. The DFESN model achieves a mean accuracy of 99.5% with the shortest training and online diagnosis times compared to other methods, making it suitable for real-time fault diagnosis applications. Full article
(This article belongs to the Special Issue Intelligent Control of Robotic System)
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