Article
Energy & Fuels
Maciej Lawrynczuk, Piotr M. Marusak, Patryk Chaber, Dawid Seredynski
Summary: This study discusses twelve initialization strategies for nonlinear model predictive control (MPC) algorithms. Two strategies, namely the hybrid strategy with an auxiliary MPC controller based on a successively linearized model and the method which uses the optimal solution obtained at the previous sampling instant, are found to be the fastest and most robust. The hybrid strategies perform better than the approximation-based ones with complex neural networks due to the negative feedback mechanism in the auxiliary controller.
Article
Multidisciplinary Sciences
Usman Nasim, Abdul Rauf Bhatti, Muhammad Farhan, Akhtar Rasool, Arslan Dawood Butt
Summary: In recent years, there has been a growing interest in synchronous reluctance motors (SynRM) due to their high efficiency and lack of magnetic material. However, unmodeled dynamics and external disturbances limit their widespread adoption. This study presents a novel reaching law-based sliding mode control approach to address these issues and optimize the performance of SynRM.
Article
Engineering, Mechanical
Sezgin Eser, Sevda Telli Cetin
Summary: This paper presents a stable control method for a single link flexible manipulator, achieving position and vibration control through optimized controller parameters. The Artificial Bee Colony Algorithm is used to simultaneously optimize controller parameters, with simulations showing that the proposed method can achieve the control objectives under different loads.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
(2022)
Article
Multidisciplinary Sciences
Tuan Anh Nguyen
Summary: This article introduces a new solution to direct an active suspension system called the PID-SMC hybrid algorithm. Simulation and calculation results show that using the PID-SMC method greatly reduces vehicle acceleration and displacement values, leading to improved road holding and ride comfort.
Article
Engineering, Aerospace
James Blaise, Michael C. F. Bazzocchi
Summary: This research explores the challenges of close-proximity space manipulation using a deep reinforcement learning control approach. The results demonstrate that the deep reinforcement learning controller can effectively capture space objects and avoid collisions when collision avoidance is considered.
Article
Automation & Control Systems
Ololade O. Obadina, Mohamed A. Thaha, Zaharuddin Mohamed, M. Hasan Shaheed
Summary: This study presents the development of a grey-box modelling approach and fuzzy logic control for real time trajectory control of an experimental four degree-of-freedom Leader-Follower Robot (LFR) manipulator system using a hybrid optimisation algorithm.
Article
Engineering, Multidisciplinary
Ameer Tamoor Khan, Shuai Li, Xinwei Cao
Summary: This paper introduces a model-free tracking controller for cooperative mobile-manipulators in smart homes, utilizing an optimization-driven approach and a novel algorithm called ZNNBAS. The algorithm is proven to be stable and efficient in achieving accurate and robust task completion for the mobile-manipulators.
Article
Automation & Control Systems
Romeo Ortega, Vladislav Gromov, Emmanuel Nuno, Anton Pyrkin, Jose Guadalupe Romero
Summary: This paper proposes a solution to the problem of parameter estimation of nonlinearly parameterized regressions, applied for adaptive control, with a focus on ensuring parameter convergence through an excitation assumption. The method is applied to design adaptive controllers for different classes of nonlinear systems and demonstrated with several classical examples.
Article
Engineering, Aerospace
Huazi Cao, Yu Wu, Lixin Wang
Summary: This paper investigates the end-effector position tracking control task for the aerial manipulator, dividing it into motion control and coordinate planning. A motion controller is designed using adaptive neural network control, and a novel predictive coordinate planning method based on the move blocking method is proposed. Simulations and comparisons demonstrate the effectiveness and performance of the proposed methods.
AEROSPACE SCIENCE AND TECHNOLOGY
(2022)
Article
Mathematics
Shou-Yu Chen, Jeng-Rong Ho, Pi-Cheng Tung, Chih-Kuang Lin
Summary: This paper presents a method for tightening M1.4 screws using a manipulator controlled via image processing algorithms and position control. Experimental results demonstrate the successful tightening of M1.4 screws into target holes using the manipulator.
Article
Automation & Control Systems
Haihong Li, Lingxiao Xun, Gang Zheng
Summary: Soft manipulators are a relatively new type of robots that pose challenges in control. This paper proposes a hybrid control strategy that combines a piecewise linear strain Cosserat model and radial basis function neural network to achieve global control of the end-effector position of soft manipulators.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Engineering, Marine
Dongyang Shang, Xiaopeng Li, Meng Yin, Sainan Zhou
Summary: The flexible single-link underwater manipulator (FSLUM) can perform various underwater tasks in autonomous underwater vehicles, playing a significant role in ocean exploration and development. This study proposes an improved sliding mode control strategy based on neural network identification to enhance the control accuracy of the FSLUM. Simulation and prototype tracking control experiments demonstrate that the proposed strategy achieves high tracking accuracy.
Article
Automation & Control Systems
Zeyu Li, Junyong Zhai, Hamid Reza Karimi
Summary: This article addresses the trajectory tracking problem for industrial robotic manipulators with control backlash by using an arctangent terminal sliding mode surface and an adaptive super-twisting sliding mode control method. The chattering in control law is overcome by the super-twisting method, achieving fast convergence and continuous control. Based on Lyapunov stability theory, the trajectory tracking error will converge to zero in a finite time with the proposed control scheme.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Engineering, Electrical & Electronic
Shijun Zhang, Shuhong Cheng, Zhenlin Jin
Summary: This paper presents a kinematic modeling method and derivation of the dynamic model for a mobile manipulator. It analyzes the null-space composition and divides the task space to design various task-switching criteria. Furthermore, a hybrid control model based on dynamic parameter identification is designed, and its stability is proved. Experimental results verify the effectiveness of the proposed method in improving the control accuracy of the mobile manipulator.
Article
Mathematics, Interdisciplinary Applications
Yixiao Ding, Xiaolian Liu, Pengchong Chen, Xin Luo, Ying Luo
Summary: This paper proposes a fractional-order impedance controller for robot manipulators, which improves the performance by accurately describing the damping force. Fair comparisons show that the fractional-order impedance controller outperforms the integer-order impedance controller in terms of step response and disturbance rejection.
FRACTAL AND FRACTIONAL
(2022)
Article
Computer Science, Interdisciplinary Applications
Emanuele Carpanzano, Amedeo Cesta, Andrea Orlandini, Riccardo Rasconi, Marco Suriano, Alessandro Umbrico, Anna Valente
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2016)
Article
Engineering, Industrial
F. Tonelli, A. A. G. Bruzzone, M. Paolucci, E. Carpanzano, G. Nicolo, A. Giret, M. A. Salido, D. Trentesaux
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2016)
Editorial Material
Computer Science, Interdisciplinary Applications
Rosanna Fornasiero, Emanuele Carpanzano
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2017)
Article
Engineering, Industrial
Doriana M. D'Addona, Fabrizio Bracco, Andrea Bettoni, Nariaki Nishino, Emanuele Carpanzano, Alessandro A. Bruzzone
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2018)
Article
Computer Science, Interdisciplinary Applications
A. Valente, M. Mazzolini, E. Carpanzano
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2015)
Article
Automation & Control Systems
Yoram Halevi, Emanuele Carpanzano, Giuseppe Montalbano
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME
(2014)
Article
Computer Science, Artificial Intelligence
E. Carpanzano, L. Ferrucci, D. Mandrioli, M. Mazzolini, A. Morzenti, M. Rossi
JOURNAL OF INTELLIGENT MANUFACTURING
(2014)
Article
Engineering, Industrial
A. Valente, D. Gitardi, E. Carpanzano
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2020)
Article
Engineering, Industrial
A. Valente, G. Pavesi, M. Zamboni, E. Carpanzano
Summary: Deliberative robots adapt their behavior based on interaction dynamics with human teammates and production context, enhancing productivity and ensuring human safety and job quality.
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2022)
Article
Engineering, Industrial
Daniel Knuttel, Stefano Baraldo, Anna Valente, Friedrich Bleicher, Konrad Wegener, Emanuele Carpanzano
Summary: This paper presents a novel machine learning based prediction approach that characterizes tool paths using features associated to process parameters and performed geometry, aiming to improve important geometrical deviations in the tool refurbishment process.
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2022)
Article
Chemistry, Multidisciplinary
Emanuele Carpanzano, Daniel Knuettel
Summary: This paper provides an overview of novel applications of AI methods to industrial control systems on different levels, aiming to improve the self-learning capacities, overall performance, process and product quality, resource utilization, safety, and resilience of production systems. The major open challenges and future perspectives are also addressed.
APPLIED SCIENCES-BASEL
(2022)
Editorial Material
Chemistry, Multidisciplinary
Emanuele Carpanzano
APPLIED SCIENCES-BASEL
(2023)
Proceedings Paper
Engineering, Manufacturing
Andrea Barni, Emanuele Carpanzano, Giuseppe Landolfi, Paolo Pedrazzoli
PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON THE INDUSTRY 4.0 MODEL FOR ADVANCED MANUFACTURING (AMP 2019)
(2019)
Article
Engineering, Industrial
J. Krueger, L. Wang, A. Verl, T. Bauernhansl, E. Carpanzano, S. Makris, J. Fleischer, G. Reinhart, J. Franke, S. Pellegrinelli
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2017)
Article
Engineering, Industrial
Anna Valente, Stefano Baraldo, Emanuele Carpanzano
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2017)
