Review
Automation & Control Systems
Mark W. Spong
Summary: This article provides a historical overview of the application of control theory to robotic manipulators, focusing on the early theoretical foundations of robot control. It discusses the properties of robot dynamics that allow for the use of advanced control methods and the implementation of robust and adaptive control for manipulators. Additionally, it explores the topic of nonlinear control for underactuated robots and teleoperators.
ANNUAL REVIEW OF CONTROL ROBOTICS AND AUTONOMOUS SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Songhua Xu, Chunwei Zhang, Ardashir Mohammadzadeh
Summary: This paper investigates the control of robotic manipulators (RMs) which are widely used in industry. The highly nonlinear dynamics and the interaction of inputs-outputs cannot be ignored to improve accuracy in practice. Non-structural uncertainties such as friction, disturbance, and unmodeled dynamics pose additional challenges. Recently, a control idea based on type-3 fuzzy logic systems (FLSs) has been suggested, which shows better accuracy in noisy environments. The proposed approach utilizes T3-FLSs to estimate the dynamics of RMs and compensates for symmetrical perturbations, enhancing stability through online learning.
Article
Computer Science, Artificial Intelligence
B. Melih Yilmaz, Enver Tatlicioglu, Aydogan Savran, Musa Alci
Summary: The research proposes a repetitive learning control method fused with adaptive fuzzy logic techniques for industrial robotic manipulators. Modeling uncertainties are addressed using a fuzzy logic network and an adaptive fuzzy logic strategy with online tuning. Stability is ensured through Lyapunov type techniques, demonstrating the efficacy of the control methodology for robot manipulators.
APPLIED SOFT COMPUTING
(2021)
Article
Automation & Control Systems
David Cruz-Ortiz, Isaac Chairez, Alexander Poznyak
Summary: This study presents the design of a robust controller based on the sliding mode theory to ensure the safe operative synchronization of a teleoperated robotic system. The proposed controller utilizes a decentralized adaptive super-twisting algorithm to estimate the force exerted on the robotic system, and a decentralized non-singular terminal sliding mode controller to solve the synchronization problem. The control approach guarantees synchronization and convergence of the tracking error within a finite time.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Weizhi Lyu, Di-Hua Zhai, Yuhan Xiong, Yuanqing Xia
Summary: This paper investigates a novel adaptive control method for robotic manipulators to address predefined performance control, input saturation, and dynamic uncertainties, demonstrating its effectiveness through simulation studies and experiments.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Xiuxing Yin, Li Pan, Shibo Cai
Summary: The paper proposes a robust adaptive fuzzy sliding mode controller for uncertain serial robotic manipulators, achieving high precision control task with trajectory tracking accuracy and attenuation of uncertainties. The control algorithm is compared to a conventional controller and shows better trajectory tracking performance and robustness against large disturbances under the same operating conditions.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Computer Science, Artificial Intelligence
Yuling Li, Liping Wang, Kun Liu, Wei He, Yixin Yin, Rolf Johansson
Summary: This article addresses the distributed cooperative control design for a class of sampled-data teleoperation systems with multiple slave mobile manipulators. The control task is to guarantee the task-space position synchronization between the master and the grasped object with the mobile bases in a fixed formation. A fully distributed control strategy including neural-network-based task-space synchronization controllers and neural-network-based null-space formation controllers is proposed. The stability and the synchronization/formation features of the system are analyzed, and the relationship among the control parameters, the upper bound of the time delays, and the maximum allowable sampling interval is established.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
B. Melih Yilmaz, Enver Tatlicioglu, Aydogan Savran, Musa Alci
Summary: This article aims to design a joint space tracking controller for robotic manipulators with uncertainties in their mathematical representations and without joint velocity sensing. The design consists of two parts: the first part deals with modeling uncertainties using a self-organized adaptive fuzzy-logic (AFL)-based controller with full-state feedback (FSFB), yielding semiglobally uniformly ultimately bounded tracking results. In the second part, a high-gain joint velocity observer is designed to replace error vectors in the FSFB controller, resulting in a self-organized AFL-based robust output-feedback controller. Comparative experiment results are presented to demonstrate the efficacy of the proposed control methodology.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Information Systems
Gaorong Lin, Jinpeng Yu, Jiapeng Liu
Summary: An adaptive fuzzy impedance control method is proposed in this paper to improve the performance and safety of robots by approximating system uncertainties, while utilizing finite-time control and command filtered techniques to optimize the interaction performance of the system and handle computational complexity. Simulations are conducted to demonstrate the effectiveness of the proposed control method in physical human-robot interaction.
Article
Computer Science, Artificial Intelligence
Gang Li, Jinpeng Yu, Xinkai Chen
Summary: In this article, an adaptive fuzzy neural network (NN) command filtered impedance control method is proposed for constrained robotic manipulators with disturbance observers. The barrier Lyapunov functions are introduced to handle the full-state constraints, and the adaptive fuzzy NN is utilized to handle the unknown system dynamics. A disturbance observer is designed to eliminate the effect of unknown bound disturbance. The effectiveness of the proposed control method is validated through simulation studies.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zhixin Zhang, Zhiyong Chen
Summary: This article proposes a simple and novel scheme for modeling and control of robotic manipulators, obtaining an analytical dynamic model from artificially excited training data using symbolic regression technique, and designing a controller based on the dynamic model. The scheme requires less training data compared to machine learning methods and simplifies controller design by utilizing decoupling feature.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Hang Su, Wen Qi, Jiahao Chen, Dandan Zhang
Summary: This paper proposes a task-space control approach based on fuzzy approximation for a teleoperated surgery scenario utilizing a serial redundant robot manipulator. The accuracy and safety of the surgical operation are improved by considering and estimating the dynamical uncertainties caused by the physical interaction.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Mathematics
Yassine Bouteraa, Khalid A. Alattas, Obaid Alshammari, Sondess Ben Aoun, Mohamed Amin Regaieg, Saleh Mobayen
Summary: The remarkable features of hybrid SMC assisted with fuzzy systems supplying parameters of the controller have led to significant success of these control approaches, especially in the control of multi-input and multi-output nonlinear systems. The development of type-1 fuzzy systems to type-2 fuzzy systems has improved the performance of fuzzy systems due to the ability to model uncertainties in the expression of expert knowledge. The paper proposes a basic approach of designing and implementing interval type-2 fuzzy sliding mode control for improved performance.
Article
Automation & Control Systems
Aldo Jonathan Munoz-Vazquez, Chidentree Treesatayapun
Summary: This paper proposes a discrete-time model-free-implementation controller, which compensates external disturbances using fuzzy logic formulation to improve control performance of robots. The feasibility of the proposed approach is demonstrated through representative simulation and experiments.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Kaixin Lu, Shuaishuai Han, Jun Yang, Haoyong Yu
Summary: Compliant actuators have great advantages in safe robot control, but achieving optimal trajectory tracking control remains a challenge. This study proposes an inverse optimal adaptive neural control scheme, which uses a tuning functions-based adaptive learning mechanism to improve control efficiency and simplify implementation in practical engineering systems.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)