Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning
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Summary
This paper investigates fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact.
- Type
- dissertation
- Published
- 2019-03-06
- Cited by
- 254
- References
- 71
- Access
- Open access
- OpenAlex
- https://openalex.org/W30843856
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:73516143
Keywords
Humanities, Philosophy
References
- Observer-Based Adaptive Backstepping Consensus Tracking Control for High-Order Nonlinear Semi-Strict-Feedback Multiagent Systems
- Adaptive fuzzy systems and control
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- Trilateral Teleoperation of Adaptive Fuzzy Force/Motion Control for Nonlinear Teleoperators With Communication Random Delays
- Impedance Control: An Approach to Manipulation: Part I—Theory
- Observer Design of Discrete-Time T–S Fuzzy Systems Via Multi-Instant Homogenous Matrix Polynomials
- Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints
- Robots at work with medicines and adhesives
- Learning From Adaptive Neural Dynamic Surface Control of Strict-Feedback Systems
- Decentralized Stabilization for a Class of Continuous-Time Nonlinear Interconnected Systems Using Online Learning Optimal Control Approach
- Adaptive neural decentralized control for strict feedback nonlinear interconnected systems via backstepping
- Exact tracking control of nonlinear systems with time delays and dead-zone input
- Human–Robot Collaboration Based on Motion Intention Estimation
- Neural Network Control of a Rehabilitation Robot by State and Output Feedback
- The Concept of “Attractive Region in Environment” and its Application in High-Precision Tasks With Low-Precision Systems
- Impedance control with adaptation for robotic manipulations
- Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model
- A reference model approach to stability analysis of neural networks
- Application of rubber artificial muscle manipulator as a rehabilitation robot
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- Adaptive optimal output feedback tracking control for unknown discrete‐time linear systems using a combined reinforcement Q‐learning and internal model method
- Impedance Control for Coordinated Robots by State and Output Feedback
- Adaptive Fuzzy Control for a Constrained Robot in the Presence of Input Nonlinearity
- Fault diagnosis and fault‐tolerant control based on Laplace transform for nonlinear networked control systems with random delay
- Leader–Follower Bipartite Output Synchronization on Signed Digraphs Under Adversarial Factors via Data-Based Reinforcement Learning
- Reinforcement Q-Learning Algorithm for H∞ Tracking Control of Unknown Discrete-Time Linear Systems
- Adaptive Smoothness Constraint Image Multilevel Fuzzy Enhancement Algorithm
- A Single Parameter-Based Adaptive Approach to Robotic Manipulators With Finite Time Convergence and Actuator Fault
- Neural networks-based fixed-time control for a robot with uncertainties and input deadzone
- Adaptive Neural Network Stabilization Control of Underactuated Unmanned Surface Vessels With State Constraints
- Adaptive Tracking Control of State Constraint Systems Based on Differential Neural Networks: A Barrier Lyapunov Function Approach
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- Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning
- Convergence Analysis of Adaptive Exponential Functional Link Network
- Active disturbance rejection control strategy for airborne radar stabilization platform based on cascade extended state observer
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