Adaptive Visual Tracking Control for Manipulator With Actuator Fuzzy Dead-Zone Constraint and Unmodeled Dynamic

Liu, Zhi; Wang, Fujie; Zhang, Yun

doi:10.1109/tsmc.2015.2420037

Cited by 94 publications

(36 citation statements)

References 53 publications

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“…Considering the limitation of the water tank in the laboratory, we have to order the robot activities to ensure that the robot can execute the trial as long as possible. Vision is always served as one of the primary sources to assist robot control [33], [34]. Therefore, the robot roughly estimates its position by the onboard camera and two landmarks are employed to improve the identification.…”

Section: A Aquatic Environment Perceptionmentioning

confidence: 99%

Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

Wang

Xie

2019

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Autonomous gait optimization is an essential survival ability for mobile robots. However, it remains a challenging task for underwater robots. This paper addresses this problem for the locomotion of a bio-inspired robotic fish and aims at identifying fast swimming gait autonomously by the robot. Our approach for learning locomotion controllers mainly uses three components: 1) a biological concept of central pattern generator to obtain specific gaits; 2) an onboard sensory processing center to discover the environment and to evaluate the swimming gait; and 3) an evolutionary algorithm referred to as particle swarm optimization. A key aspect of our approach is the swimming gait of the robot is optimized autonomously, equivalent to that the robot is able to navigate and evaluate its swimming gait in the environment by the onboard sensors, and simultaneously run a built-in evolutionary algorithm to optimize its locomotion all by itself. Forward speed optimization experiments conducted on the robotic fish demonstrate the effectiveness of the developed autonomous optimization system. The latest results show that our robotic fish attained a maximum swimming speed of 1.011 BL/s (40.42 cm/s) through autonomous gait optimization, faster than any of the robot's previously recorded speeds.

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Section: A Aquatic Environment Perceptionmentioning

confidence: 99%

Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

Wang

Xie

2019

IEEE Trans. Syst. Man Cybern, Syst.

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“…Remark 3: In this paper, RBF neural networks are employed due to its capabilities of approximating any unstructured smooth nonlinear functions to arbitrary accuracy over a compact set. In fact, one can use other online approximation method instead, such as fuzzy logic systems [43], [44], [45], [32]. The online approximator can be further reduced to a regressor function by assuming that the uncertainties are structured and are linear in parameters [46], which requires a set of model-specific basis functions.…”

Section: Preliminaries: Neural Networkmentioning

confidence: 99%

“…Recently, a robust adaptive neural tracking control with integral BLF [27] is proposed in [28] with integral BLFs for a class of SISO strict-feedback nonlinear systems under state constraints while in [29], adaptive control subject to full state constraints is achieved for a more general SISO pure-feedback nonlinear system, with simulation results on a single-link robot. To tackle the uncertainties in multi-inputmulti-output (MIMO) systems like robots, neural networks [30], [31] or fuzzy logic systems (FLS) [32], [33], [34] are employed as online model-free approximators. In [35], an adaptive fault tolerant control is derived for a class of input and state constrained MIMO nonlinear systems, where the state constraints are formulated as a constraint of the state vector's norm.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control of Robotic Manipulators With Unified Motion Constraints

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-In this paper, we present an adaptive control of robotic manipulators with parametric uncertainties and motion constraints. Position and velocity constraints are considered and they are unified and converted into the constraint of the nominal input. An adaptive neural network control is developed to achieve trajectory tracking, while the problems of motion constraints are addressed by considering the saturation effect of the nominal input. The uniform boundedness of all closed-loop signals is verified through Lyapunov analysis. Simulation and experiment results on a 2 DOF robotic manipulator demonstrate the effectiveness of the proposed method.

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“…For the problem of unknown model of vehicle engine torque, unknown input observer and adaptive parameters estimation are proposed in [18]. Many other results have shown that neural network (NN) [19][20][21][22][23] and fuzzy logic systems (FLSs) are universal approximators [24][25][26] with online learning capability to emulate complicated nonlinearity and uncertain functions. For an uncertain three-link flexiblejoint electrically driven manipulator with complex nonlinear functions, an adaptation algorithms observer combining with NNs is used to estimate the uncertain information such as the link and actuator velocity in systems in [27]; a link position tracking controller with estimated states is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Because FLSs have high interpretability of rich expert experience comprised with NNs, many researchers incline to deal with uncertainties terms in systems by using FLSs combining with the adaptation algorithms technology. From a mathematical point of view, the fuzzy adaptation algorithms control ideas in [24,25,32] depend on the output of fuzzy logic systems, mainly represented as a linear basis function, and the combinatorial coefficients can be adjusted automatically online. On one hand, many fuzzy rules must be used in order to improve the accuracy of approximation, so great rules are used for this intent.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Adaptation Algorithms’ Control for Robot Manipulators with Uncertainty Modelling Errors

2018

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A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators' system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy logic system with updated parameter laws, and can be formed for a new fashioned adaptation algorithms controller. The error closed-loop dynamical system can be stabilized based on Lyapunov analysis, the number of online learning computation burdens can be reduced greatly, and the different kinds of fuzzy logic systems with fuzzy rules or without any fuzzy rules are also suited. Finally, effectiveness of the proposed approach has been shown in simulation example.

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Adaptive Visual Tracking Control for Manipulator With Actuator Fuzzy Dead-Zone Constraint and Unmodeled Dynamic

Cited by 94 publications

References 53 publications

Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

Adaptive Control of Robotic Manipulators With Unified Motion Constraints

Fuzzy Adaptation Algorithms’ Control for Robot Manipulators with Uncertainty Modelling Errors

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