This paper discusses the energy minimization problem of a class of chaotic systems, and constructs an optimal neuro-controller based on adaptive dynamic programming (ADP) algorithm. To learn the optimal performance in...
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An object grasping method based on fuzzy approaching for a mobile manipulator with an Eye-in-Hand CMOS (complementary metal-oxide-semiconductor transistor) camera was proposed. The approaching guidance identifier with...
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An object grasping method based on fuzzy approaching for a mobile manipulator with an Eye-in-Hand CMOS (complementary metal-oxide-semiconductor transistor) camera was proposed. The approaching guidance identifier with double cross was recognized by the mobile manipulator, and the angle between the line from mobile platform to the center of the identifier and its heading direction were served as the inputs of fuzzy controller. A double input and single output fuzzy controller was designed to regulate the direction of the mobile platform for smooth approaching. When the object (a red cylinder with double black lines) was in the workspace of manipulator, the mobile platform stops and the object was grasped by the manipulator with its joint angles solved based on inverse kinematics. Experiments results show the validity of the proposed approach.
A motion control method for mobile robot based on sub-regions evaluation in local sensing environment was proposed. Firstly, the local environment was divided into multiple sub-regions based on the sensing information...
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A motion control method for mobile robot based on sub-regions evaluation in local sensing environment was proposed. Firstly, the local environment was divided into multiple sub-regions based on the sensing information provided by laser range finder. Then, each sub-region was evaluated by considering the distance influence factor, as well as visual guide and memory-based judgment. Finally, the optimal sub-region adapted to current environment will be used for motion decision of mobile robot. Experimental results show that the proposed method is capable of dividing sensing environment into multiple sub-regions and producing the optimal sub-region adapted to current environment. Motion control for robot was realized and the effectiveness of the proposed method was verified.
A robotic fish propelling itself by two long fins was designed and implemented from the inspiration of stingrays. The yaw control of this robotic fish was studied. The dynamic model was studied based on the stress ana...
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A robotic fish propelling itself by two long fins was designed and implemented from the inspiration of stingrays. The yaw control of this robotic fish was studied. The dynamic model was studied based on the stress analysis of the robotic fish. A yaw controller was designed based on the active disturbance rejection control (ADRC) technique. The model error and external disturbance were unified into a total system disturbance, which was estimated by an extended state observer. By compensating the total system disturbance in the control signal, the yaw control system was simplified to a typical second order cascade integral system which could be easily controlled by use of a more effective nonlinear feedback control. The ADRC based yaw controller was verified through simulations showing that the designed controller can effectively control the yaw angle of the robotic fish and has good dynamic and static characteristics.
One of the most important issues among active rehabilitation technique is how to extract the voluntary intention of patient through bio-signals, especially EEG signal. This pilot study investigates the feasibility of ...
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In this paper, a novel adaptive dynamic programming algorithm based on policy iteration is developed to solve online multi-player non-zero-sum differential game for continuous-time nonlinear systems. This algorithm is...
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In this paper, a novel adaptive dynamic programming algorithm based on policy iteration is developed to solve online multi-player non-zero-sum differential game for continuous-time nonlinear systems. This algorithm is mathematically equivalent to the quasi-Newton's iteration in a Banach space. The implementation using neural networks is given, where a critic neural network is used to learn its value function, and an action neural network sharing the same parameters with the corresponding critic neural network is used to learn its optimal control policy for each player. All the critic and action neural networks are updated online in real-time and continuously. A simulation example is presented to demonstrate the effectiveness of the developed scheme.
In this paper, a new self-learning method using policy iterative adaptive dynamic programming (ADP) is developed to obtain the optimal control scheme of discrete-time nonlinear systems. The iterative ADP algorithm per...
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In this paper, a new self-learning method using policy iterative adaptive dynamic programming (ADP) is developed to obtain the optimal control scheme of discrete-time nonlinear systems. The iterative ADP algorithm permits an arbitrary admissible control law to initialize the iterative algorithm. It is the first time that the properties of the policy iterative ADP are established for the discrete-time situation. It proves that the iterative performance index function is non-increasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman (HJB) equation. It also proves that any of the iterative control policy can stabilize the nonlinear systems. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, a simulation example is given to illustrate the performance of the present method.
In the recent years haptic interfaces became a reliable solution in order to solve problems which arise when humans interact with the environment. If in the research area of the haptic interaction between human and en...
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In the recent years haptic interfaces became a reliable solution in order to solve problems which arise when humans interact with the environment. If in the research area of the haptic interaction between human and environment there are important researches, a innovative approach for the interaction between the robot and the environment using haptic interfaces and virtual projection method is presented in this paper. In order to control this interaction we used the Virtual Projection Method where haptic control interfaces of impedance and admittance will be embedded. The obtained results, validated by simulations assure stability, stiffness, high maneuverability and adaptability for rescue walking robots in order to move in disaster, dangerous and hazardous areas.
This paper develops an adaptive optimal control for the infinite-horizon cost of unknown nonaffine nonlinear continuous-time systems with control constraints. A recurrent neural network (NN) is constructed to identify...
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This paper develops an adaptive optimal control for the infinite-horizon cost of unknown nonaffine nonlinear continuous-time systems with control constraints. A recurrent neural network (NN) is constructed to identify the unknown system dynamics with stability proof. Then, two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal value, respectively. By using this architecture, the action NN and the critic NN are tuned simultaneously, without the requirement of the knowledge of system dynamics. In addition, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded based on Lyapunov's direct method. A simulation example is provided to verify the effectiveness of the developed theoretical results.
In this paper, we present the theory of online sparse least squares support vector machine (OS-LSSVM) for prediction and propose a predictor with OS-LSSVM to detect sensor fault. The principle of the predictor and its...
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