This paper considers the distributed attitude synchronization problem for multiple spacecraft with external disturbances. Distributed discontinuous adaptive controller is designed based on the relative attitudes and a...
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ISBN:
(纸本)9781467355322
This paper considers the distributed attitude synchronization problem for multiple spacecraft with external disturbances. Distributed discontinuous adaptive controller is designed based on the relative attitudes and angular velocities of neighboring spacecraft and attitude synchronization can be reached for any communication graph containing a directed spanning tree. To tackle the chattering effect caused by the discontinuous controller, a continuous controller is further proposed, under which both the synchronization errors and the adaptive gains are ultimately bounded. Extensions to the case with a leader-follower communication graph with the leader as the spanning tree's root are further studied.
Electro-hydraulic servo systems(EHSSs) have been widely used in industrial and military applications for their high power-to-size ratio and the ability to supply huge ***,precise control of EHSSs cannot be easily obta...
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ISBN:
(纸本)9781479900305
Electro-hydraulic servo systems(EHSSs) have been widely used in industrial and military applications for their high power-to-size ratio and the ability to supply huge ***,precise control of EHSSs cannot be easily obtained due to their inner nonlinearity and parameter *** load is another factor to decrease the tracking performance of *** adaptive robust control(IARC) was proposed to improve the tracking performance of EHSS,but due to the poor parameter adapting speed,IARC can be further improved to have better *** projection type parameter estimation algorithm is redesigned to increase the adapting speed when parameter is changed.A fast adaptive robust control (FARC) is then proposed to speed up the parameter adapting speed,so that a better tracking performance of FARC is maintained. Simulation results show that the proposed FARC gives an improved tracking performance and a faster parameter adaptation.
This paper considers the distributed attitude tracking problem of multiple spacecraft with disturbances and unknown inertia matrices. Based on the relative attitudes and angular velocities of neighboring spacecraft, w...
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ISBN:
(纸本)9781479900305
This paper considers the distributed attitude tracking problem of multiple spacecraft with disturbances and unknown inertia matrices. Based on the relative attitudes and angular velocities of neighboring spacecraft, we design a distributed robust controller to each follower to guarantee that the attitude errors between the followers and the leader converge to zero for any communication graph containing a directed spanning tree with the leader as the root. To deal with the problem of parameters uncertainties, we further propose a distributed adaptive sliding mode controller, under which the system is globally robust with respect to external disturbances and inertia matrices uncertainties.
The behavior patterns and strategies of Internet Water Army in online forums are investigated in this paper. Internet Water Army focuses on the controlling and steering of cyber collective opinions, and adjusts their ...
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In this paper, we consider continuous-state systems and pursue a near-optimal policy through online learning. A new online reinforcement learning algorithm-MSEC (Multi-Samples in Each Cell) is proposed. The proposed a...
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In this paper, we consider continuous-state systems and pursue a near-optimal policy through online learning. A new online reinforcement learning algorithm-MSEC (Multi-Samples in Each Cell) is proposed. The proposed algorithm combines state aggregation technique and efficient exploration principle, making high utilization of samples observed online. More concretely, we apply a grid over the continuous state space and partition it into different cells. Then, a near-upper Q iteration operator is defined to use samples in each cell and produce a near-upper Q function, whose corresponding greedy policy is efficient for exploration. MSEC is a totally model-free algorithm, which means no system dynamics is required during the implementation. It collects the system knowledge during the online learning. Based on PAC (Probability Approximately Correct) principle, MSCE can find a near-optimal policy in finite time bound online. To test the performance, an inverted pendulum is simulated and the results show the new algorithm is qualified for solving online optimal control problems.
Finite-time controlsystems usually have better disturbance rejection property and faster convergence performance. For the linearized dynamics of the linear navigation system of an agricultural tractor, a novel contro...
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This paper mainly provides and develops a new continuous-discrete PSO algorithm for handling with the optimal formation problem in the three dimensional space. For one class of formation problem with the particular co...
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ISBN:
(纸本)9781479937097
This paper mainly provides and develops a new continuous-discrete PSO algorithm for handling with the optimal formation problem in the three dimensional space. For one class of formation problem with the particular constraints, it is shown that the center of the desired shape is determined and equal to the center of the initial shape by utilizing the Lagrangian method. From the perspective of efficiency, the main task of the continuous-discrete PSO algorithm, short for CDPSO, is to search for some key parameters and to minimize the distance of all robots from the initial shape to the desired shape. To demonstrate the effectiveness of the new CDPSO algorithm, numerical results chiefly concentrate on the optimal helicopters formation from the initial shape to the desired shape in the three dimensional space and the typical shape conversion from the two-dimensional space to the three-dimensional space.
In this paper, an optimal tracking control scheme is proposed for a class of discrete-time chaotic systems using the approximation-error-based adaptive dynamic programming (ADP) algorithm. Via the system transformat...
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In this paper, an optimal tracking control scheme is proposed for a class of discrete-time chaotic systems using the approximation-error-based adaptive dynamic programming (ADP) algorithm. Via the system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then the novel optimal tracking control method is proposed. It is shown that for the iterative ADP algorithm with finite approximation error, the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions under some convergence conditions. Two examples are given to demonstrate the validity of the proposed optimal tracking control scheme for chaotic systems.
Repetitive activities of daily living (ADL) and robotic active training are commonly practised in the rehabilitation of paralyzed patients, both of which have been proven rather effective to recover the locomotor func...
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Repetitive activities of daily living (ADL) and robotic active training are commonly practised in the rehabilitation of paralyzed patients, both of which have been proven rather effective to recover the locomotor function of impaired limbs. ADL classification based on electroencephalogram (EEG) is of great significance to perform active robotic rehabilitation for patients with complete spinal cord injury (SCI) who lose locomotion of affected limbs absolutely, where surface electromyography (sEMG) or active force signal can hardly be detected. It is a challenge to achieve a satisfying result in neuro-rehabilitation robotics using EEG signals due to the high randomness of the EEG data. A classification method is proposed based on spiking neural networks (SNN) to identify the upper-limb ADL of three classes with 14-channel EEG data. The continuous real-number signals are firstly encoded into spike trains through Ben's Spike Algorithm (BSA). The generated spikes are then submitted into a 3-D brain-mapped SNN reservoir called NeuCube trained by Spike Timing Dependant Plasticity (STDP). Spike trains from all neurons of the trained reservoir are finally classified using one version of dynamic evolving spiking neuron networks (deSNN) - deSNNs. Classifications are presented with and without NeuCube respectively on the same EEG data set. Results indicate that using the reservoir improves identification accuracy which turns out pretty promising despite that EEG data is highly noisy, low frequently sampled, and only from 14 channels. The classification technique reveals a great potential for the further implementation of active robotic rehabilitation to the sufferers of complete SCI.
作者:
Xia, YuanqingLiu, BoSchool of Automation
Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing 100081 China
Summary This paper is devoted to the detection of abrupt changes for multiple-input, multiple-output (MIMO) linear systems based on frequency domain data. The real discrete-time Fourier transform is used to map the me...
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