A chaotic particle swarm optimization (CPSO) algorithm is proposed by introducing chaos state into the original Particle Swarm Optimization (PSO) which aims to solving the flaws of easy plunging into local optimum and...
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ISBN:
(纸本)9781467355339
A chaotic particle swarm optimization (CPSO) algorithm is proposed by introducing chaos state into the original Particle Swarm Optimization (PSO) which aims to solving the flaws of easy plunging into local optimum and losing search ability in the last period for the fast particle velocity decrease. CPSO algorithm takes advantage of the ergodicity, randomicity, and regularity of chaos to make chaotic searching for the global extremun at the same time with the particle swarm optimization. This algorithm synthesizes the high efficiency of global optimization of PSO algorithm and the ergodicity and randomicity of local search of chaotic algorithm. This paper utilizes aforementioned algorithm to identify the Bouc-Wen hysteresis model for piezoelectric ceramic actuators (PCA). The experimental results show that the model identified by CPSO algorithm has better performance than that by PSO algorithm.
This paper presents observer-based fuzzy control for nonlinear fractional-order systems with the fractional order α satisfying 1 < α < 2 via fuzzy T-S models. Using the properties of the Kronecker product and ...
This paper presents observer-based fuzzy control for nonlinear fractional-order systems with the fractional order α satisfying 1 < α < 2 via fuzzy T-S models. Using the properties of the Kronecker product and LMI approach, the feedback and observer gain matrices are designed. By this method, the state of nonlinear system described as the fuzzy T-S model is convergent to the equilibrium and the observer error is convergent to zero. Finally, the simulation result of a numerical example is given to illustrate the effectiveness of this method.
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 *** principle of the predictor and its online a...
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ISBN:
(纸本)9781479900305
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 *** principle of the predictor and its online algorithm are *** with the traditional least squares support vector machine(LSSVM),OS-LSSVM has an advantage on training speed owing to the online training algorithm based on the base vector *** real-time output data of sensor is employed as the training vector to establish the regression *** method is compared with the LSSVM predictor in the *** typical faults of sensors are investigated and the simulation result indicates that the OS-LSSVM predictor can diagnose sensor fault accurately and rapidly,thus it is especially suitable for online sensor fault detection.
Considering the characteristics of manipulation and collimation control in traction artillery control system, complex mathematic model, uncertainty and nonlinearity, ADRC (Active Disturbance Rejection controller) with...
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In this paper, decentralized filtering of multiagent systems with coupling uncertainties is proposed and investigated. The considered multi-agent system is composed of many agents, each of which evolves with a discret...
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ISBN:
(纸本)9781467355339
In this paper, decentralized filtering of multiagent systems with coupling uncertainties is proposed and investigated. The considered multi-agent system is composed of many agents, each of which evolves with a discrete-time stochastic linear time-varying dynamics, and every agent can be locally influenced by its neighbor agents. Therefore the states evolution of each agent is not only related with its previous states but also related with its neighbors' previous states in the linear dynamic system. Communication limitations existing in the considered multi-agent system restrict that each agent can only observe its own measurements (outputs) and its neighbor agents' outputs while the states are invisible to any agent. Because of communication limitations and information constraints, without knowing the coupling gains of the local interactions, it is not easy for each agent to estimate its states by traditional kalman filter or other state observers, which were extensively discussed in the literature. In this preliminary study, for the considered coupled linear discrete-time multiagent system with uncertain linear local couplings, based on the key idea of state augmentation and the certainty-equivalence principle borrowed from the area of adaptive control, we propose an efficient decentralized kalman filtering scheme, for each agent, to simultaneously estimate the unknown states and coupling parameters, and extensive simulations are conducted, which have clearly verified the effectiveness of the proposed decentralized filtering scheme.
To make humanoid robots walking fast, it's important to improve driving force of their leg joints. Usually, each joint of humanoid robots is driven by a single motor. Dual-motor joint, on the other hand, is one of...
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ISBN:
(纸本)9781467355339
To make humanoid robots walking fast, it's important to improve driving force of their leg joints. Usually, each joint of humanoid robots is driven by a single motor. Dual-motor joint, on the other hand, is one of the candidate solutions to meet the power requirement needed for fast walking. This paper proposed a new dual-motor control model. In the model, two motors are treated as a single control plant instead of two parallel control plants. With the usage of current distributor, the control model can pump different current to each motor freely so as to eliminate the unbalance of the load imposed on each motor. Simulation and experiment show that the proposed model works well under high joint load and it can be used on a fast walking humanoid robot.
A biomimetic controller with online adaptation of impedance and force is applied to a full kinematic and dynamic model of the Baxter bimanual robot. A set of fuzzy logic engines are proposed to infer the values of tun...
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A biomimetic controller with online adaptation of impedance and force is applied to a full kinematic and dynamic model of the Baxter bimanual robot. A set of fuzzy logic engines are proposed to infer the values of tuning gains which affect the control performance and control effort of the controller, which would conventionally be set to a static value based on expert knowledge of the controller; the aim of this being to avoid the use of arbitary values to set these values. A simulated experiment is carried out, where the Baxter robot is required to move an object through a trajectory while subjected to two different disturbance forces in four phases. The controller with fuzzy inferred control gains is compared against the same controller with fixed gains to gauge the effectiveness of the new method. Results show that fuzzy inference of control gains impart an improvement in both tracking error and control effort.
With the increase of the capacity of PV generated systems, how to eliminate the problem caused by the randomness of power output for photovoltaic system becomes more significant. Most of the existing photovoltaic pred...
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For UGV, driving in the unstructured environment safely and quickly without human intervention becomes increasingly important. While many scholars have conducted researches in driving in this case, the results seem qu...
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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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