This paper considers the problem of formation control for mobile robots which are subject to input constraints. The control strategy based on virtual robot tracking is proposed, which leads to a compact controller for...
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This paper considers the problem of formation control for mobile robots which are subject to input constraints. The control strategy based on virtual robot tracking is proposed, which leads to a compact controller form in terms of two feedback parameters by Lyapunov method. The introduced elliptic approximation of input constraints brings much convenience to obtain suitable feedback parameters. It is show that the presented sufficient condition guarantees the existence of feasible feedback parameters. Moreover, guidelines for the selection of feedback parameters are provided with geometric analysis. Simulation results verify the effectiveness of the proposed control strategy.
Abstract In this paper, the relative position parameters of the target spacecraft are obtained by using the vision measurement and the target maneuver positions are calculated through the isochronous interpolation met...
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Abstract In this paper, the relative position parameters of the target spacecraft are obtained by using the vision measurement and the target maneuver positions are calculated through the isochronous interpolation method. Furthermore, new switch control laws under constant thrust are designed for active collision avoidance maneuver of the chaser along a specified trajectory. The switch control laws are obtained based on the acceleration sequences and the working times of thrusters in three axes which can be respectively computed by the time series analysis method. The perturbations and fuel consumptions are addressed during the computation of the working times. The simulation results show that the constant thrust maneuver can effectively guarantee the chaser maneuvering along the specified trajectory.
Abstract In this paper, a risk-sensitive filter for a class of jump Markov nonlinear systems is proposed. By using a set of weighted cubature points to approximate the intractable risk-sensitive recursions, the propos...
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Abstract In this paper, a risk-sensitive filter for a class of jump Markov nonlinear systems is proposed. By using a set of weighted cubature points to approximate the intractable risk-sensitive recursions, the proposed filter could exhibit improved numerical stability with respect to the derivation of the square-root covariance, for which the unscented transform technique might halt the operations due to the presence of non-positive definite covariance. The performance of the proposed filter is illustrated through a simulation study of tracking a land vehicle with maneuvers.
This work studies the identification and control of circulation fluidized bed (CFB) boilers. The CFB boiler under investigation shows strong nonlinearity due to big changes of steam load. A linear parameter varying (L...
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In this paper, an adaptive iterative learning control (ILC) scheme is proposed for trajectory tracking of uncertain delay systems based on model matching technique. The reference model is a delay system operating over...
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In this paper, an adaptive iterative learning control (ILC) scheme is proposed for trajectory tracking of uncertain delay systems based on model matching technique. The reference model is a delay system operating over in a finite time interval. An iterative model matching controller is designed and an iteration domain adaptive law is chosen to estimate the unknown parameters. It shows that the model matching technique can be applied in a straightforward method to ILC problem. A simulation example is included to illustrate the designed scheme.
This paper presents a Gaussian mixture probability hypothesis density (GM-PHD) smoother for tracking multiple maneuvering targets that follow jump Markov models. Unlike the generalization of the multiple model GM-PHD ...
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This paper presents a Gaussian mixture probability hypothesis density (GM-PHD) smoother for tracking multiple maneuvering targets that follow jump Markov models. Unlike the generalization of the multiple model GM-PHD filters, our aim is to approximate the dynamics of the linear Gaussian jump Markov system (LGJMS) by a best-fitting Gaussian (BFG) distribution so that the GM-PHD smoother can be carried out with respect to an approximated linear Gaussian system. Our approach is inspired by the recognition that the BFG approximation provides an accurate performance measure for the LGJMS. Furthermore, the multiple model estimation is avoided and less computational cost is required. The effectiveness of the proposed smoother is verified with a numerical simulation.
The goal of innovative education in engineering field is to cultivate excellent engineering talents with high quality, innovative spirit, innovative ability and innovative quality. With the wide application of microco...
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Minimum variance control is an established method in control of systems corrupted by noise. In these cases, as it is not possible to directly control the actual value of the system variables, one aims to reduce the va...
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This paper presents three different LQ controllers, which are used to attenuate rotor vibrations in an electrical motor. The controllers are compared in performance and control effort. A dynamic way of solving the rel...
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A new culture-based Particle Swarm Optimization (PSO) with mutation, CPSOM, is proposed in this paper to improve the overall optimization performance of the original PSO and combat with the well-known premature proble...
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