The stabilization problem of linear time-varying systems with both state and input constraints is considered. Sufficient conditions for the existence of the solution to this problem are derived and a gain-switched（ga...The stabilization problem of linear time-varying systems with both state and input constraints is considered. Sufficient conditions for the existence of the solution to this problem are derived and a gain-switched（gain-scheduled） state feedback control scheme is built to stabilize the constrained timevarying system. The design problem is transformed to a series of convex feasibility problems which can be solved efficiently. A design example is given to illustrate the effect of the proposed algorithm.展开更多
In order to deal with the chattering of rudder angle and the problem of non-convex attainable thrust regions,introduce the concept of dynamic attainable region for each thruster and rudder to limit the thruster rotati...In order to deal with the chattering of rudder angle and the problem of non-convex attainable thrust regions,introduce the concept of dynamic attainable region for each thruster and rudder to limit the thruster rotational speed and the rudder angle,and decompose the thrust allocation optimization problem into several optimization sub-problems.The optimization sub-problems were solved by particle swarm optimization(PSO) algorithm.Simulation studies with comparisons on a model ship were carried out to illustrate the effectiveness of the proposed thrust allocation optimization method.展开更多
By utilizing the improvement function,we change the nonsmooth convex constrained optimization into an unconstrained optimization,and construct an infeasible quasi-Newton bundle method with proximal form.It should be n...By utilizing the improvement function,we change the nonsmooth convex constrained optimization into an unconstrained optimization,and construct an infeasible quasi-Newton bundle method with proximal form.It should be noted that the objective function being minimized in unconstrained optimization subproblem may vary along the iterations(it does not change if the null step is made,otherwise it is updated to a new function).It is necessary to make some adjustment in order to obtain the convergence result.We employ the main idea of infeasible bundle method of Sagastizabal and Solodov,and under the circumstances that each iteration point may be infeasible for primal problem,we prove that each cluster point of the sequence generated by the proposed algorithm is the optimal solution to the original problem.Furthermore,for BFGS quasi-Newton algorithm with strong convex objective function,we obtain the condition which guarantees the boundedness of quasi-Newton matrices and the R-linear convergence of the iteration points.展开更多
基金supported by the National Natural Science Foundation of China(6132106261503100)the China Postdoctoral Science Foundation(2014M550189)
文摘The stabilization problem of linear time-varying systems with both state and input constraints is considered. Sufficient conditions for the existence of the solution to this problem are derived and a gain-switched（gain-scheduled） state feedback control scheme is built to stabilize the constrained timevarying system. The design problem is transformed to a series of convex feasibility problems which can be solved efficiently. A design example is given to illustrate the effect of the proposed algorithm.
基金National Natural Science Foundations of China(Nos.51579026,51079013)Program for Excellent Talents in Universities of Liaoning,China(No.LR2015007)+1 种基金Project of Resource and Social Security of Ministry of Human Province,ChinaFundamental Research Funds for the Central Universities,China(No.3132016020)
文摘In order to deal with the chattering of rudder angle and the problem of non-convex attainable thrust regions,introduce the concept of dynamic attainable region for each thruster and rudder to limit the thruster rotational speed and the rudder angle,and decompose the thrust allocation optimization problem into several optimization sub-problems.The optimization sub-problems were solved by particle swarm optimization(PSO) algorithm.Simulation studies with comparisons on a model ship were carried out to illustrate the effectiveness of the proposed thrust allocation optimization method.
文摘By utilizing the improvement function,we change the nonsmooth convex constrained optimization into an unconstrained optimization,and construct an infeasible quasi-Newton bundle method with proximal form.It should be noted that the objective function being minimized in unconstrained optimization subproblem may vary along the iterations(it does not change if the null step is made,otherwise it is updated to a new function).It is necessary to make some adjustment in order to obtain the convergence result.We employ the main idea of infeasible bundle method of Sagastizabal and Solodov,and under the circumstances that each iteration point may be infeasible for primal problem,we prove that each cluster point of the sequence generated by the proposed algorithm is the optimal solution to the original problem.Furthermore,for BFGS quasi-Newton algorithm with strong convex objective function,we obtain the condition which guarantees the boundedness of quasi-Newton matrices and the R-linear convergence of the iteration points.
