1,721,010 research outputs found
GRASP in Switching Input Optimal Control Synthesis
No full textIn this paper, several optimal control problems are introduced, referred to the switching input control scheme. The system is characterized by several control variables and at any time only one of them can be acted. At any time, the control decision involves the input variable to be activated and its value. Under suitable assumption about both the finite time and the infinite time but periodic case, the optimal control can be computed as standard feedback law, once the switching policy is defined. The problem turns in a high dimension discrete problem and a search procedure can be used to solve it. Here we apply the meta-heuristic Greedy Randomized Search Procedure with good results
GRASP in Switching Input Optimal Control Synthesis
No full textIn this paper, several optimal control problems are introduced, referred to the switching input control scheme. The system is characterized by several control variables and at any time only one of them can be acted. At any time, the control decision involves the input variable to be activated and its value. Under suitable assumption about both the finite time and the infinite time but periodic case, the optimal control can be computed as standard feedback law, once the switching policy is defined. The problem turns in a high dimension discrete problem and a search procedure can be used to solve it. Here we apply the meta-heuristic Greedy Randomized Search Procedure with good results
Using GRASP for choosing best periodic observation strategy in stochastic systems filtering
No full textThe problem of optimal periodic scheduling of single channel measures for the state estimation of a multi output discrete time stochastic system is considered. The optimality criterion chosen is the value of the trace of the error covariance matrix of Kalman filter in the periodic steady state, averaged over the observation period. Two interesting examples for practical applications, are studied. The first one considers the case of a number of independent single output subsystems observed by a single observation channel, while the second case deals with the optimization of measurement points and of the relative scanning sequence for the model of a parabolic distributed parameter system. Given the combinatorial nature of the resulting problem, an approximate global optimization method is used to solve it and heuristic rules are devised to overcome difficulties arising from possibly slow convergence in computation of objective function. Numerical examples are reported showing a great improvement with respect to the standard scanning policy
Automatic Discovery of Drug Mode of Action andDrug Repositioning from Gene Expression Data
No full textShortest paths in randomly time varying networks
No full textA dynamic single-source single-destination shortest path problem on a directed graph is considered. The edge lengths are not constant, but they change as a function of time in a random way. The problem is modeled as a multi-stage decision process and solved by using a re-optimization method that solves the ι.th stage using the results of the solution of the (ι-1).th one. An algorithm, called the dynamical shortest path algorithm, is proposed for solving the global problem either finding a solution or detecting that the problem is infeasible and an upper bound on its running time is found. Numerical examples are reported in order to show the effectiveness of the method
Graph collapsing in shortest path Auction algorithms
No full textIn this paper we consider the problem of finding a shortest path from a source node to a fixed target node (SSP) or to all the nodes (SPT) on a directed graph. A family of algorithms which derives from the known auction algorithm is introduced. The key feature of these algorithms is based on topological transformations operated on the graphs that replace an optimal sub-path with a single arc of the same length (graph collapsing concept). The same idea is applied both to the standard auction algorithm and to a modified version of the algorithm. In the last mentioned case a good saving in computation cost is obtained as shown by the reported numerical examples
Rendezvous of mobile robots in unknown environment via graph optimization approach
No full textThe problem of finding a path for the motion of two autonomous mobile robots from a starting point to meet the other robot in a two dimensional domain is considered in the presence of arbitrary shaped obstacles. No a priori information is known in advance about the geometry and the dimensions of the workspace nor about the number, extension and location of obstacles. The robots have a sensing device that detects all obstacles or pieces of walls lying beyond a fixed view range. The problem is embedded in a graph optimization framework and a new algorithm is proposed, characterized by little computer power requirements. Theoretical and practical aspects of the algorithm are investigated
Vehicle Routing Problem in Optimal Warehouse Management for FlexibleManufacturing Plants
No full textNetwork optimization for sensor based robotic navigation with moving obstacles
No full textThe problem of finding a path for the motion of a small mobile robot from a starting point to a fixed target in a two dimensional domain is considered in the presence of moving arbitrary shaped obstacles. No a priori information is known in advance about the geometry and the dimensions of the workspace nor about the number, extension and location of obstacles. The robot has a sensing device that detects all obstacles or pieces of walls lying beyond a fixed view range. A discrete version of the problem is solved by an iterative algorithm that at any iteration step finds the smallest path length from the actual point to the target with respect to the actual knowledge about the obstacles, then the robot is steered along the path until a new obstacle point interfering with the path is found, at this point a new iteration is started. Such an algorithm stops in a number of steps depending on the geometry, finding a solution for the problem or detecting that the problem is unfeasible. Since the algorithm must be applied on line, the effectiveness of the method depends strongly on the efficiency of the optimization step
Network optimization for sensor based robotic navigation with moving obstacles
No full textThe problem of finding a path for the motion of a small mobile robot from a starting point to a fixed target in a two dimensional domain is considered in the presence of moving arbitrary shaped obstacles. No a priori information is known in advance about the geometry and the dimensions of the workspace nor about the number, extension and location of obstacles. The robot has a sensing device that detects all obstacles or pieces of walls lying beyond a fixed view range. A discrete version of the problem is solved by an iterative algorithm that at any iteration step finds the smallest path length from the actual point to the target with respect to the actual knowledge about the obstacles, then the robot is steered along the path until a new obstacle point interfering with the path is found, at this point a new iteration is started. Such an algorithm stops in a number of steps depending on the geometry, finding a solution for the problem or detecting that the problem is unfeasible. Since the algorithm must be applied on line, the effectiveness of the method depends strongly on the efficiency of the optimization step
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