135 research outputs found
A fractional model of the friction-temperature behavior in robot joints
No full textIt is well-known that friction changes with the temperature in robotic joints, and the temperature depends on the action being performed. In this paper we present a fractional model that describes the relationship between power generation/loss and temperature in the joints of industrial robots. The proposed mathematical model is used to predict friction variation during the robot working cycle without using temperature sensors. Several experimental tests have been performed on a commercial 6 degree-of-freedom anthropomorphic manipulator. Results confirm that the proposed model is able to estimate the behavior of the friction in the joints during robot working cycles. This can be used to compensate for friction, thus improving the control performance, and to predict energy consumption
Should I try saltbush for a cover crop?
No full textThe article discusses the use of saltbrush as a mid-row cover crop as done in the "Soil Management for Yield and Quality" project in Australia. Advantages include the improvement of soil through reduced tillage and the enhancement of biodiversity through the growth of a dense, prostrate ground cover with a long flowering window. The Nuriootpa and Loxton project sites were established from direct sown seed. In the Murray Valley, germination was prevented by drought conditions. Saltbrush species also provided habitat for beneficial invertebrates.Chris Penfol
Optimal fleet composition via dynamic programming and golden section search
No full textIn this paper, we consider an optimization problem arising in vehicle fleet management. The problem is to construct a heterogeneous vehicle fleet in such a way that cost is minimized subject to a constraint on the overall fleet size. The cost function incorporates fixed and variable costs associated with the fleet, as well as hiring costs that are incurred when vehicle requirements exceed fleet capacity. We first consider the simple case when there is only one type of vehicle. We show that in this case the cost function is convex, and thus the problem can be solved efficiently using the well-known golden section method. We then devise an algorithm, based on dynamic programming and the golden section method, for solving the general problem in which there are multiple vehicle types. We conclude the paper with some simulation results
A gradient-based parameter identification method for time-delay chaotic systems
No full textIn this paper, the parameter identification problem for a general class of time-delay chaotic systems is considered. The objective of the problem is to determine optimal values for an unknown time-delay and unknown system parameters such that the dynamic model of the system best fits given experimental data. We propose a gradient-based optimization algorithm to solve this problem, where accurate values for the partial derivatives of the error function are obtained by solving a set of auxiliary time-delay systems. Simulation results for two example problems show that the proposed algorithm is robust and efficient
Global optimization method for continuous - Time sensor scheduling
No full textWe consider a situation in which several sensors are used to collect data for signal processing since operating multiple sensors simultaneously canses system interference, only one sensor can be active at any one time.The problem of scheduling a discrete-valued optimal control problem.This problem cannot be solved using conventional optimization problem.The Transformed problem is then decomposed into a bi-level optimization problem, which is solved using a discreate filled function method in conjunction with a conventional optimal control algorithm.Numerical results show that our algorithm is robust, efficient, and reliable in attaining a near globally optimal solution
Optimal state feedback for constrained nonlinear systems
No full textIn this paper, we consider a general nonlinear control system that is subject to both terminal state and continuous inequality constraints. The continuous inequality constraints must be satisfied at every point in the time horizon—an infinite number of points. Our aim is to design an optimal feedback controller that yields efficient system performance and satisfaction of all constraints. We first formulate this problem as a semi-infinite optimization problem. We then show that, by using a novel exact penalty approach, this semi-infinite optimization problem can be converted into a sequence of nonlinear programming problems, each of which can be solved using standard numerical techniques. We conclude the paper with some convergence results
Optimal control problems involving constrained, switched, and delay systems
No full textIn this thesis, we develop numerical methods for solving five nonstandard optimal control problems. The main idea of each method is to reformulate the optimal control problem as, or approximate it by, a nonlinear programming problem. The decision variables in this nonlinear programming problem influence its cost function (and constraints, if it has any) implicitly through the dynamic system. Hence, deriving the gradient of the cost and the constraint functions is a difficult task. A major focus of this thesis is on developing methods for computing these gradients. These methods can then be used in conjunction with a gradient-based optimization technique to solve the optimal control problem efficiently.The first optimal control problem that we consider has nonlinear inequality constraints that depend on the state at two or more discrete time points. These time points are decision variables that, together with a control function, should be chosen in an optimal manner. To tackle this problem, we first approximate the control by a piecewise constant function whose values and switching times (the times at which it changes value) are decision variables. We then apply a novel time-scaling transformation that maps the switching times to fixed points in a new time horizon. This yields an approximate dynamic optimization problem with a finite number of decision variables. We develop a new algorithm, which involves integrating an auxiliary dynamic system forward in time, for computing the gradient of the cost and constraints in this approximate problem.The second optimal control problem that we consider has nonlinear continuous inequality constraints. These constraints restrict both the state and the control at every point in the time horizon. As with the first problem, we approximate the control by a piecewise constant function and then transform the time variable. This yields an approximate semi-infinite programming problem, which can be solved using a penalty function algorithm. A solution of this problem immediately furnishes a suboptimal control for the original optimal control problem. By repeatedly increasing the number of parameters used in the approximation, we can generate a sequence of suboptimal controls. Our main result shows that the cost of these suboptimal controls converges to the minimum cost.The third optimal control problem that we consider is an applied problem from electrical engineering. Its aim is to determine an optimal operating scheme for a switchedcapacitor DC-DC power converter—an electronic device that transforms one DC voltage into another by periodically switching between several circuit topologies. Specifically, the optimal control problem is to choose the times at which the topology switches occur so that the output voltage ripple is minimized and the load regulation is maximized. This problem is governed by a switched system with linear subsystems (each subsystem models one of the power converter’s topologies). Moreover, its cost function is non-smooth. By introducing an auxiliary dynamic system and transforming the time variable (so that the topology switching times become fixed), we derive an equivalent semi-infinite programming problem. This semi-infinite programming problem, like the one that approximates the continuously-constrained optimal control problem, can be solved using a penalty function algorithm.The fourth optimal control problem that we consider involves a general switched system, which includes the model of a switched-capacitor DC-DC power converter as a special case. This switched system evolves by switching between several subsystems of nonlinear ordinary differential equations. Furthermore, each subsystem switch is accompanied by an instantaneous change in the state. These instantaneous changes—so-called state jumps—are influenced by control variables that, together with the subsystem switching times, should be selected in an optimal manner. As with the previous optimal control problems, we tackle this problem by transforming the time variable to obtain an equivalent problem in which the switching times are fixed. However, the functions governing the state jumps in this new problem are discontinuous. To overcome this difficulty, we introduce an approximate problem whose state jumps are governed by smooth functions. This approximate problem can be solved using a nonlinear programming algorithm. We prove an important convergence result that links the approximate problem’s solution with the original problem’s solution.The final optimal control problem that we consider is a parameter identification problem. The aim of this problem is to use given experimental data to identify unknown state-delays in a nonlinear delay-differential system. More precisely, the optimal control problem involves choosing the state-delays to minimize a cost function measuring the discrepancy between predicted and observed system output. We show that the gradient of this cost function can be computed by solving an auxiliary delay-differential system. On the basis of this result, the optimal control problem can be formulated—and hence solved—as a standard nonlinear programming problem
Optimal investment and proportional reinsurance with risk constraint
No full textAuthor name used in this publication: Yiu, Ka Fai Cedric.2013-2014 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishedC
An exact cutting plane method for the Euclidean max-sum diversity problem
No full textThis paper aims to answer an open question recently posed in the literature, that is to find a fast exact method for solving the max-sum diversity problem, a nonconcave quadratic binary maximization problem. We show that, for Euclidean max-sum diversity problems (EMSDP), the distance matrix defining the quadratic term is always conditionally negative definite. This interesting property ensures that the cutting plane method is exact for (EMSDP), even in the absence of concavity. As such, the cutting plane method, which is primarily designed for concave maximisation problems, converges to the optimal solution of (EMDSP). The method was evaluated on several standard benchmark test sets, where it was shown to outperform other exact solution methods for (EMSDP), and is capable of solving two-coordinate problems of up to eighty-five thousand variables
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