In this paper, we mainly propose a new parameter-free filled function, which derives from two inverse trigonometric functions. First, we accomplish the analytical studies, to prove that filled function proposed in thi...
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In this paper, we mainly propose a new parameter-free filled function, which derives from two inverse trigonometric functions. First, we accomplish the analytical studies, to prove that filled function proposed in this paper possess all the properties of filled function. Secondly, based on this parameter-free filled function, a comparatively new algorithm was built. Finally, we implemented the algorithm to solve unconstrained global optimization problems. The computational results demonstrated in this paper revealed the effectiveness of the proposed filled function compared to some related results of the filled functions in the literature.
The most important class of applied optimal control problems are time-optimal control problems. The linear speed limit problem for dynamic control objects with scalar input is discussed. By means of the Pontryagin max...
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Aiming at the reentry trajectory planning of hypersonic vehicle, considering the constraints of no fly zone in the process, the reentry guidance scheme is designed based on the adaptive Radau pseudospectrum method;Fir...
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With the development of control theory, people begin to apply it to practical projects, including nonlinear programming, neural networks, etc. This paper mainly studies the time delay optimization control algorithm ba...
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Distributed photovoltaics (DPVs) have been widely integrated into power systems due to their abundance, renewability and low cost, while the stochastic nature of DPVs imposes significant influence on the hosting capac...
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Distributed photovoltaics (DPVs) have been widely integrated into power systems due to their abundance, renewability and low cost, while the stochastic nature of DPVs imposes significant influence on the hosting capacity (HC) of DPVs. Here, the integrated electricity and heat system (IEHS) is explored to increase the deployment of DPVs in comparison to the power system by exploiting the interaction between electric and thermal energy. An MILP-based HC assessment model for DPVs of the IEHS is proposed to efficiently promote the penetration level of DPV generation in distribution networks considering the uncertainty of DPV generation. To reduce the computational burden for HC assessment, the linearisation method combined with the incremental formulations of the big-M method is developed to simplify the complex non-linear model of energy devices and networks in IEHS. A scenario-based uncertainty modelling approach is applied to characterise DPV uncertainty, which improves the accuracy of HC assessment for DPVs. Comprehensive case studies according to a 33-bus electric network and a 6-node heat network validate the superiority of the proposed assessment model.
We propose a new economic nonlinear model predictive control (eNMPC) formulation that tracks the optimality conditions of the real-time optimization problem rather than any specific steady states. The proposed formula...
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We propose a new economic nonlinear model predictive control (eNMPC) formulation that tracks the optimality conditions of the real-time optimization problem rather than any specific steady states. The proposed formulation maintains its nature of optimizing economic performance and assured stability properties with the Lyapunov inequality constraint for the closed-loop control. Under general assumptions, we prove that the proposed controller is asymptotically stable without process disturbances and is input-to-state stable when there is a process disturbance. The proposed eNMPC is demonstrated on two case studies and compared against setpoint-tracking NMPC with setpoints determined by the steady-state real-time optimizer to show improved dynamic performance. We also highlight the capability of self-stabilization of the new eNMPC with parameter updates in the process model.
Hybrid Unmanned Aerial Vehicles UAV are vehicles capable of take-off and landing vertically like helicopters while maintaining the long-range efficiency of fixed-wing aircraft. Unfortunately, due to their wing area, t...
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Hybrid Unmanned Aerial Vehicles UAV are vehicles capable of take-off and landing vertically like helicopters while maintaining the long-range efficiency of fixed-wing aircraft. Unfortunately, due to their wing area, these vehicles are sensitive to wind gusts when hovering. One way to increase the hovering wind-rejection capabilities of hybrid UAV is through the addition of extra actuators capable of directing the thrust of the rotors. Nevertheless, the ability to control UAVs with many actuators is strictly related to how well the Control Allocation problem is solved. Generally, to reduce the problem complexity, conventional (CA) methods make use of linearized control effectiveness in order to optimize the inputs that achieve a certain control objective. We show that this simplification can lead to oscillations if it is applied to thrust vectoring vehicles, with pronounced non-linear actuator effectiveness. When large control objectives are requested or actuators saturate, the linearized effectiveness based CA methods tend to compute a solution far away from the initial actuator state, invalidating the linearization. A potential solution could be to impose limits on the solution domain of the linearized CA algorithm. However, this solution only reduces the oscillations at the expense of a lag in the vehicle acceleration response. To overcome this limitation, we present a fully nonlinear CA method, which uses an Sequential Quadratic programming (SQP) algorithm to solve the CA problem. The method is tested and implemented on a single board computer that computes the actuator solution in real time onboard a dual axis tilting rotor quad-plane. Flight test experiments confirm the problem of severe oscillations in the linearized effectiveness CA algorithms and show how the only algorithm able to optimally solve the CA problem is the presented nonlinear method.
We consider what we term existence-constrained semi-infinite programs. They contain a finite number of (upper-level) variables, a regular objective, and semi-infinite existence constraints. These constraints assert th...
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We consider what we term existence-constrained semi-infinite programs. They contain a finite number of (upper-level) variables, a regular objective, and semi-infinite existence constraints. These constraints assert that for all (medial-level) variable values from a set of infinite cardinality, there must exist (lower-level) variable values from a second set that satisfy an inequality. Existence-constrained semi-infinite programs are a generalization of regular semi-infinite programs, possess three rather than two levels, and are found in a number of applications. Building on our previous work on the global solution of semi-infinite programs (Djelassi and Mitsos in J Glob Optim 68(2):227-253, 2017), we propose (for the first time) an algorithm for the global solution of existence-constrained semi-infinite programs absent any convexity or concavity assumptions. The algorithm is guaranteed to terminate with a globally optimal solution with guaranteed feasibility under assumptions that are similar to the ones made in the regular semi-infinite case. In particular, it is assumed that host sets are compact, defining functions are continuous, an appropriate global nonlinear programming subsolver is used, and that there exists a Slater point with respect to the semi-infinite existence constraints. A proof of finite termination is provided. Numerical results are provided for the solution of an adjustable robust design problem from the chemical engineering literature.
Engineering optimization problems can be always classified into two main categories including the linear programming(LP)and nonlinear programming(NLP)*** programming problem further involves the unconstrained conditio...
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Engineering optimization problems can be always classified into two main categories including the linear programming(LP)and nonlinear programming(NLP)*** programming problem further involves the unconstrained conditions and constrained conditions for design variables of the optimized *** paper will focus on the issue about the design problem of NLP with the constrained *** employed method for such NLP problems is a variant of particle swarm optimization(PSO),named improved particle swarm optimization(IPSO).The developed IPSO is to modify the velocity updating formula of the algorithm to enhance the search ability for given optimization *** this work,many different kinds of physical engineering optimization problems are examined and solved via the proposed IPSO *** results compared with various optimization methods reported in the literature will show the effectiveness and feasibility for solving NLP problems with the constrained conditions.
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