In this paper, a slack variable-based control variable parameterization (CVP) method is proposed for solving engineering dynamic optimization problems with inequality path constraints. An improved slack variable trans...
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ISBN:
(纸本)9781538635247
In this paper, a slack variable-based control variable parameterization (CVP) method is proposed for solving engineering dynamic optimization problems with inequality path constraints. An improved slack variable transform technique is introduced so that the original problem is converted into an unconstrained dynamic optimization problem (uDOP). No approximation error generated and the inequality path constraints are satisfied during the whole time intervals. A nonlinear programming (NLP) problem, which approximates the original dynamic optimization problem, is obtained by applying the control variable parameterization. The variational method is derived to calculate the gradients so that the transformed NLP problem can he easily solved. Test results show that the proposed method performs better than the penalty method in terms of computation accuracy and computation time.
This study introduces a hierarchical model predictive control (HMPC) algorithm utilizing multihorizon optimization for the energy management strategy (EMS) in connected fuel cell electric vehicles (FCEVs). The algorit...
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This study introduces a hierarchical model predictive control (HMPC) algorithm utilizing multihorizon optimization for the energy management strategy (EMS) in connected fuel cell electric vehicles (FCEVs). The algorithm leverages the benefits of both long horizon and short horizon decision-making, can solve problems globally, and ensures high computational efficiency. By combining the multihorizon optimization method, the upper layer plans the real-time reference state of charge (SOC) trajectory of the battery efficiently over the long shrinking horizon. In the lower layer, real-time energy optimization is performed with specific objectives such as hydrogen consumption, component lifespan, and following the reference SOC. Additionally, this paper focuses on a nonlinear MPC solution algorithm based on control variable parameterization (CVP). By parameterizing controlvariables, the number of control sequences to be solved within the prediction horizon is reduced. The algorithm demonstrates significant advantages in rapid solving. Through evaluation in hardware- in-the-loop (HIL) experiments, compared with the traditional MPC algorithm, the proposed algorithm exhibits cost savings ranging from 4.46% to 5.51%, and the computational efficiency of the real-time optimization is improved by 19.94%.
Many researchers have reported their methods to handle state variable path constraints in dynamic optimization problems. However, very few of them mentioned the techniques to handle controlvariable path constraints s...
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Many researchers have reported their methods to handle state variable path constraints in dynamic optimization problems. However, very few of them mentioned the techniques to handle controlvariable path constraints specifically. This paper firstly introduces two methods to deal with controlvariable path constraints, then presents a novel smoothed quadratic penalty function method that can solve dynamic optimization problems with both control and state variable path constraints at the same time. The control variable parameterization strategy is used to solve the resulting problems. Two separate cases are considered where testing of case1 demonstrates the characteristics of the three methods, while testing of Case 2 indicates the effectiveness and the superiority of the proposed smoothed penalty function method.
The near-space solar-powered unmanned aerial vehicle has broad prospects in application owing to its high altitude long-endurance performance. Launching solar-powered unmanned aerial vehicle into the near-space with b...
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The near-space solar-powered unmanned aerial vehicle has broad prospects in application owing to its high altitude long-endurance performance. Launching solar-powered unmanned aerial vehicle into the near-space with balloon-borne approach has advantages over the traditional sliding take-off methods, in that it is able to quickly and safely cross the turbulent zone. In this article, we investigate the control technology of balloon-borne launching for the solar-powered unmanned aerial vehicles. First, the motion of the launching process is divided into longitudinal and lateral-directional motion, with the longitudinal process and its equation addressed in detail. We then analyze the flight state and restriction conditions that the unmanned aerial vehicle should meet during the process. Second, the target variables and constraints are selected to formulate the optimization problem. The control variable parameterization method is applied to find the optimal pitch angle in the releasing-and-pulling process. More explicitly, a three-channel attitude stabilization controller is designed, in which the longitudinal channel takes the optimal pitch angle as the pitch instruction, the transverse channel carries out the zero control of the inclination angle, and the course channel takes the stabilization control, respectively. Numerical simulation results show that our proposed control design is capable of accelerating the solar-powered unmanned aerial vehicles from the vertical state and pulling them up to the horizontal cruising flight state, with the flight angle of attack, the maximum speed, and the maximum axial acceleration in the pulling process all within the designed range.
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