An enhanced control parameterization technique with variable switching times for constrained optimal control problems with control-dependent time-delayed arguments and discrete time-delayed arguments

Yu, Changjun; Wong, K. H.

doi:10.1016/j.cam.2023.115106

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…The CVP (control vector parametrization method) [37][38][39][40][41][42] is adopted to convert continuous slow-time-varying full-cycle optimization variables into several discrete parameter variables, which helps in solving the optimization model. In recent years, alternative and improved methods of CVP are still playing an important role in dynamic optimization of complex nonlinear models, such as an alternative method based on Fourier series for the control vector parameterization [43], an improved method to generate optimal evaluation for two objectives [44], a nonuniform method with time grid refinement [45], and an alternative method with control-dependent time-delayed arguments [46]. Using this method, the full-cycle economic benefit curve (Figure 4) is shown through the solutions of Model 4 (including the curve before the operation cycle remains unchanged).…”

Section: Model 3 Minmentioning

confidence: 99%

Process Scheduling Analysis and Dynamic Optimization Maintaining the Operation Margin for the Acetylene Hydrogenation Fixed-Bed Reactor

Xie,

Luo

2023

Processes

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The full-cycle operation optimization of the acetylene hydrogenation reactor should strictly adhere to the operation optimization scheme within the operation cycle, regardless of scheduling changes. However, in actual industrial processes, in order to meet temporary process scheduling requirements, the acetylene hydrogenation reactor needs to adjust its operation strategy temporarily within the remaining operation cycle based on the results of dynamic optimization for a certain period. It brings additional challenges and a research gap to the operational optimization problem. To make up for this research gap, this paper focuses on researching a type of full-cycle dynamic optimization problem where the operation optimization scheme is temporarily adjusted during the operation cycle. The methods employed for changing the operation optimization scheme include modifying the operation cycle, maximizing economic benefits, and altering the optimization goal to maximize the operation cycle. A novelty full-cycle scheduling optimization framework based on surplus margin estimate is proposed to build a platform for these methods. The paper analyzes the impact of process scheduling changes on full-cycle optimization using a dynamic optimization model that maintains the operation margin. It establishes a full-cycle scheduling optimization model and obtains the optimal scheduling strategy by a novelty method NSGBD (non-convex sensitivity-based generalized Benders decomposition). In this process, an adaptive CVP (control vector parameterization) based on a decomposition optimization algorithm is proposed, which tackles the challenge of optimizing complex acetylene hydrogenation reactor models on a large time scale. Scheduling optimization can be realized as an annualized benefit of 1.56 × 106 and 1.57 × 106 ¥ separately within two scheduling optimization constraints, and the computational time required is much less than previous operational optimizations.

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Section: Model 3 Minmentioning

confidence: 99%

Process Scheduling Analysis and Dynamic Optimization Maintaining the Operation Margin for the Acetylene Hydrogenation Fixed-Bed Reactor

Xie,

Luo

2023

Processes

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Dynamic optimal control of flow front position in injection molding process: A control parameterization-based method

Ren,

Lin,

et al. 2023

Journal of Process Control

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Sequential adaptive switching time optimization technique for maximum hands-off control problems

Lin,

Meng,

Yuan

et al. 2024

era

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<abstract><p>In this paper, we consider maximum hands-off control problem governed by a nonlinear dynamical system, where the maximum hands-off control constraint is characterized by an $ L^{0} $ norm. For this problem, we first approximate the $ L^{0} $ norm constraint by a $ L^{1} $ norm constraint. Then, the control parameterization together with sequential adaptive switching time optimization technique is proposed to approximate the optimal control problem by a sequence of finite-dimensional optimization problems. Furthermore, a smoothing technique is exploited to approximate the non-smooth maximum operator and an error analysis is investigated for this approximation. The gradients of the cost functional with respect to the decision variables in the approximate problem are derived. On the basis of these results, we develop a gradient-based optimization algorithm to solve the resulting optimization problem. Finally, an example is solved to demonstrate the effectiveness of the proposed algorithm.</p></abstract>

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An enhanced control parameterization technique with variable switching times for constrained optimal control problems with control-dependent time-delayed arguments and discrete time-delayed arguments

Cited by 4 publications

References 28 publications

Process Scheduling Analysis and Dynamic Optimization Maintaining the Operation Margin for the Acetylene Hydrogenation Fixed-Bed Reactor

Process Scheduling Analysis and Dynamic Optimization Maintaining the Operation Margin for the Acetylene Hydrogenation Fixed-Bed Reactor

Dynamic optimal control of flow front position in injection molding process: A control parameterization-based method

Sequential adaptive switching time optimization technique for maximum hands-off control problems

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