Parametric Controllers in Simultaneous Process and Control Design Optimization

The numerical method to solve simultaneous design and control optimization problems is considered. Firstly, the simultaneous optimization problem dealt with in this paper is defined, and this problem is transformed into a new formulated nonlinear programming problem. Considering that the nonlinear programming problem is solved by using a sequential quadratic programming method, the diagonal form of the Hessian matrix for the Lagrange function is introduced in a new formulation named Block Diagonal Hessian (BDH) method. Secondly, the BDH method is applied to the simultaneous spaceplane design and trajectory optimization problem, where the best size and its optimal trajectory are estimated to realize it. Through the several calculations, it is confirmed that the proposed BDH method is effective for simultaneous design and control optimization problems.

Real-world multibody systems are often subject to phenomena like friction, joint clearances, and external events. These phenomena can significantly impact the optimal design of the system and its controller. This work addresses the gradient-based optimization methodology for multibody dynamic systems with joint friction using a direct sensitivity approach. The Brown–McPhee model has been used to characterize the joint friction in the system. This model is suitable for the study due to its accuracy for dynamic simulation and its compatibility with sensitivity analysis. This novel methodology supports codesign of the multibody system and its controller, which is especially relevant for applications like robotics and servo-mechanical systems, where the actuation and design are highly dependent on each other. Numerical results are obtained using a software package written in Julia with state-of-the-art libraries for automatic differentiation and differential equations. Three case studies are provided to demonstrate the attractive properties of simultaneous optimal design and control approach for certain applications.

Simultaneous plant-controller design optimization of a two-link planar manipulator

<div>This article investigates the fuel savings potential of a series hybrid military truck using a simultaneous battery pack design and powertrain supervisory control optimization algorithm. The design optimization refers to the sizing of the lithium-ion battery pack in the hybrid configuration. The powertrain supervisory control optimization determines the most efficient way to split the power demand between the battery pack and the engine. Despite the available design and control optimization techniques, a generalized mathematical formulation and solution approach for combined design and control optimization is still missing in the literature. This article intends to fill that void by proposing a unified framework to simultaneously optimize both the battery pack size and power split control sequence. This is achieved through a combination of genetic algorithm (GA) and Pontryagin’s minimum principle (PMP) where the design parameters are integrated into the Hamiltonian function. As GA and PMP are global optimization methodologies under suitable conditions, the solution can be considered as a benchmark for the application under study. Five military drive cycles are used to evaluate the proposed approach. The simulation results show 5%-19% reduction in fuel consumption depending on the drive cycle compared to a baseline non-optimized case.</div>

The formulation and implementation of an optimization method called Simultaneous Aerodynamic Analysis and Design Optimization (SAADO) are extended from single discipline analysis (aerodynamics only) to multidisciplinary analysis - in this case, static aero-structural analysis - and applied to a simple 3-D wing problem. The method aims to reduce the computational expense incurred in performing shape optimization using state-of-the-art Computational Fluid Dynamics (CFD) flow analysis, Finite Element Method (FEM) structural analysis and sensitivity analysis tools. Results for this small problem show that the method reaches the same local optimum as conventional optimization. However, unlike its application to the rigid wing (single discipline analysis), the method, as implemented here, may not show significant reduction in the computational cost. Similar reductions were seen in the two-design-variable (DV) problem results but not in the 8-DV results given here.

In the last few years, a significant amount of research has focused on learning and modeling personalized thermal preferences to satisfy individuals in buildings. However, there is a disconnection between personal comfort studies and building thermal control research. The objective of this article is to review research studies that integrate personalized thermal preferences into building control systems, and their control performance (in terms of comfort and energy) through either simulation studies or field implementations. Existing studies can be categorized into two main categories, depending on the type of control integration: conventional, reactive (feedback) control and advanced model-based predictive control. A deeper analysis of the degree of integration, number of occupants considered, control setpoint type, and thermal preference aggregation method, leads to a more detailed classification of thermal preference-based control research. In the absence of a general consensus of robust implementation of thermal preference-based control in buildings, this categorization allows a better understanding of current focus, research gaps, and needs for future work.

Multidisciplinary aircraft design optimization and optimal trajectory analysis are both popular research topics, yet few studies have considered optimal flight-path analysis during the aircraft design process. This paper describes simultaneous aircraft and trajectory design optimization using the Optimizer-Based Decomposition (OBD) architecture for multidisciplinary optimization. The analysis used for this problem would be difficult to develop and run using a traditional unified architecture. The problem is decomposed both by discipline and by stages of the trajectory, creating a matrix of independent subproblems that each consist of a single discipline at one stage of the mission. This study, with high-fidelity analyses and many subproblems, demonstrates OBD as a practical, useful approach to real aerospace design problems.

This work presents a new methodology for the simultaneous process flowsheet and control design of dynamic systems under uncertainty using a model predictive control (MPC) strategy. Although several...

An optimization-based approach to the integrated thickness design and active control is developed for laminated cylindrical shells. The formulation of the problem is obtained by introducing the quadratic cost functionals and comprises a weighted sum of the design and control objectives. The design objective is the maximization of the fundamental frequency, and the control objective is the minimization of the dynamic response of a laminated cylindrical shell, which is expressed in terms of the energy of the structure. The effects of damping on the dynamics of composite structures are incorporated. The problem is solved using a maximum principle of Pareto optimal approach. Numerical examples of a simply supported composite cylindrical shell are presented to demonstrate the significance of the simultaneous design and control optimization as well as the damping effects.

The growing penetration of renewable energy sources requires resilient microgrids capable of providing stable and continuous operation. Hybrid energy storage systems (HESS), which integrate hydrogen-based storage systems (HBSS), battery storage systems (BSS), and supercapacitor banks (SCB), are essential to ensuring the flexibility and robustness of these microgrids. Accurate modelling of these microgrids is crucial for analysis, controller design, and performance optimization, but the complexity of HESS poses a significant challenge: simplified linear models fail to capture the inherent nonlinear dynamics, while nonlinear approaches often require excessive computational effort for real-time control applications. To address this challenge, this study presents a novel state space model with linear variable parameters (LPV), which effectively balances accuracy in capturing the nonlinear dynamics of the microgrid and computational efficiency. The research focuses on a high-voltage DC bus microgrid architecture, in which the BSS and SCB are connected directly in parallel to provide passive DC bus stabilization, a configuration that improves system resilience but has received limited attention in the existing literature. The proposed LPV framework employs recursive linearisation around variable operating points, generating a time-varying linear representation that accurately captures the nonlinear behaviour of the system. By relying exclusively on directly measurable state variables, the model eliminates the need for observers, facilitating its practical implementation. The developed model has been compared with a reference model validated in the literature, and the results have been excellent, with average errors, MAE, RAE and RMSE values remaining below 1.2% for all critical variables, including state-of-charge, DC bus voltage, and hydrogen level. At the same time, the model maintains remarkable computational efficiency, completing a 24-h simulation in just 1.49 s, more than twice as fast as its benchmark counterpart. This optimal combination of precision and efficiency makes the developed LPV model particularly suitable for advanced model-based control strategies, including real-time energy management systems (EMS) that use model predictive control (MPC). The developed model represents a significant advance in microgrid modelling, as it provides a general control-oriented approach that enables the design and operation of more resilient, efficient, and scalable renewable energy microgrids.

Dynamic Inherent Safety Analysis of a Distillation Column under Simultaneous Design and Control

In ultra-precision motion systems, vibration has non-negligible influence on motion performance, especially when these systems are getting more lightweight and more flexible. Research on simultaneous optimization of structural and controller design has been conducted to achieve effective vibration control. However, these methods will not have an adequate performance when used in ultra-precision motion systems with a time-varying performance location. In this paper, structural sizes, actuators configuration, and controller parameters are simultaneously optimized. To realize global vibration control and facilitate simultaneous optimization, a new vibration controller with position-dependent control gains is proposed, in which the worst-case vibration magnitude across all considered performance locations is set to be the objective function. To achieve high modeling accuracy, mass and stiffness distribution of actuators is also included into structural dynamics since it plays a large role in structural dynamics. The genetic algorithm is adopted to search for a global optimum. To increase efficiency, R-functions and level-set functions are introduced to translate the complicated over-lapping constraints into a simple integral equality. Neural fitting models instead of the finite element analysis method are used to derive eigenvalues and eigenvectors of the plant. The proposed method is verified on a simplified fine stage in the wafer stage. The numerical results prove its effectiveness.

Simultaneous design and operational optimization for flexible carbon capture process using ionic liquids

Simultaneous design and part-load optimization of an industrial ammonia synthesis reactor

14 - A framework for synthesis of operable and intensified reactive separation systems