Simultaneous design and control optimisation under uncertainty

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.

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

While design optimization has been employed effectively in the past few years to address the integration of design and control, the implementation of advanced model-based control techniques in such a framework has largely been ignored despite their advantages. The reason is that the advanced control schemes involve solving an optimization problem on-line, leading to a drastic increase in the complexity of the design framework. In this work a novel method is presented for integrating advanced model-based predictive controllers in a simultaneous process and control design methodology. Parametric programming is used for the controller derivation, giving rise to an explicit controller structure and removing the need for solving an optimization problem online. The resulting parametric controller is readily incorporated in the design optimization framework, bringing about significant economic and operability benefits. The key features and advantages of our approach are highlighted via binary distillation and evaporator motivating examples.

Recent advances in the field of control theory have enabled us to design active vibration control systems for various structures. In many studies, the controller used to suppress vibration has been synthesized for the given mathematical model of structure. In these cases, the designer has not been able to utilize the degree of freedom to adjust the structural parameters of the control object. To overcome this problem, so called “Structure/ Control Simultaneous Optimization Method” is used. In this context of view, this paper is concerned with the active vibration control of bridge towers, platforms and ocean vehicles etc. Simultaneous design method is used to achieve optimal system performance. Here, a general framework for the simultaneous design problem of output feedback case is introduced based on LMI (Linear Matrix Inequality). The simulation results show that the proposed design method achieves desirable control performance.

Cooperative adaptive cruise control (CACC) is a promising technology for vehicle platoons to increase roadway capacity. This paper proposes a networked Lagrange system (NLS) based CACC dynamic model based on which a hierarchical anti-disturbance controller is developed to solve the stability problem of CACC in vehicle platoons with time-varying parameter uncertainties, external disturbances, and directional dynamic communication topology. First, a hierarchical anti-disturbance controller for NLS is constructed which comprises three layers, these are, the adaptive smooth estimator-control layer, the distributed classifying amplitude-related layer, and the anti-disturbance control layer, in which the parameter uncertainties are estimated in the adaptive smooth estimator-control layer, the disturbance classification and distribute control are executed in the distributed classifying amplitude-related layer and the anti-disturbance control layer respectively. In addition, the proposed controller is extended to address the stability problem of CACC in vehicle platoons with time-varying parameter uncertainties, external disturbances, and directional dynamic communication topology conditions, which shows the versatility of the controller. Finally, comparison studies and simulation results are provided to demonstrate the effectiveness, significance, and advantages of the presented controllers.

ABSTRACTIn this paper, reliable control for linear systems with time-varying delays and parameter uncertainties is considered. By constructing newly augmented Lyapunov–Krasovskii functionals and utilizing some mathematical techniques such as Leibnitz's rule, Schur's complement, reciprocally convex combination, and so on, a reliable controller design method for linear systems with time-varying delays and parameter uncertainties will be suggested in Theorem 1. Based on the result of Theorem 1, a non-reliable stabilization criterion will be presented in Corollary 1. Theorem 1 and Corollary 1 are derived within the framework of linear matrix inequalities(LMIs) which can be easily solved by utilizing various optimization algorithms. Two numerical examples are included to show the effectiveness and necessity of the proposed results.

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

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...

This paper presents both simulation and experimental investigations into the simultaneous design of a steering wheel and its control system for vibration suppression. The research consists of two steps. In the first step, the simultaneous optimization is formulated as a general nonlinear programming problem with the closed-loop H∞ norm as objective function. Then the problem is solved by an iterative approach in which the control designs are nested into the structural designs. The simulation results show that the simultaneously optimized system gives much better performance than the original one. In the second step, experiments are performed to verify the effectiveness of the new design method. To deal with the effects of modeling error and system uncertainty, in the experiments µ synthesis is used instead of H∞ control. It is found from the experimental results that the optimized structure is still superior to the original one after the redesign of control.

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

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.

Simultaneous design and control under uncertainty: A back-off approach using power series expansions

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.

Robust sensor fault detection and isolation of gas turbine engines subjected to time-varying parameter uncertainties

Using a class of linear static controllers, we stabilize the Petersen open-loop two-dimensional linear system (Ref. 1), which consists of one time-varying uncertainty in the state matrixA and one timevarying uncertainty in the input matrixB. We show that the worst-case uncertainty strategy for the closed-loop system is a piecewise constant strategy of the angular state θ with three switches on the half-turn, −π/2≤θ≤π/2; it is unique with respect to a set of measure zero. Formulas are derived for the worst-case half-turn radius gainr HT as a function of the parameters of the class of stabilizing linear static controllers. Using the class of scalar-quadratic Lyapunov functions, we show that a necessary and sufficient condition for the closed-loop system to be robustly stable against all time-varying admissible uncertainties is thatr HT be less than unity. The bound on the time-varying real parameter uncertainties for the closed-loop system to be robustly stable is derived for the class of linear static feedback controllers. We obtain stabilizing linear static controllers such that the bound is as close to infinity as desired. The derived results are compared with numerical results obtained using commerical robust-control software.