Arturo Molina-Cristobal

Linear matrix inequalities and evolutionary optimization in multiobjective control

The multiobjective problems of H 2 optimal control (LQG case) and mixed H 2/H ∞ are addressed using two different approaches: Evolutionary Algorithms and Linear Matrix Inequalities (LMIs). This study illustrates with numerical examples how both approaches can be used to find the trade-off between different signal sensitivities to noise and to find the trade-off of the mixed H 2/H ∞ problem. For the mixed H 2/H ∞ example, this paper shows how a Multiobjective Genetic Algorithm (MOGA) could find an improved Pareto-optimal front compared to the LMI approach.

MULTIOBJECTIVE CONTROLLER DESIGN: OPTIMISING CONTROLLER STRUCTURE WITH GENETIC ALGORITHMS

Abstract This paper studies the open problem of reduced- and fixed-order H∞ synthesis. Often, this non-convex constraint is tackled with iterative convex optimisation procedure over LMI constraints. In this paper, an evolutionary approach is proposed such that the trial and error approach involved in LMI techniques might be overcome. The order of the controller is optimised as a multiobjective problem over a set of controller structures, H∞, and time-domain specifications. Numerical results are presented with its counterpart the LMI procedure design, that show the advantage of investigating the Pareto optimal set resulting from the design procedure proposed.

Multi-fidelity simulation modelling in optimization of a submarine propulsion system

An inherent trade-off exists in simulation model development and use: a trade-off between the level of detail simulated and the simulation models computational cost. It is often desirable to simulate a high level of detail to a high degree of accuracy. However, due to the nature of design optimization, which requires a large number of design evaluations, the application of such simulation models can be prohibitively expensive. This paper presents an optimization framework consisting of a series hybrid optimization algorithm, in which a global search optimizes a submarine propulsion system using low-fidelity models and, in order to refine the results, a local search is used with high-fidelity models.

Enabling exploration in the conceptual design and optimisation of complex systems

In this paper, a design support framework for handling complexity associated with design exploration is presented. Two enabling methods are proposed. The first is a novel algorithm for dependency analysis of computational workflows. It provides a means for guiding the designer in adequately reversing the inputs and outputs of models and workflows, depending on the particular formulation(s) of the design studies to be conducted. Ultimately, the algorithm is intended to enable the (re)configuration of workflows while guaranteeing solvability. A method for supporting the investigation of what-if studies is also proposed. It is based on a generalisation of the isocontour method aimed at analysing the effects of different definitions of the design variable bounds on the topology of the feasible design space. The methodology is demonstrated by an industrially relevant test case concerning aircraft early conceptual design. The example demonstrates how different options for adequately reformulating optimisation studies are proposed to the designer for further exploration of design alternatives that guarantee the satisfaction of the entire set of constraints.

Robust H∞ Controller Design for Aircraft Lateral Dynamics using Multi-objective Optimization and Genetic Algorithms

Two techniques are combined during the design of an optimal controller: Linear Matrix Inequalities (LMIs) and Multi-objective Genetic Algorithms (MOGAs). In this paper the LMI optimization technique is used to obtain a single controller while MOGA is used to convert the controller design into a multi-objective optimization procedure. The combination of these techniques is proposed in this document and is shown to be advantageous against independent application of the aforementioned techniques. It is also presented how the sensitivity and complementary sensitivity functions are shaped with the weighting functions, while restricting the magnitude of the control signals by adding them as a hard objective in the MOGA approach.

The development of a multi-threaded multi-objective Tabu search algorithm

The reliance of Tabu Search (TS) algorithms on a local search leads to a logical development of algorithms that use more than one search concurrently. In this paper we present a multi-threaded TS algorithm employing a number of threads that share information.We assess the performance of this algorithm compared to previous multi-objective TS algorithms, via the results obtained from applying the algorithms to a range of standard test functions. We also consider whether an optimal number of threads can be found, and what impact changing the number of threads used has on performance. We discover that, contrary to the popular belief that multi-threading is usually beneficial, performance only improves in a few special cases.

Set-Based Approach to Passenger Aircraft Family Design

Presented is a method for the design of passenger aircraft families. Existing point-based methods found in the literature employ sequential approaches in which a single design solution is selected early and is then iteratively modified until all requirements are satisfied. The challenge with such approaches is that the design is driven toward a solution that, although promising to the optimizer, may be infeasible due to factors not considered by the models. The proposed method generates multiple solutions at the outset. Then, the infeasible solutions are discarded gradually through constraint satisfaction and set intersection. The method has been evaluated through a notional example of a three-member aircraft family design. The conclusion is that point-based design is still seen as preferable for incremental (conventional) designs based on a wealth of validated empirical methods, whereas the proposed approach, although resource-intensive, is seen as more suited to innovative designs.

Simulating the RRS James Clark Ross as part of a multiobjective design process

A model is developed for the RRS James Clark Ross using the Virtual Test Bed (VTB) simulation package. The model is integrated into a multiobjective optimisation process through a simulation interface developed for this purpose. The interface allows for visualisation of the optimisation results, and facilitates the transfer of design parameters to the simulation model. The simulation model is applied to the existing James Clark Ross design, and the effect of hybridising the propulsion system by adding a battery and IGBT inverter to provide power factor compensation for the DC drive is explored.

Aircraft systems architecting: a functional-logical domain perspective

Presented is a novel framework for early systems architecture design. The framework defines data structures and algorithms that enable the systems architect to operate interactively and simultaneously in both the functional and logical domains. A prototype software tool, called AirCADia Architect, was implemented, which allowed the framework to be evaluated by practicing aircraft systems architects. The evaluation confirmed that, on the whole, the approach enables the architects to effectively express their creative ideas when synthesizing new architectures while still retaining control over the process.

Efficient Method for Variance-Based Sensitivity Analysis

Presented is an efficient method for variance-based sensitivity analysis. It provides a general approach to transforming a sensitivity problem into one uncertainty propagation process, so that various existing approximation techniques (for uncertainty propagation) can be applied to speed up the computation. In this paper, formulations are deduced to implement the proposed approach with one specific technique named Univariate Reduced Quadrature (URQ). This implementation was evaluated with a number of numerical test-cases. Comparison with the traditional (benchmark) Monte Carlo approach demonstrated the accuracy and efficiency of the proposed method, which performs particularly well on the linear models, and reasonably well on most non-linear models. The current limitations with regard to non-linearity are mainly due to the limitations of the URQ method used.