In this paper, we extend the blending technique introduced in [2] to a general case. Different from, where all the columns of the distribution matrices of the exogenous inputs should be linearly independent of each other, we only require that part of the columns are linearly independent, and the other matrices have full column ranks. Based on this blending technique, we present a novel approach to the robust fault-tolerant control scheme and robust tracking problem. The novelty lies in the fact that we can separately design the controller gains for different performance indices, while simultaneously achieving all the indices via an elegant construction of an overall controller. The design procedure is based on LMI techniques and basic linear algebra tools.

Genetic algorithms (GA) are applied to a problem of conceptual airplane design. This paper discusses an integrated approach of designing the airplane geometry and flight control by means of GAs, where the two designs are conducted concurrently. The aim is to redesign the airplane's physical configuration in order to offer better closed-loop performance will less control effort. The overall design objective comprises control performances based on the linear quadratic regulator (LQR), and a cost function in terms of sizes of airplane components. Both single-objective and multi-objective GAs are explored. The flexibility offered by the proposed integrated- design approach is demonstrated in numerical examples.

In this paper, we present a novel surrogate-assisted evolutionary optimization framework for solving computationally expensive problems. The proposed framework uses computationally cheap hierarchical surrogate models constructed through online learning to replace the exact computationally expensive objective functions during evolutionary search. At the first level, the framework employs a data-parallel Gaussian process based global surrogate model to filter the evolutionary algorithm (EA) population of promising individuals. Subsequently, these potential individuals undergo a memetic search in the form of Lamarckian learning at the second level. The Lamarckian evolution involves a trust-region enabled gradient-based search strategy that employs radial basis function local surrogate models to accelerate convergence. Numerical results are presented on a series of benchmark test functions and on an aerodynamic shape design problem. The results obtained suggest that the proposed optimization framework converges to good designs on a limited computational budget. Furthermore, it is shown that the new algorithm gives significant savings in computational cost when compared to the traditional evolutionary algorithm and other surrogate assisted optimization frameworks

Solving design optimization problems using evolutionary algorithms has always been perceived as finding the optimal solution over the entire search space. However, the global optima may not always be the most desirable solution in many real-world engineering design problems. In practice, if the global optimal solution is very sensitive to uncertainties, for example, small changes in design variables or operating conditions, then it may not be appropriate to use this highly sensitive solution. In this paper, we focus on combining evolutionary algorithms with function approximation techniques for robust design. In particular, we investigate the application of robust genetic algorithms to problems with high dimensions. Subsequently, we present a novel evolutionary algorithm based on the combination of a max-min optimization strategy with a Baldwinian trust-region framework employing local surrogate models for reducing the computational cost associated with robust design problems. Empirical results are presented for synthetic test functions and aerodynamic shape design problems to demonstrate that the proposed algorithm converges to robust optimum designs on a limited computational budget.

In this paper, we present an evolutionary framework for efficient aerodynamic shape design. The approach suggests employing hybrid evolutionary algorithm with gradient-based local search method in the spirit of Lamarckian and surrogate models that approximates the computationally expensive adjoint computational fluid dynamics during design search. In particular, we reveal that the proposed framework guarantees global convergence by inheriting the properties of trust-region method to interleave use of the exact solver for the objective function with computationally cheap surrogate models during local search. Empirical results on 2D airfoil shape design using an adjoint inverse pressure design problem indicates that the approaches global convergences on a limited computational budget.