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In this paper, we consider the problem of synthesizing online optimal flight controllers, in the presence of multiple objectives. The problem is cast as an adaptive Multi-Objective Optimization (MO-Op) flight control problem, in which a control policy is sought that attempt to optimize over multiple, sometimes conflicting objectives. A solution strategy utilizing Gaussian Process (GP) based adaptive-optimal control is presented, in which the system uncertainties are learned with an online updated budgeted GP. The mean of the GP is used to feedback linearize the system and reference model shaping is utilized for optimization. To make the MO-Op problem online realizable, a relaxation strategy that poses some objectives as adaptively updated soft constraints is proposed. The strategy is validated on a nonlinear roll dynamics model with simulated statedependent flexible-rigid mode interaction.

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An adaptive-optimal control architecture is presented for adaptive control of constrained aerospace systems with matched uncertainties that are subject to dynamic stochastic change. The architecture brings together three key elements, ie, model predictive control-based reference command shaping, Gaussian process (GP)-based Bayesian nonparametric model reference adaptive control (MRAC), and online GP clustering over nonstationary GPs. Model predictive control optimizes reference model and its shaped output is passed into GP-based MRAC, which is used to learn the model in presence of significant time-varying stochastic uncertainty while maintaining stability. Based on a likelihood ratio test, the changepoints are detected and learned. Lastly, the models are created and clustered by non-Bayesian clustering algorithm. The key salient feature of our architecture is that not only can it detect changes but also it uses online GP clustering to enable the controller to utilize past learning of similar models to significantly reduce learning transients. Furthermore, persistence of excitation conditions are significantly relaxed due to the use of GP-MRAC. Stability of the architecture is argued theoretically and performance is validated empirically on different scenarios for wing rock dynamics.

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Across different geographical and industrial boundaries, different firms are attempting to implement sustainability in their supply chain in response to pressures from different groups. This article aims at identifying and analyzing influential practices for implementing sustainable supply chain management (SSCM). By determining these practices, top management can focus on them in order to improve the performance of their supply chains. The petrochemical industry was selected because of its role in the Iranian economy and its considerable environmental and social impacts. The interpretive structural modeling (ISM) technique was used as a useful technique to identify interrelations between different sustainable practices. According to the results, set up reduction and pull production system (related to JIT practices) are driving other practices, and these practices have vital role among other practices. There are four practices related to evaluating and collaborating suppliers and other industry peers, which occupy the highest level.