This letter provides a brief explanation of echo state networks and provides a rigorous bound for guaranteeing asymptotic stability of these networks. The stability bounds presented here could aid in the design of echo state networks that would be applicable to control applications where stability is required. Index Termsecho state networks, weighted operator norms, recurrent neural networks, nonlinear systems, Lyapunov stability, robust controls

No abstract

The applicability of machine learning techniques for feedback control systems is limited by a lack of stability guarantees. Robust control theory offers a framework for analyzing the stability of feedback control loops, but for the integral quadratic constraint (IQC) framework used here, all components are required to be represented as linear, time-invariant systems plus uncertainties with, for IQCs used here, bounded gain. In this paper, the stability of a control loop including a recurrent neural network (NN) is analyzed by replacing the nonlinear and time-varying components of the NN with IQCs on their gain. As a result, a range of the NN's weights is found within which stability is guaranteed. An algorithm is demonstrated for training the recurrent NN using reinforcement learning and guaranteeing stability while learning.

While having the potential to significantly improve heating, ventilating and air conditioning (HVAC) system performance, advanced (e.g., optimal, robust and various forms of adaptive) controllers have yet to be incorporated into commercial systems.Controllers consisting of distributed proportional-integral (PI) control loops continue to dominate commercial HVAC systems. Investigation into advanced HVAC controllers has largely been limited to proposals and simulations, with few controllers being tested on physical systems. While simulation can be insightful, the only true means for verifying the performance provided by HVAC controllers is by Preprint submitted to Energy and Buildings 4 January 2006 actually using them to control an HVAC system. The construction and modeling of an experimental system for testing advanced HVAC controllers, is the focus of this article.A simple HVAC system, intended for controlling the temperature and flow rate of the discharge air, was built using standard components. While only a portion of an overall HVAC system, it is representative of a typical hot water to air heating system. In this article, a single integrated environment is created that is used for data acquisition, controller design, simulation, and closed loop controller implementation and testing. This environment provides the power and flexibility needed for rapid prototyping of various controllers and control design methodologies.

Abstract-Microalgae have the potential to produce enough biofuels to meet the current US fuel demands. In order to achieve this potential, photobioreactors (PBRs) need to be developed that are efficient, scalable, and affordable. Models are an analytical tool that can be used to evaluate various PBRs. In this article, a dynamic model is developed for growing microalgae in a vertical flat panel photobioreactor (PBR) that may be used to measure PBR efficiency for various architectures independent of scale. The growth model is used to estimate the microalgae growth and byproduct production and consumption as a function of incident light. A feed-forward controller is developed that uses the estimated amount of CO2 consumed to determine the amount of additional CO2 to add to the system during photosynthesis. An overall controller structure that uses both feed-forward and feedback control is presented for growing microalgae inside a PBR.

The International Marine Organization (IMO) has a goal of reaching 40% reduction of GHG emissions by 2030 and target of a full 50% reduction in marine fleet wide GHG emissions by 2050, while other organizations and governments desire to develop a path to Net-Zero GHG emissions by no later than 2050. To accomplish this, engines with near zero GHG emissions must be developed now. In addition to new ships, there is a large existing fleet of diesel fueled engines in the market today which are candidates for retrofit. Ammonia fueling of a diesel engine using dual-fuel combustion represents a viable zero-carbon fuel and combustion strategy suitable for long-haul / heavy-duty transportation due to its favorable storage properties of liquid at low tank pressure. The challenge, however, is ammonia is hard to ignite, slow to burn, and cool when it does burn which creates a significant challenge from a combustion point of view. Conventional dual fuel (CDF) will not be able to burn more than 50% NH3-Diesel ratios efficiently with acceptable combustion quality, thus, combustion enhancement is required to get ammonia to ignite and burn at higher substitution rates. Woodward has developed a fueling and combustion control strategy using diesel pilot injection as the ignition source and combustion accelerant. And using RCCI combustion (Reactivity Controlled Compression Ignition) controlled by Active Combustion Control (ACC) high ammonia-diesel substitution ratios (GSR) is demonstrated to burn as fast or faster than the baseline diesel. With the proportional reduction of carbon in the fuel and an appropriate ammonia slip catalytic technology, it is demonstrated that ammonia can be used as a GHG reduction fuel in dual-fuel diesel engines which can contribute to reduction in GHG emissions proportional to the NH3 substitution ratio. This is a technology which can be deployed today on both retrofit of existing engines as well as on new engines to meet the marine fleet average GHG emissions goals.

No abstract

No abstract