A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version

Robust nonlinear control of vectored thrust aircraft

An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations

Rationalizing Noneconomic Damages: A Health-Utilities Approach

Studdert et al examine why making compensation of noneconomic damages in personal-injury litigation more rational and predictable is socially valuable. Noneconomic-damages schedules as an alternative to caps are discussed, several potential approaches to construction of schedules are reviewed, and the use of a health-utilities approach as the most promising model is argued. An empirical analysis that combines health-utilities data created in a previous study with original empirical work is used to demonstrate how key steps in construction of a health-utilities-based schedule for noneconomic damages might proceed

Advances in Theoretical and Computational Energy Optimization Processes

Industry, construction and transport are the three sectors that traditionally lead to the highest energy requirements. This is why, over the past few years, all the involved stakeholders have widely expressed the necessity to introduce a new approach to the analysis and management of those energy processes characterizing the aforementioned sectors. The objective is to guide production and energy processes to an approach aimed at energy savings and a decrease in environmental impact. Indeed, all of the ecosystems are stressed by obsolete production schemes deriving from an unsustainable paradigm of constant growth and related to the hypothesis of an environment able to absorb and accept all of the anthropogenic changes

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Predictive Energy Management of Islanded Microgrids with Photovoltaics and Energy Storage

Islanded microgrids powered primarily by photovoltaic (PV) arrays present a challenging control problem due to the intermittent production and the relatively close scale between the sources and the loads. Energy storage in such microgrids plays an important role in balancing supply with demand, and in extending operation during periods when the PV supply is not available or insufficient. The efficient operation of such microgrids requires effective management of all resources. A predictive energy management strategy can potentially avoid or effectively mitigate upcoming outages. This thesis presents an energy management system (EMS) for such microgrids. The EMS uses a predictive approach to set operational schedules in order to (a) prolong the supply to critical system loads and (2) minimize the chances and duration of system-wide outages, specifically through pre-emptive load shedding. Online weather forecast data has been combined with the PV system model to assess potential energy production over a 48 hour period. These predictions, along with load forecasts and a model of the energy storage system, are used to predict the state-of-charge of the storage devices and characterize potential power shortages. Pre-emptive load shedding is subsequently planned and executed to avert outages or minimize the duration of unavoidable outages. A bounding technique has also been proposed to account for uncertainties in estimates of the stored energy. The EMS has been implemented using an event-driven framework with network communication. The approach has been validated through simulations and experiments using recorded real-world solar irradiance data. The results show that the outage durations have been reduced by a factor of 87% to 100% for an example operating scenario, selected to demonstrate the features of the scheme. The impact of uncertainties in the prediction models has also been investigated, specifically for the PV system rating and the battery capacity. A technique has been developed to compensate for such uncertainties by analyzing the data streams from the source and storage units. The technique is applied to the developed EMS strategy, where it is able to shorten the total outage duration by a factor of 12% over a 42-day scenario exhibiting a variety of irradiance conditions

Dependable Control for Wireless Distributed Control Systems

The use of wireless communications for real-time control applications poses several problems related to the comparatively low reliability of the communication channels. This paper is concerned with adaptive and predictive application-level strategies for ameliorating the effects of packet losses and burst errors in industrial sampled-data Distributed Control Systems (DCSs), which are implemented via one or more wireless and/or wired links, possibly spanning multiple hops. The paper describes an adaptive compensator that reconstructs the best estimates (in a least squares sense) of a sequence of one or more missing sensor node data packets in the controller node. At each sample time, the controller node calculates the current control, and a prediction of future controls to apply over a short time horizon; these controls are forwarded to the actuator node every sample time step. A simple design method for a digital Proportional Integral Derivative (PID)-like adaptive controller is also described for use in the controller node. Together these mechanisms give robustness to packet losses around the control loop; in addition, the majority of the computational overhead resides in the controller node. An implementation of the proposed techniques is applied to a case study using a Hardware in the Loop (HIL) test facility, and favorable results (in terms of both performance and computational overheads) are found when compared to an existing robust control method for a DCS experiencing artificially induced burst errors