Stability of nonlinear asynchronous systems

In this work, we focus on a class of nonlinear asynchronous systems defined by two different modes of operation, one stable and the other one unstable. The switching between the two modes of operation is driven by external asynchronous events. It is assumed that on any time interval of a given length, the maximum time in which the system evolves in the unstable mode is bounded. This property is given in the form of a rate constraint. Under this assumption, we study the behavior of this class of systems and provide existential results of conditions on this rate constraint under which various types of stability of the origin of the nonlinear asynchronous system can be assured.     

Approximating distributional behaviour of LTI differential systems using Gaussian function and its derivatives

This article is concerned with the approximation of the distributional behaviour of linear, time-invariant (LTI) systems. First, we review the different types of approximations of distributions by smooth functions and explain their significance in characterising system properties. Second, we consider the problem of changing the state of controllable LTI differential systems in a very short time. Thus, we establish an interesting relation between the time and volatility parameters of the Gaussian function and its derivatives in the approximation of distributional solutions. An algorithm is then proposed for calculating the distributional input and its smooth approximation which minimises the distance to an arbitrary target state. The optimal choice of the volatility parameter for the state transition is also derived. Finally, some complementary distance problems are also considered. The main results of this article are illustrated by numerous examples.     

Distributed economic MPC: Application to a nonlinear chemical process network

In the present work, we focus on the development and application of Lyapunov-based economic model predictive control (LEMPC) designs to a catalytic alkylation of benzene process network, which consists of four continuously stirred tank reactors and a flash separator. We initially propose a new economic measure for the entire process network which accounts for a broad set of economic considerations on the process operation including reaction conversion, separation quality and energy efficiency. Subsequently, steady-state process optimization is first carried out to locate an economically optimal (with respect to the proposed economic measure) operating steady-state. Then, a sequential distributed economic model predictive control design method, suitable for large-scale process networks, is proposed and its closed-loop stability properties are established. Using the proposed method, economic, distributed as well as centralized, model predictive control systems are designed and are implemented on the process to drive the closed-loop system state close to the economically optimal steady-state. Extensive simulations are carried out to demonstrate the application of the proposed economic MPC (EMPC) designs and compare them with a centralized Lyapunov-based model predictive control design, which uses a conventional, quadratic cost function that includes penalty on the deviation of the states and inputs from their economically optimal steady-state values, from computational time and closed-loop performance points of view.     

Output feedback control of switched nonlinear systems using multiple Lyapunov functions

This work presents a hybrid nonlinear control methodology for a broad class of switched nonlinear systems with input constraints. The key feature of the proposed methodology is the integrated synthesis, via multiple Lyapunov functions, of “lower-level” bounded nonlinear feedback controllers together with “upper-level” switching laws that orchestrate the transitions between the constituent modes and their respective controllers. Both the state and output feedback control problems are addressed. Under the assumption of availability of full state measurements, a family of bounded nonlinear state feedback controllers are initially designed to enforce asymptotic stability for the individual closed-loop modes and provide an explicit characterization of the corresponding stability region for each mode. A set of switching laws are then designed to track the evolution of the state and orchestrate switching between the stability regions of the constituent modes in a way that guarantees asymptotic stability of the overall switched closed-loop system. When complete state measurements are unavailable, a family of output feedback controllers are synthesized, using a combination of bounded state feedback controllers, high-gain observers and appropriate saturation filters to enforce asymptotic stability for the individual closed-loop modes and provide an explicit characterization of the corresponding output feedback stability regions in terms of the input constraints and the observer gain. A different set of switching rules, based on the evolution of the state estimates generated by the observers, is designed to orchestrate stabilizing transitions between the output feedback stability regions of the constituent modes. The differences between the state and output feedback switching strategies, and their implications for the switching logic, are discussed and a chemical process example is used to demonstrate the proposed approach.     

The fundamental subspace sequences of matrix pencils: A toeplitz matrix unified characterization

The geometric theory of the domain of an ordered pair (F, G) of matrices or the geometry associated with matrix pencils provides a unifying framework for the study of algebraic, dynamic and feedback properties of linear singular systems. The key concepts and tools of the geometry are the notions of the (F, G)-, (G, F)-invariance and a set of subspace sequences. In this paper, an alternative characterization of these sequences is given based on the properties of the partitioned null spaces of appropriate sequences of Toeplitz matrices defined by the (F, G) pair. The results provide a simple procedure for the computation of the limit spaces of these sequences and clearly cover corresponding problems of the singular, implicit systems theory.     

Machine-learning-based construction of barrier functions and models for safe model predictive control

In this paper, we propose a control Lyapunov-barrier function-based model predictive control method utilizing a feed-forward neural network specified control barrier function (CBF) and a recurrent neural network (RNN) predictive model to stabilize nonlinear processes with input constraints, and to guarantee that safety requirements are met for all times. The nonlinear system is first modeled using RNN techniques, and a CBF is characterized by constructing a feed-forward neural network (FNN) model with unique structures and properties. The FNN model for the CBF is trained based on data samples collected from safe and unsafe operating regions, and the resulting FNN model is verified to demonstrate that the safety properties of the CBF are satisfied. Given sufficiently small bounded modeling errors for both the FNN and the RNN models, the proposed control system is able to guarantee closed-loop stability while preventing the closed-loop states from entering unsafe regions in state-space under sample-and-hold control action implementation. We provide the theoretical analysis for bounded unsafe sets in state-space, and demonstrate the effectiveness of the proposed control strategy using a nonlinear chemical process example with a bounded unsafe region.     

An integrated semantic framework supporting universal accessibility to ICT

As we are moving rapidly to a digital economy, accessing and effectively using Information and Communication Technologies (ICT) in everyday life is widely recognized as an important requirement. However, the accessibility technologies that we have up to date are meeting the needs of only some, at a very high cost and, as a consequence, accessible ICT for all people still remains a major research and development goal. This work presents an integrated ontological framework for the semantic representation of terms and concepts (i.e., related to user needs and preferences (N&P) with respect to ICT use, as well as solutions, platforms and devices) that are required for addressing the universal accessibility in the scope of the Cloud4all project and the Global Public Inclusive Infrastructure (GPII). Cloud4all aims at advancing and building upon the concept of GPII through the development of the necessary tools and models for making ICT accessible for all by exploiting the cloud computing paradigm. The main goal of the proposed framework lays in the separation between generalized accessibility concepts, user interaction mechanisms and N&P with the particular details of different ICT artifacts. Thus, the framework aims at integrating concepts related with user N&P, as well as ICT solutions, platforms, devices and their customizable settings along with information concerning their vendors or implementers, in order to (a) offer the necessary expressiveness for defining/representing personal N&P across applications, platforms and devices, (b) link N&P with the conditions/context according to which these shall be applicable for (e.g., considering the user activity and the physical environment), (c) link interaction requirements (originated from user characteristics) with N&P and (d) support the Cloud4all matchmaking process through the mapping between N&P and application-specific settings based on semantic rules and automatic reasoning techniques.     

Physical evaluation of a maize‐based extruded snack with curry powder

Response surface methodology was used to analyze the effect of screw speed (200–280 rpm), feed moisture (13.0–17.0%, wet basis), and curry powder (6.0–9.0%) on the bulk density, lateral expansion, and firmness of maize‐based extruded snack with curry powder. Regression equations describing the effect of each variable on the responses were obtained. Responses were most affected by changes in feed moisture followed by screw speed and curry powder (p < 0.05). Lateral expansion increased linearly as the amount of curry powder added was increased whereas a quadratic increase was obtained in lateral expansion with decreasing feed moisture. The firmness of samples was increased with an increase in feed moisture. The bulk density of samples was increased with increasing feed moisture and screw speeds. Radial expansion was found to be a better index to measure the physical properties of the extruded product indicated by a higher correlation coefficient.