Konstantin Y. Volyanskyy

A novel Q-modification term for adaptive control

A novel modification term is suggested for use in adaptive control. The development is of use in any setting in which uncertainty is linearly parameterized. The modification uses state and control time histories. The effect is justified through stability analysis, and illustrated on a dynamic model for wing rock

An Error Minimization Method in Adaptive Control

THIS paper presents a design approach based on error minimization in adaptive control for improving the rate of adaptation and allowing under certain conditions exponential convergence of the error dynamics. Global stability results are given for the case of perfectly parameterized uncertainty. The approach relies on the fact that the unknown weights in any linearly parameterized representation of uncertainty satisfy an integral equation involving the state and control variables. The equation is used to formulate an error minimization problem, the solution for which can be incorporated in the adaptive law. The paper extends an idea originally developed in (1) for the case of scalar uncertainty to the vector case. The results are conceptually similar to the notion of composite adaptation (2), and techniques developed for state estimation (3). The main difference is that these approaches use different signals. The effect of the modified adaptive components is illustrated on a dynamical model of an aircraft in which uncertainty is present both in control effectiveness and non-linear state dependent terms.

Pressure- and Work-Limited Neuroadaptive Control for Mechanical Ventilation of Critical Care Patients

In this paper, we develop a neuroadaptive control architecture to control lung volume and minute ventilation with input pressure constraints that also accounts for spontaneous breathing by the patient. Specifically, we develop a pressure - and work-limited neuroadaptive controller for mechanical ventilation based on a nonlinear multicompartmental lung model. The control framework does not rely on any averaged data and is designed to automatically adjust the input pressure to the patients physiological characteristics capturing lung resistance and compliance modeling uncertainty. Moreover, the controller accounts for input pressure constraints as well as work of breathing constraints. Finally, the effect of spontaneous breathing is incorporated within the lung model and the control framework.

A new neuroadaptive control architecture for nonlinear uncertain dynamical systems: Beyond σ- and e-modifications

Neural networks are a viable paradigm for adaptive system identification and control. This paper develops a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture involving additional terms in the update laws that are constructed using a moving window of the integrated system uncertainty. These terms can be used to identify the ideal system parameters as well as effectively suppress system uncertainty. A linear parameterization of the system uncertainty is considered and state feedback neuro-adaptive controllers are developed.

6-DOF Nonlinear Simulation of Vision-based Formation Flight

This paper presents an adaptive guidance and control law algorithm for implementation on a pair of Unmanned Aerial Vehicles (UAVs) in a 6 DOF leader-follower formation flight simulation. The objective of the simulation study is to prepare for a flight test involving a pair of UAVs in formation flight where the follower aircraft will be equipped with an onboard camera to estimate the relative distance and orientation to the leader aircraft. The follower guidance law is an adaptive acceleration based guidance law designed for the purpose of tracking a maneuvering leader aircraft. We also discuss the limitations of a preceding version of the guidance algorithm shown in a previous paper. Finally, we discuss the design of an adaptive controller (autopilot) to track the commands from the guidance algorithm. Simulation results for different leader maneuvers are presented and analyzed.

Neuroadaptive output feedback control for automated anesthesia with noisy EEG measurements

Critical care patients, whether undergoing surgery or recovering in intensive care units, require drug administration to regulate physiological variables such as blood pressure, cardiac output, heart rate, and degree of consciousness. The rate of infusion of each administered drug is critical, requiring constant monitoring and frequent adjustments. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control of drug administration. In this paper, we develop a neuroadaptive output feedback control framework for nonlinear uncertain nonnegative and compartmental systems with nonnegative control inputs and noisy measurements. In addition, the neuroadaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space. Finally, the proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery in the face of noisy electroencephalographic (EEG) measurements.

Pressure- and work-limited neuroadaptive control for mechanical ventilation of critical care patients

In this paper, we develop a neuroadaptive control architecture to control lung volume and minute ventilation with input pressure constraints that also accounts for spontaneous breathing by the patient. Specifically, we develop a pressure-and work-limited neuroadaptive controller for mechanical ventilation based on a nonlinear multi-compartmental lung model. The control framework does not rely on any averaged data and is designed to automatically adjust the input pressure to the patients physiological characteristics capturing lung resistance and compliance modeling uncertainty. Moreover, the controller accounts for input pressure constraints as well as work of breathing constraints. Finally, the effect of spontaneous breathing is incorporated within the lung model and the control framework.

Neuroadaptive output feedback control for nonlinear nonnegative dynamical systems with actuator amplitude and integral constraints

A neuroadaptive output feedback control architecture for nonlinear nonnegative dynamical systems with input amplitude constraints is developed. Specifically, the neuroadaptive controller guarantees that the imposed amplitude and integral input constraints are satisfied and the physical system states remain in the nonnegative orthant of the state space. The proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery in the face of infusion rate constraints and a drug dosing constraint over a specified period.

Adaptive Control for Compartmental Dynamical Systems with Disturbance Rejection Guarantees

Compartmental system models involve dynamic states whose values are nonnegative. These models are widespread in biological and physiological sciences and play a key role in understanding these processes. In this paper, we develop a direct adaptive control framework for compartmental dynamical systems with exogenous bounded disturbances. The proposed framework is Lyapunov-based and guarantees partial asymptotic stability of the closed-loop system, that is, asymptotic stability with respect to part of the closed loop system states associated with the plant dynamics. The remainder of the states associated with the adaptive gains are shown to be Lyapunov stable. Finally, in the case of bounded energy L2 disturbances the proposed approach guarantees a non-expansivity constraint on the closed-loop input-output map.

A Q-modification neuroadaptive control architecture for discrete-time systems

This paper extends the new neuroadaptive control framework for continuous-time nonlinear uncertain dynamical systems based on a Q-modification architecture to discrete-time systems. As in the continuous-time case, the discrete-time update laws involve auxiliary terms, or Q-modification terms, predicated on an estimate of the unknown neural network weights which in turn involve a set of auxiliary equations characterizing a set of affine hyperplanes. In addition, we show that the Q-modification terms in the discrete-time update law are designed to minimize an error criterion involving a sum of squares of the distances between the update weights and the family of affine hyperplanes.