Adaptive output regulation for a class of nonlinear systems with guaranteed transient performance

This paper is dedicated to adaptive output regulation for a class of nonlinear systems with asymptotic output tracking and guarantee of prescribed transient performance. With the employment of internal model principle, we first transform this problem into a specific adaptive stabilization problem with output constraints. Then, by integrating the time-varying Barrier Lyapunov Function (BLF) technique together with the high gain feedback method, we develop an output-based control law to solve the constrained stabilization problem and consequently confine the output tracking error to a predefined arbitrary region. The output-based control law enables adaptive output regulation in the sense that, under unknown exosystem dynamics, all the closed-loop system signals are bounded whilst the controlled output constraints are not violated. Finally, efficacy of the proposed design is illustrated through a simulation example

Modified Direct Adaptive Regulation Scheme Applied to a Benchmark Problem

International audienceA direct adaptive regulation scheme using a FIR Youla-Kučera Filter has been proposed for solving the EJC Benchmark [4] for rejection of multiple unknown and time-varying narrow-band disturbances. Despite the excellent results this approach requires a careful design of the central controller in terms of selection of some of the assigned closed-loop poles. A modified scheme is proposed in this paper which will incorporate a particular adaptive IIR Youla-Kučera Filter. Called ρ-notch structure (the denominator is a projection inside the unit circle) of the model of the disturbance which has roots on the unit circle. The adaptive scheme estimates separately the numerator and denominator parameters of the IIR Youla-Kučera Filter. Stability and convergence proofs are given along with simulation and real-time results. Comparison with results already obtained for the EJC Benchmark are provided. The use of this approach drastically simplify the design of the central controller and provide even better results than [4] with the advantage to use a single central controller independently of the number of narrow band disturbances

Optimization techniques in respiratory control system models

One of the most complex physiological systems whose modeling is still an open study is the respiratory control system where different models have been proposed based on the criterion of minimizing the work of breathing (WOB). The aim of this study is twofold: to compare two known models of the respiratory control system which set the breathing pattern based on quantifying the respiratory work; and to assess the influence of using direct-search or evolutionary optimization algorithms on adjustment of model parameters. This study was carried out using experimental data from a group of healthy volunteers under CO2 incremental inhalation, which were used to adjust the model parameters and to evaluate how much the equations of WOB follow a real breathing pattern. This breathing pattern was characterized by the following variables: tidal volume, inspiratory and expiratory time duration and total minute ventilation. Different optimization algorithms were considered to determine the most appropriate model from physiological viewpoint. Algorithms were used for a double optimization: firstly, to minimize the WOB and secondly to adjust model parameters. The performance of optimization algorithms was also evaluated in terms of convergence rate, solution accuracy and precision. Results showed strong differences in the performance of optimization algorithms according to constraints and topological features of the function to be optimized. In breathing pattern optimization, the sequential quadratic programming technique (SQP) showed the best performance and convergence speed when respiratory work was low. In addition, SQP allowed to implement multiple non-linear constraints through mathematical expressions in the easiest way. Regarding parameter adjustment of the model to experimental data, the evolutionary strategy with covariance matrix and adaptation (CMA-ES) provided the best quality solutions with fast convergence and the best accuracy and precision in both models. CMAES reached the best adjustment because of its good performance on noise and multi-peaked fitness functions. Although one of the studied models has been much more commonly used to simulate respiratory response to CO2 inhalation, results showed that an alternative model has a more appropriate cost function to minimize WOB from a physiological viewpoint according to experimental data.Postprint (author's final draft

PI/PID controller stabilizing sets of uncertain nonlinear systems: an efficient surrogate model-based approach

Closed forms of stabilizing sets are generally only available for linearized systems. An innovative numerical strategy to estimate stabilizing sets of PI or PID controllers tackling (uncertain) nonlinear systems is proposed. The stability of the closed-loop system is characterized by the sign of the largest Lyapunov exponent (LLE). In this framework, the bottleneck is the computational cost associated with the solution of the system, particularly including uncertainties. To overcome this issue, an adaptive surrogate algorithm, the Monte Carlo intersite Voronoi (MiVor) scheme, is adopted to pertinently explore the domain of the controller parameters and classify it into stable/unstable regions from a low number of nonlinear estimations. The result of the random analysis is a stochastic set providing probability information regarding the capabilities of PI or PID controllers to stabilize the nonlinear system and the risk of instabilities. The minimum of the LLE is proposed as tuning rule of the controller parameters. It is expected that using a tuning rule like this results in PID controllers producing the highest closed-loop convergence rate, thus being robust against model parametric uncertainties and capable of avoiding large fluctuating behavior. The capabilities of the innovative approach are demonstrated by estimating robust stabilizing sets for the blood glucose regulation problem in type 1 diabetes patients

PI/PID controller stabilizing sets of uncertain nonlinear systems: an efficient surrogate model-based approach

AbstractClosed forms of stabilizing sets are generally only available for linearized systems. An innovative numerical strategy to estimate stabilizing sets of PI or PID controllers tackling (uncertain) nonlinear systems is proposed. The stability of the closed-loop system is characterized by the sign of the largest Lyapunov exponent (LLE). In this framework, the bottleneck is the computational cost associated with the solution of the system, particularly including uncertainties. To overcome this issue, an adaptive surrogate algorithm, the Monte Carlo intersite Voronoi (MiVor) scheme, is adopted to pertinently explore the domain of the controller parameters and classify it into stable/unstable regions from a low number of nonlinear estimations. The result of the random analysis is a stochastic set providing probability information regarding the capabilities of PI or PID controllers to stabilize the nonlinear system and the risk of instabilities. The minimum of the LLE is proposed as tuning rule of the controller parameters. It is expected that using a tuning rule like this results in PID controllers producing the highest closed-loop convergence rate, thus being robust against model parametric uncertainties and capable of avoiding large fluctuating behavior. The capabilities of the innovative approach are demonstrated by estimating robust stabilizing sets for the blood glucose regulation problem in type 1 diabetes patients

Structural Analysis and Control of a Model of Two-site Electricity and Heat Supply

This paper introduces a control problem of regulation of energy flows in a two-site electricity and heat supply system, where two Combined Heat and Power (CHP) plants are interconnected via electricity and heat flows. The control problem is motivated by recent development of fast operation of CHP plants to provide ancillary services of power system on the order of tens of seconds to minutes. Due to the physical constraint that the responses of the heat subsystem are not necessary as fast as those of the electric subsystem, the target controlled state is not represented by any isolated equilibrium point, implying that stability of the system is lost in the long-term sense on the order of hours. In this paper, we first prove in the context of nonlinear control theory that the state-space model of the two-site system is non-minimum phase due to nonexistence of isolated equilibrium points of the associated zero dynamics.Instead, we locate a one-dimensional invariant manifold that represents the target controlled flows completely. Then, by utilizing a virtual output under which the state-space model becomes minimum phase, we synthesize a controller that achieves not only the regulation of energy flows in the short-term regime but also stabilization of an equilibrium point in the long-term regime. Effectiveness of the synthesized controller is established with numerical simulations with a practical set of model parameters

Robust hybrid estimation and rejection of multi-frequency signals

We consider the problem of output regulation for LTI systems in the presence of unknown exosystems. The knowledge about the multi-frequency signals exosystem consists in the maximum number of frequencies and their maximal value. The control scheme relies on two main components: an estimation algorithm, to reconstruct the signal generated by the exosystem, and a controller, to enforce the output regulation property to the closed-loop system. To tackle the first task, we propose a hybrid observer for the estimation of the (possibly piece-wise continuous) number and values of the frequencies contained in the exogenous signal. The hybrid observer is particularly appealing for numerical implementations, and it is combined with a self-tuning algorithm of the free parameters (gains), thus improving its performance even in case of noisy measurements. Semi-global exponential convergence of the estimation error is provided. As far as the second task is concerned, a robust hybrid regulator is designed for practical rejection of the multi-frequency disturbance signal acting on the plant. The result is achieved by exploiting the frequencies estimated by the hybrid observer. The effectiveness of the proposed control scheme is shown by means of numerical simulations