Sampled-data extremum-seeking framework for constrained optimization of nonlinear dynamical systems

Most extremum-seeking control (ESC) approaches focus solely on the problem of finding the extremum of some unknown, steady-state input–output map, providing parameter settings that lead to optimal steady-state system performance. However, many industrial applications also have to deal with constraints on operating conditions due to, e.g., actuator limitations, limitations on tunable system parameters, or constraints on measurable variables. In particular, constraints on measurable variables are typically unknown in terms of their relationship with the tunable system parameters. In addition, the constraints on system inputs as a result of the constraints on measurable variables may conflict with the otherwise optimal operational condition, and hence should be taken into account in the data-based optimization approach. In this work, we propose a sampled-data extremum-seeking framework for the constrained optimization of a class of nonlinear dynamical systems with measurable constrained variables. In this framework, barrier function methods are employed, exploiting both the objective function and constraint functions which are available through output measurement only. We show, under the assumption that the parametric initialization yield operating conditions that do not violate the constraints, that (1) the resulting closed-loop dynamics is stable, (2) constraint satisfaction of the inputs is guaranteed for all iterations of the optimization process, and (3) constrained optimization is achieved. We illustrate the working principle of the proposed framework by means of an industrial case study of the constrained optimization of extreme ultraviolet light generation in a laser-produced plasma source within a state-of-the-art lithography system.</p

Sampled-data extremum-seeking framework for constrained optimization of nonlinear dynamical systems

Most extremum-seeking control (ESC) approaches focus solely on the problem of finding the extremum of some unknown, steady-state input–output map, providing parameter settings that lead to optimal steady-state system performance. However, many industrial applications also have to deal with constraints on operating conditions due to, e.g., actuator limitations, limitations on tunable system parameters, or constraints on measurable variables. In particular, constraints on measurable variables are typically unknown in terms of their relationship with the tunable system parameters. In addition, the constraints on system inputs as a result of the constraints on measurable variables may conflict with the otherwise optimal operational condition, and hence should be taken into account in the data-based optimization approach. In this work, we propose a sampled-data extremum-seeking framework for the constrained optimization of a class of nonlinear dynamical systems with measurable constrained variables. In this framework, barrier function methods are employed, exploiting both the objective function and constraint functions which are available through output measurement only. We show, under the assumption that the parametric initialization yield operating conditions that do not violate the constraints, that (1) the resulting closed-loop dynamics is stable, (2) constraint satisfaction of the inputs is guaranteed for all iterations of the optimization process, and (3) constrained optimization is achieved. We illustrate the working principle of the proposed framework by means of an industrial case study of the constrained optimization of extreme ultraviolet light generation in a laser-produced plasma source within a state-of-the-art lithography system.</p

Extremum Seeking With Enhanced Convergence Speed for Optimization of Time-Varying Steady-State Behavior of Industrial Motion Stages

Recently, an extremum-seeking control (ESC) approach has been developed for optimization of generically time-varying steady-state responses of nonlinear systems. A generic filter structure was introduced, the so-called dynamic cost function, which has been instrumental in facilitating the use of ESC in the more generic, time-varying context. However, the dynamic cost function must operate sufficiently slow compared to the time-varying nature of the system responses, thereby compromising the convergence speed of the ESC scheme. In this work, a modified ESC approach is proposed that incorporates explicit knowledge about the user-defined dynamic cost function, able to enhance the convergence speed of the ESC scheme. Moreover, we provide a stability analysis for this extended approach. The main contribution of this work is the experimental demonstration of both ESC approaches for the performance optimal tuning of a variable-gain control (VGC) strategy employed on a high-accuracy industrial motion stage setup, exhibiting generically time-varying steady-state responses. VGC is able to enhance the system performance by balancing the typical linear control tradeoff between low-frequency disturbance suppression properties and sensitivity to high-frequency disturbances in a more desirable manner. We experimentally show that, for the unknown disturbance situation at hand, the variable-gain controller can be automatically tuned using both ESC approaches to achieve the optimal system performance. In addition, enhanced convergence speed with the modified ESC approach is evidenced experimentally.acceptedVersio

Proportional-Integral-Derivative-Based Learning Control for High-Accuracy Repetitive Positioning of Frictional Motion Systems

Classical proportional-integral-derivative (PID) control is exploited widely in industrial motion systems with dry friction motivated by the intuitive and easy-to-use design and tuning tools available. However, classical PID control suffers from severe performance limitations. In particular, friction-induced limit cycling (i.e., hunting) is observed when integral control is employed on frictional systems that suffer from the Stribeck effect, thereby compromising setpoint stability. In addition, the resulting time-domain behavior, such as rise time, overshoot, settling time, and positioning accuracy, highly depends on the particular frictional characteristic, which is typically unknown or uncertain. On the other hand, omitting integral control can lead to constant nonzero setpoint errors (i.e., stick). To achieve superior setpoint performance for frictional motion systems in a repetitive motion setting, we propose a PID-based feedback controller with a time-varying integrator gain design. To ensure optimal setpoint positioning accuracy, a data-based sampled-data extremum-seeking architecture is employed to obtain the optimal time-varying integrator gain design. The proposed approach does not rely on knowledge on the friction characteristic. Finally, the effectiveness of the proposed approach is evidenced experimentally by application to an industrial nanopositioning motion stage setup of a high-end electron microscope

Extremum-seeking control for steady-state performance optimization of nonlinear plants with time-varying steady-state outputs

Extremum-seeking control is a useful tool for the steady-state performance optimization of plants for which the dynamics and disturbance situation can be unknown. The case when steady-state plant outputs are constant received a lot of attention, however, in many applications time-varying outputs characterize plant performance. As a result, no static parameter-to-steady-state performance map can be obtained. In this work, an extremum-seeking control method is proposed that uses a so-called dynamic cost function to cope with these time-varying outputs. We show that, under appropriate conditions, the solutions of the extremum-seeking control scheme are uniformly ultimately bounded in view of bounded and time-varying external disturbances, and the region of convergence towards the optimal tunable plant parameters can be made arbitrarily small. Moreover, its working principle is illustrated by means of the performance optimal tuning of a variable-gain controller for a motion control application

Sampled-Data Extremum-Seeking Control for Optimization of Constrained Dynamical Systems Using Barrier Function Methods

Most extremum-seeking control approaches focus solely on the problem of finding the extremum of some unknown, steady-state performance map. However, many industrial applications also have to deal with constraints on operating conditions due to, e.g., actuator limitations, limitations on design or tunable system parameters, or constraints on measurable signals. These constraints, which can be unknown a-priori, may conflict with the otherwise optimal operational condition, and should be taken into account in performance optimization. In this work, we propose a sampled-data extremum-seeking approach for optimization of constrained dynamical systems using barrier function methods, where both the objective function and the constraint function are available through measurement only. We show that, under the assumption that initialization does not violate constraints, the interconnection between a constrained dynamical system and optimization algorithms that employ barrier function methods is stable, the constraints are satisfied, and optimization is achieved. We illustrate the results by means of a numerical example

Extremum-seeking control for steady-state performance optimization of nonlinear plants with time-varying steady-state outputs

Extremum-seeking control is a useful tool for the steady-state performance optimization of plants for which the dynamics and disturbance situation can be unknown. The case when steady-state plant outputs are constant received a lot of attention, however, in many applications time-varying outputs characterize plant performance. As a result, no static parameter-to-steady-state performance map can be obtained. Recently, we proposed an extremum-seeking control method that uses a so-called dynamic cost function to cope with these time-varying outputs. We showed that, under appropriate conditions, the solutions of the extremum-seeking control scheme are uniformly ultimately bounded in view of bounded and time-varying external disturbances, and the region of convergence towards the optimal tunable plant parameters can be made arbitrarily small. In this technical report, a proof of the local stability result is presented

Extremum-seeking control for optimization of time-varying steady-state responses of nonlinear systems

Extremum-seeking control (ESC) is a useful tool for the steady-state performance optimization of plants for which limited knowledge about its dynamical behavior and disturbance situation is known. The case when the steady-state plant responses correspond to equilibrium solutions received a lot of attention. However, in many industrial applications plant performance is characterized by time-varying steady-state behavior. In those cases, no static parameter-to-steady-state performance map can be defined. In this work, we propose an ESC method that employs a so-called dynamic cost function to cope with time-varying steady-state responses of general nonlinear systems. We prove semi-global practical asymptotic stability of the closed-loop ESC scheme in the presence of bounded and time-varying external disturbances. Moreover, the working principle is illustrated by means of the real-time performance optimal tuning of a nonlinear control strategy for a motion control application

Extremum-seeking control for steady-state performance optimization of nonlinear plants with time-varying steady-state outputs

Extremum-seeking control is a useful tool for the steady-state performance optimization of plants for which the dynamics and disturbance situation can be unknown. The case when steady-state plant outputs are constant received a lot of attention, however, in many applications time-varying outputs characterize plant performance. As a result, no static parameter-to-steady-state performance map can be obtained. In this work, an extremum-seeking control method is proposed that uses a so-called dynamic cost function to cope with these time-varying outputs. We show that, under appropriate conditions, the solutions of the extremum-seeking control scheme are uniformly ultimately bounded in view of bounded and time-varying external disturbances, and the region of convergence towards the optimal tunable plant parameters can be made arbitrarily small. Moreover, its working principle is illustrated by means of the performance optimal tuning of a variable-gain controller for a motion control application.</p