Data-driven Control Design Research Articles

Data-Driven Control Design for Power Converters Approximated as Switched Affine Systems and Experimental Validation

Data-Driven Control Design for Power Converters Approximated as Switched Affine Systems and Experimental Validation

Algebraic approach to synthesis of data-driven control design for dissipativity

Algebraic approach to synthesis of data-driven control design for dissipativity

Data-driven event-triggering mechanism for linear systems subject to input saturation

This paper focuses on designing an event-triggering mechanism aimed at reducing control updates while maintaining the stability of a saturated closed-loop system. It addresses the regional stabilization of linear systems under input saturation conditions from a data-driven perspective. To do so, we propose a systematic method to convert model-driven conditions into data-driven control design Linear Matrix Inequality (LMI) conditions, enabling the co-design of the event-triggering rule and the state feedback gain. The theoretical contribution is then applied to the control of a spacecraft rendezvous problem.

Improved Data-Driven Control Design Based on LMI and Its Applications in Lithium-Ion Batteries

This paper focuses on the data-driven control for discrete-time systems based on linear matrix inequality (LMI) and provides its application in Lithium-Ion batteries with disturbances. First, the system’s state response is collected from a noisy environment to obtain the data matrices. Then, a new definition of parameters is proposed to improve the solvability of LMIs. To enhance the control scheme’s robustness against disturbances, a penalty function with an adjustable factor is introduced. The range of disturbances that can be tolerated is quantified under the control mechanism. Finally, data-driven state-of-charge tracking control for Lithium-Ion batteries is studied in the simulation. Comparisons with existing approaches show the effectiveness and superiority of the proposed method in terms of performance and disturbance tolerance.

Adaptive Robust Data-Driven Building Control via Bilevel Reformulation: An Experimental Result

Data-driven control approaches for the minimization of energy consumption of buildings have the potential to significantly reduce deployment costs and increase the uptake of advanced control in this sector. A number of recent approaches based on the application of Willems’ fundamental lemma for data-driven controller design from input/output measurements are very promising for deterministic linear time-invariant (LTI) systems. This article proposes a systematic way to handle unknown measurement noise and measurable process noise, and extends these data-driven control schemes to adaptive building control via a robust bilevel formulation, whose upper level ensures robustness and whose lower level guarantees prediction quality. Corresponding numerical improvements and an active excitation mechanism are proposed to enable a computationally efficient reliable operation. The efficacy of the proposed scheme is validated by a multizone building simulation and a real-world experiment on a single-zone conference building on the École Polytechnique Fédérale de Lausanne (EPFL) campus. The real-world experiment includes a 20-day nonstop test, where, without extra modeling effort, our proposed controller improves 18.4% energy efficiency against an industry-standard controller while also robustly ensuring occupant comfort.

Learning Controllers From Data via Approximate Nonlinearity Cancellation

We introduce a method to deal with the data-driven control design of nonlinear systems. We derive conditions to design controllers via (approximate) nonlinearity cancellation. These conditions take the compact form of data-dependent semi-definite programs. The method returns controllers that can be certified to stabilize the system even when data are perturbed and disturbances affect the dynamics of the system during the execution of the control task, in which case an estimate of the robustly positively invariant set is provided.

Data-driven control for discrete-time piecewise affine systems

This paper studies the problem of data-driven control for discrete-time piecewise affine (PWA) systems. Based on a sequel of sampled control inputs and states with satisfactory rank conditions, the closed loop dynamics of PWA systems can be represented and therefore the explicit information of system matrices is not needed in system stability analysis and controller synthesis processes. First, via S-procedure, system stability analysis results have been obtained, and then, two novel data-driven controller design methods in the form of linear matrix inequalities and equations have been proposed such that the resulting closed loop system is asymptotically stable. For PWA systems with unknown state space partition information, an online data-driven control method has been further proposed to guarantee the asymptotic stability of the resulting closed loop systems. Finally, simulation studies of two examples have been given to validate the effectiveness of the proposed data-driven controller design approaches.

Adaptive policy learning for data-driven powertrain control with eco-driving

Modern powertrain control design practices rely on model-based approaches accompanied by costly calibrations in order to meet ever stringent energy use and emissions targets specified for standardized drive cycles. These practices struggle to capture the complexity and uncertainty of real-world driving. However, the deluge of operational data now available with connected vehicle technology presents opportunities to foster data-driven control design methods that adapt the powertrain control systems to their field use conditions. While the most attractive of these methods is reinforcement learning (RL), it is rarely directly applicable in physical applications due to its challenges of learning efficiency (sample complexity) and guaranteeing safety. In this paper, we propose and evaluate an adaptive policy learning (APL) framework that leverages existing source policies shipped with vehicles to accelerate initial learning while recovering the asymptotic performance of a reinforcement learning-based powertrain control agent trained from scratch. We present a critique of related residual policy learning approaches and detail our algorithmic implementations for two versions of the proposed framework. We find that the APL powertrain control agents offer in the order of 10% fuel economy improvement over a default powertrain controller of a commercial vehicle without compromising driver accommodation metrics. We demonstrate that the APL frameworks offer a viable approach towards potentially applying RL for real-world scenarios by addressing its learning efficiency issues.

Fault Estimation and Control for Unknown Discrete-Time Systems Based on Data-Driven Parameterization Approach.

This study investigates the problem of fault estimation and control for unknown discrete-time systems. Such a problem was first formulated as an multiobjective optimization problem. Then, a data-driven parameterization controller design method was proposed to optimize both fault estimation and robust control performances. In terms of the single-objection control problem, necessary and sufficient conditions for designing the suboptimal controller were presented, and the performance index optimized by the developed data-driven method was shown to be consistent with that of the model-based method. In addition, by introducing additional slack variables into the controller design conditions, the conservatism of solving the multiobjective optimization problem was reduced. Furthermore, contrary to the existing data-driven controller design methods, the initial stable controller was not required, and the controller gain was directly parameterized by the collected state and input data in this work. Finally, the effectiveness and advantages of the proposed method are shown in the simulation results.

A general infrastructure for data-driven control design and implementation in tokamaks

A general infrastructure for tokamak controllers based on data-driven neural net models is presented. The paradigm allows for more flexible choices of both the underlying model and the desired controlled variables and targets. The system is implemented and tested on the DIII-D tokamak, enacting simultaneous pressure and temperature control via a finite-set model-predictive controller. Traditional control methods such as proportional–integral–derivative (PID) have proven effective for decoupled control tasks, but scale poorly when trying to achieve more complicated goals such as full state control. This is exactly where model-based controllers succeed.

Data-Driven Control Design With LMIs and Dynamic Programming

The goal of this paper is to study model-free data-driven control evaluation and design strategies for discrete-time linear time-invariant systems, where the system model is unknown. In particular, our main contribution is twofold: 1) new state-input exploration and data collection schemes from experiences; 2) new data-driven linear matrix inequalities and dynamic programming methods for stabilization and optimal control problems. The proposed exploration and data collection schemes theoretically guarantee to acquire sufficient information from the system’s state-input trajectories that can solve the underlying control design problems. We prove that under mild assumptions, as more and more data is accumulated, the collected data can solve the problems with higher probability along with the proposed algorithms.

Data-Driven Model Identification and Control of the Inertial Systems

In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the system in the open loop. To tackle these difficulties, an approach of data-driven model identification and control algorithm design based on the maximum stability degree criterion is proposed in this paper. The data-driven model identification procedure supposes the finding of the mathematical model of the system based on the undamped transient response of the closed-loop system. The system is approximated with the inertial model, where the coefficients are calculated based on the values of the critical transfer coefficient, oscillation amplitude and period of the underdamped response of the closed-loop system. The data driven control design supposes that the tuning parameters of the controller are calculated based on the parameters obtained from the previous step of system identification and there are presented the expressions for the calculation of the tuning parameters. The obtained results of data-driven model identification and algorithm for synthesis the controller were verified by computer simulation.

The valve opening control of the motor-operated valve using virtual internal model tuning and vata-driven prediction

This paper deals with applications of data-driven control, which is a controller tuning method that directly utilizes the data, for mechanical systems. Particularly, we focus on the valve opening control of the motor-operated valve as one of the representative mechanical control units which are used in much industrial equipment and are needed to be precisely controlled. If we have a mathematical model of such a system, a model-based controller design is the most rational approach. Otherwise, data-driven controller tuning is also expected to be a rational approach. However, there exists a nonlinear characteristic, such as the limit of a speed implemented in the nonlinear compensator. Since conventional data-driven controller design can not be used because data-driven control is generally derived for only linear time-invariant systems, we have to modify the method of driven controller tuning method so as to be applicable for such systems. This paper proposes a controller tuning method for the valve-opening control system implemented as a cascade control system with a limit of the speed. Here, we apply Virtual Internal Model Tuning, which is abbreviated as VIMT, to tune the inner linear controller. Then, we also apply data-driven prediction to compute the outer loop controller including the limit of the speed. Finally, we verified the usefulness of the proposed method using experimental verification.

A Non-Iterative Approach to Direct Data-Driven Control Design of MIMO LTI Systems

A Non-Iterative Approach to Direct Data-Driven Control Design of MIMO LTI Systems

Robust data-driven control for nonlinear systems using the Koopman operator*

Data-driven analysis and control of dynamical systems have gained a lot of interest in recent years. While the class of linear systems is well studied, theoretical results for nonlinear systems are still rare. In this paper, we present a data-driven controller design method for discrete-time control-affine nonlinear systems. Our approach relies on the Koopman operator, which is a linear but infinite-dimensional operator lifting the nonlinear system to a higher-dimensional space. Particularly, we derive a linear fractional representation of a lifted bilinear system representation based on measured data. Further, we restrict the lifting to finite dimensions, but account for the truncation error using a finite-gain argument. We derive a linear matrix inequality based design procedure to guarantee robust local stability for the resulting bilinear system for all error terms satisfying the finite-gain bound and, thus, also for the underlying nonlinear system. Finally, we apply the developed design method to the nonlinear Van der Pol oscillator.

Direct data-driven LPV control of nonlinear systems: An experimental result

We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) system concept. An LPV data-driven representation is used as a surrogate LPV form of the data-driven representation of the original nonlinear system. The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior. Efficiency of the proposed approach is demonstrated experimentally on a nonlinear unbalanced disc system showing for the first time in the literature that behavioral data-driven methods are capable to stabilize arbitrary forced equilibria of a real-world nonlinear system by the use of only 7 data points.

Model-free repetitive control design and implementation for dynamical galvanometer-based raster scanning

Data-driven repetitive control (RC) is proposed in this work to track online, dynamical raster trajectories in galvanometer-based scanning. To remove the requirement of a plant model in conventional model-based RC, we use model-free iterative learning control (ILC) to synthesize the data-driven repetitive controllers. Specifically, the frequency-domain plant-inversion and loop-shaping methods are both converted into time-domain trajectory tracking problems. The ILC is then applied to solve the trajectory tracking problems and subsequently derive the repetitive controllers from data. The stability conditions of both methods are analyzed and used to guide the data-driven control design. Experimental results on a commercially available galvanometer scanner demonstrate that the proposed methods improve the tracking error of a predefined raster scan by more than 30 times, as the conventional ILC does. Moreover, after applying data-driven RC, users can online assign various center positions and magnitudes of the raster trajectory. Once assigning a new reference in this continuous mode, the tracking error rapidly converges to the steady-state within ten periods.

Learning-based hierarchical control of water reservoir systems

The optimal control of a water reservoir system represents a challenging problem, due to uncertain hydrologic inputs and the need to adapt to changing environment and varying control objectives. In this work, we propose a real-time learning-based control strategy based on a hierarchical predictive control architecture. Two control loops are implemented: the inner loop is aimed to make the overall dynamics similar to an assigned linear model through data-driven control design, then the outer economic model-predictive controller compensates for model mismatches, enforces suitable constraints, and boosts the tracking performance. The effectiveness of the proposed approach is illustrated on an accurate simulator of the Hoa Binh reservoir in Vietnam. Results show that the proposed approach outperforms stochastic dynamic programming.

Two-Degree-of-Freedom Controller Design Based on a Data-Driven Estimation Approach

Data-driven control design is a method to create and tune controllers directly from the initial experimental data without a mathematical model to be controlled. Tracking and disturbance suppression are necessary to control real systems. A two-degree-of-freedom (2DOF) control system is effective to simultaneously enhance the performances of both. This study proposes a direct data-driven tuning method for the controller parameters of a 2DOF control system using only one-shot initial experimental data without mathematical modeling of the controlled object. The proposed approach improves the tracking and disturbance suppression performances by utilizing an estimation method in which the sensitivity function and the closed-loop transfer function are identified after updating the parameters in the time domain. Specifically, the closed-loop response and control input are estimated after updating the parameters, realizing efficient control system design because the control performance can be evaluated prior to implementation. To validate the effectiveness of the proposed method, a simulation for a mechanical system and an experiment for motor control are performed. The proposed method can estimate the response and control input of a 2DOF control system after updating the parameters by offline computations. The optimal control parameters can be obtained to enhance the tracking and disturbance suppression performances.

Direct Data-Driven Design for a Sparse Feedback Controller Based on VRFT and LASSO regression

Direct data-driven controller design has attracted considerable attention because of simple design procedure. This paper presents a direct model-referenced data-driven design for obtaining sparse controller to prevent over learning from single-experiment data. In conventional methods, controller parameters are obtained for controller with order defined by user. In case where there is little plant information, it is difficult to define controller order to achieve model-matching. Also, if high order of the controller is simply set, over learning may occur. Thus, the sparse controller design method is proposed. The proposed method is based on VRFT (virtual reference feedback tuning) and LASSO (least absolute shrinkage and selection operator) regression. A simulation is performed to verify the proposed method. The results show that the sparse controller can be obtained for the controller with high order.