Data-driven Control Research Articles

Adaptive data-driven modeling, prediction and optimal control for loaded transmission error of helicopter zerol spiral bevel gear transmission system

In complex multi-stage transmission system especially for the helicopter zerol spiral bevel gears, the loaded transmission error (LTE) is a primary source of noise and vibration, and the misalignment is always an important noise factor affecting geometric and physical performances. An innovative adaptive data-driven LTE modeling, prediction and optimal control are proposed by modifying misalignment evaluations. Firstly, misalignment evaluations on helicopter transmission system are used as the data-driven design variables for tooth contact analysis with error (eTCA). Then, numerical loaded tooth contact analysis (NLTCA) considering flank elastic contact deformation, loaded contact pattern and load distribution is proposed for data-driven LTE modeling. To distinguish with the conventional minimizing LTE, an adaptive data-driven prediction and control system is provided. By prescribing the design target relating to the actual requirements, an adaptive data-driven functional relationship with respect to misalignment evaluations is established. For the input signal namely initial misalignment and its predicted LTE, by predicting its gap from the prescribed design target, this result is used to establish data-driven control objective function. Finally, after giving misalignment optimal operation, adaptive control strategy and solution are proposed to get a robust control result. The numerical instance is provided to verify the proposed methodology.

Data-Driven Tuning of PID Controlled Piezoelectric Ultrasonic Motor

Ultrasonic motors employ resonance to amplify the vibrations of piezoelectric actuator, offering precise positioning and relatively long travel distances and making them ideal for robotic, optical, metrology and medical applications. As operating in resonance and force transfer through friction lead to nonlinear characteristics like creep and hysteresis, it is difficult to apply model-based control, so data-driven control offers a good alternative. Data-driven techniques are used here for iterative feedback tuning of a proportional integral derivative (PID) controller parameters and comparing between different motor driving techniques, single source and dual source dual frequency (DSDF). The controller and stage system used are both produced by the company Physik Instrumente GmbH, where a PID controller is tuned with the help of four search methods: grid search, Luus–Jaakola method, genetic algorithm, and a new hybrid method developed that combines elements of grid search and Luus–Jaakola method. The latter method was found to be quick to converge and produced consistent result, similar to the Luus–Jaakola method. Genetic Algorithm was much slower and produced sub optimal results. The grid search has also proven the DSDF driving method to be robust, less parameter dependent, and produces far less integral position error than the single source driving method.

Sparse data-driven wavefront prediction for large-scale adaptive optics.

This paper presents a computationally efficient wavefront aberration prediction framework for data-driven control in large-scale adaptive optics systems. Our novel prediction algorithm splits prediction into two stages: a high-resolution and a low-resolution stage. For the former, we exploit sparsity structures in the system matrices in a data-driven Kalman filtering algorithm and constrain the identified gain to be likewise sparse; for the latter, we identify a dense Kalman gain and perform corrections to the suboptimal predictions of the former on a smaller grid. This novel prediction framework is able to retain the robustness to measurement noise of the standard Kalman filter in a much more computationally efficient manner, in both its offline and online aspects, while minimally sacrificing performance; its data-driven nature further compensates for modeling errors. As an intermediate result, we present a sparsity-exploiting data-driven Kalman filtering algorithm able to quickly estimate an approximate Kalman gain without solving the Riccati equation.

A lab-scale manufacturing system environment to investigate data-driven production control approaches

Controlling production and release of material into a manufacturing system effectively can lower work-in-progress inventory and cycle time while ensuring the desired throughput. With the extensive data collected from manufacturing systems, developing an effective real-time control policy helps achieving this goal. Validating new control methods using the real manufacturing systems may not be possible before implementation. Similarly, using simulation models can result in overlooking critical aspects of the performance of a new control method. In order to overcome these shortcomings, using a lab-scale physical model of a given manufacturing system can be beneficial. We discuss the construction and the usage of a lab-scale physical model to investigate the implementation of a data-driven production control policy in a production/inventory system. As a data-driven production control policy, the marking-dependent threshold policy is used. This policy leverages the partial information gathered from the demand and production processes by using joint simulation and optimization to determine the optimal thresholds. We illustrate the construction of the lab-scale model by using LEGO Technic parts and controlling the model with the marking-dependent policy with the data collected from the system. By collecting data directly from the lab-scale production/inventory system, we show how and why the analytical modeling of the system can be erroneous in predicting the dynamics of the system and how it can be improved. These errors affect optimization of the system using these models adversely. In comparison, the data-driven method presented in this study is considerably less prone to be affected by the differences between the physical system and its analytical representation. These experiments show that using a lab-scale manufacturing system environment is very useful to investigate different data-driven control policies before their implementation and the marking-dependent threshold policy is an effective data-driven policy to optimize material flow in manufacturing systems.

Data-driven adaptive control of wide-area non-linear systems with input and output saturation: A power system application

In this paper, a Data-Driven Adaptive Controller is proposed for wide-area Multi-Input and Multi-Output (MIMO) non-linear systems featuring input–output saturations. Complexity has always been a major concern in the modeling and control of real-world industrial processes. Traditionally, researchers look for simplified and enough precise models of these systems. However, with the significant advancement in sensor and communication technologies, a considerable amount of data is available from the process which may compensate for the lack of precise models. The emerging data-driven control systems perform both identification and control actions based on data collected from the process, making the system less dependent on a pre-identified model. In this context, an efficient MIMO Data-Driven Adaptive Control (MIMO-DDAC) is designed in this paper for stabilizing a class of unknown non-linear systems. A recursive modeling/control process is proposed for the MIMO systems in the presence of saturation in sensors and actuators, and its stability and performance are mathematically proved. This approach is applied to the load frequency control problem in an interconnected multi-area power grid in the presence of governor saturation, load and parametric uncertainties. Simulation results reveal the effectiveness of the proposed method.

Data-driven process control for manufacturing spiral bevel and hypoid gears by using design for six sigma (DFSS) considering numerical loaded tooth contact analysis (NLTCA)

Data-driven process control considering both geometric and loaded contact performance evaluations has been an increasingly important stage in field of spiral bevel and hypoid gears. A new data-driven manufacturing process control strategy is proposed for a high performance spiral bevel and hypoid gears. Here, to distinguish with the conventional simulated loaded tooth contact analysis (SLTCA) using economical finite element software package, the numerical loaded tooth contact analysis (NLTCA) is of more flexibility and practicality. In light of the advantages of the improved design for six sigma (DFSS), it is integrated with NLTCA for establishing a novel data-driven process control of gear manufacturing. Firstly, in improved DFSS framework, quality function deployment (QFD) is used to determine four sub-objective high-performance evaluation items. Then, their data-driven relationships between machine settings are respectively determined by using NLTCA. In particular, the manufacturing process control is further converted into multi-objective optimization (MOO) modification of the hypoid generator settings. Finally, an interactive preference point approach is applied for data-driven control of its iterative step and it can obtain a robust solution from Pareto optimal front. A case study is provided to verify the proposed methodology.

An adaptive data-driven controller for underwater manipulators with variable payload

The underwater environment poses a complex problem for developing control systems for underwater manipulators. Modeling the system is a complicated and costly process due to the highly nonlinear dynamics and the presence of unknown hydrodynamical effects. Furthermore, manipulators are usually deployed on Autonomous Underwater Vehicles (AUVs) which further influence and change the dynamics of the arm. This is aggravated in underwater operations where manipulating different objects is necessary. These diverse manipulation tasks introduce external disturbances to the system and can lead to a fast degradation of the control system performance. In this article, we propose a novel adaptive controller for underwater robot manipulators that have to handle varying payloads with different masses, geometries, and buoyant forces. The proposed control strategy utilizes a data-driven model of the system in an optimal control formulation based on neural networks. Moreover, we developed an online tuning strategy, based on the adaptive interaction theory, which allows the gains of the controller to be updated online with respect to a set of performance metrics. Experiments were performed with the robotic arm manipulating a variety of payloads while mounted on both a fixed base and a free-floating vehicle. We present a number of simulated and experimental results that illustrate the benefits of the proposed strategy. In addition, a comparative study against a classical Model Predictive Control (MPC) demonstrates the benefits of our adaptive proposal.

Koopman-based data-driven control for continuous fluidized bed spray granulation with screen-mill-cycle

Continuous fluidized bed spray granulation processes with external screen-mill cycle are known to be susceptible to nonlinear oscillations. As has been shown recently, mass control may induce periodical oscillation of the particle size distribution in fine grinding regimes, which requires the design of additional stabilizing control. In this contribution a data-driven control approach is investigated. It is based on the identification of the Koopman-based linear representation of the system and does not require any manual system modeling as it is based purely on input–output measurements. The resulting representation makes possible a linear–quadratic control design, that does not depend on a specific operational point. The proposed approach was successfully validated in a numerical study.

Direct data-driven design of LPV controllers with soft performance specifications

If only experimental measurements are available, direct data-driven control design becomes an appealing approach, as control performance is directly optimized based on the collected samples. The direct synthesis of a feedback controller from input-output data typically requires the blind choice of a reference model, that dictates the desired closed-loop behavior. In this paper, we propose a data-driven design scheme for linear parameter-varying (LPV) systems to account for soft performance specifications. Within this framework, the reference model is treated as an additional hyper-parameter to be learned from data, while the user is asked to provide only indicative performance constraints. The effectiveness of the proposed approach is demonstrated on a benchmark simulation case study, showing the improvement achieved by allowing for a flexible reference model.

Experimental Verification of Self-Adapting Data-Driven Controllers in Active Distribution Grids

Lately, data-driven algorithms have been proposed to design local controls for Distributed Generators (DGs) that can emulate the optimal behaviour without any need for communication or centralised control. The design is based on historical data, advanced off-line optimization techniques and machine learning methods, and has shown great potential when the operating conditions are similar to the training data. However, safety issues arise when the real-time conditions start to drift away from the training set, leading to the need for online self-adapting algorithms and experimental verification of data-driven controllers. In this paper, we propose an online self-adapting algorithm that adjusts the DG controls to tackle local power quality issues. Furthermore, we provide experimental verification of the data-driven controllers through power Hardware-in-the-Loop experiments using an industrial inverter. The results presented for a low-voltage distribution network show that data-driven schemes can emulate the optimal behaviour and the online modification scheme can mitigate local power quality issues.

Direct tuning of the data-driven controller considering closed-loop stability based on a fictitious reference signal

The direct tuning of controller parameters, which is based on data-driven control, has been attracting considerable attention because of the ease of its control system design. In practical use, it is important to consider the stability of the closed-loop system and model matching with few design parameters. In this study, we propose a direct tuning method based on a fictitious reference signal that considers the bounded-input bounded-output (BIBO) and model matching without repeating experiments. The proposed method includes two steps. In the first step, the BIBO stability is satisfied. The pole information is lost in the cost function of the conventional method using a fictitious reference signal. Then, we derive a new cost function that can prevent the loss of the pole information. This provides controller parameters that can stabilize the closed-loop system. The model matching between the reference model and the closed-loop system is considered in the second step. When model matching is achieved, the characteristics of the reference model almost match those of the closed-loop system, including the gain and phase margins. The parameters of the reference model are automatically tuned to realize model matching. Using the two-step method, we can obtain parameters considering BIBO stability and the model matching. In addition, there are no design parameters apart from the dealing noise. Two simulations and an experiment were performed on a system with dead time to verify the effectiveness of the proposed two-step method. The results showed that the proposed method provides BIBO stability and model-matched control parameters from the measured data through a one-time experiment without trial and error.

Design of Syncretic Fuzzy-Neural Control for WWTP

Owing to the possible existence of system failures and packet dropouts in the wastewater treatment process (WWTP), it is difficult to obtain sufficient data, which will result in data shortage. Therefore, it is a challenge to design an effective data-driven controller with the above data shortage issue for WWTP. To solve this problem, a syncretic fuzzy-neural controller (SFNC) was developed and analyzed in this article. First, the knowledge obtained from the operation conditions was made full use by a knowledge reconstruction mechanism to construct the initial condition of SFNC. Then, the proposed SFNC was able to obtain the accurate parameters and compact structure in the initialization phase. Second, a syncretic-form strategy (SFS) was designed to syncretize the knowledge from the fuzzy rules and the data from the operation process to optimize the structure of SFNC. Then, the adaptability of SFNC can be improved to achieve good control performance in the presence of insufficient data. Third, the stability of the developed SFNC was proved by using Lyapunov stability theorem. Then, the stability of SFNC was given to guarantee its successful application. Finally, the proposed controller was tested on the Benchmark Simulation Model No.1 to confirm its effectiveness. The results demonstrated that the proposed SFNC can achieve superior control performance than some other existing controllers.

Event-Based Projective Synchronization for Different Dimensional Complex Dynamical Networks with Unknown Dynamics by Using Data-Driven Scheme

In this paper, the projective synchronization problem for different dimensional complex networks (CNs) with unknown dynamics is investigated. First, by selecting a projective matrix, the error system is obtained and an event-based projective synchronization control policy is proposed to realize the projective synchronization between two complex networks with different dimensions. It is revealed that the projective synchronization problem can be transformed into the optimal regulation of the error system with a performance function. Then, a data-driven control scheme is proposed to implement the event-trigged projective synchronization control policy, which is composed of identifier, critic network and actuator. The identifier is applied to estimate the unknown dynamics. The actuator is employed to construct the control inputs and the optimal value is estimated by the critic network. Both actuator and critic network are based on neural networks. The neural network weights and controller are updated at event-triggered instant so that the computing and communication resources can be saved. By employing appropriate event-triggered threshold and learning rate of neural network, the synchronization error is proved to be be asymptotically approaching zero and the Zeno behaviors are excluded. Finally, a numerical example is given to verify the effectiveness of the obtained results.

Gain-Preserving Data-Driven Approximation of the Koopman Operator and Its Application in Robust Controller Design

In this paper, we address the data-driven modeling of a nonlinear dynamical system while incorporating a priori information. The nonlinear system is described using the Koopman operator, which is a linear operator defined on a lifted infinite-dimensional state-space. Assuming that the L2 gain of the system is known, the data-driven finite-dimensional approximation of the operator while preserving information about the gain, namely L2 gain-preserving data-driven modeling, is formulated. Then, its computationally efficient solution method is presented. An application of the modeling method to feedback controller design is also presented. Aiming for robust stabilization using data-driven control under a poor training dataset, we address the following two modeling problems: (1) Forward modeling: the data-driven modeling is applied to the operating data of a plant system to derive the plant model; (2) Backward modeling: L2 gain-preserving data-driven modeling is applied to the same data to derive an inverse model of the plant system. Then, a feedback controller composed of the plant and inverse models is created based on internal model control, and it robustly stabilizes the plant system. A design demonstration of the data-driven controller is provided using a numerical experiment.

Data-driven based eco-driving control for plug-in hybrid electric vehicles

With the development of connected and automated vehicles, eco-driving control is reckoned to generate unprecedented potential on energy-saving in electrified powertrain. In this paper, a data-driven based eco-driving control strategy with efficient computation capacity is proposed for plug-in hybrid electric vehicles to achieve approximate optimal energy economy. An efficient hierarchical optimal control scheme is designed to mitigate the massive computational cost during velocity optimization and powertrain control. A data-driven optimal energy consumption cost model and an optimal battery current model are respectively constructed via two neural networks and served as the critic model and the system model during velocity optimization. Furthermore, the neural network-based dynamic programming is exploited to optimize the vehicle velocity by merging the data-driven models and Bellman optimality principle. The simulation results demonstrate that the proposed method can remarkably improve fuel economy by up to 16.7% in complicated driving conditions, compared with conventional sequential optimization methods. Furthermore, the data-driven control scheme can drastically improve the computational efficiency with slight sacrifice on fuel economy, compared with the optimum benchmark.

Data-driven robust controller design by geometric constraints in frequency-domain

This paper aims to propose an improved method for designing the robust controller that satisfies the mixed sensitivity criterion. In robust controller design using frequency-domain data, the important task is to find the appropriate constraints for the design parameters. In our method, the controller is designed with fixed-order linearly parameterized structure and the desired robust performance criteria are represented by the geometrical constraints for open-loop frequency characteristic. Then the constraints lead to the convex searching problem for the parameters of controller so that we obtain the optimal parameters by the numerical iterative algorithm for the finite number of the linear inequalities. The significance of our research is that the proposed method is more reliable for the robust performance criterion because it does not require the approximation for the constraint. The control simulation for the robust PID controller shows the improved transient response.

Model-Based Evaluation of a Data-Driven Control Strategy: Application to Ibuprofen Crystallization

This work presents a methodology that relies on the application of the radial basis functions network (RBF)-based feedback control algorithms to a pharmaceutical crystallization process. Within the scope of the model-based evaluation of the proposed strategy, firstly strategies for the data treatment, data structure and the training methods reflecting the possible scenarios in the industry (Moving Window, Growing Window and Golden Batch strategies) were introduced. This was followed by the incorporation of such RBF strategies within a soft sensor application and a nonlinear predictive data-driven control application. The performance of the RBF control strategies was tested for the undisturbed cases as well as in the presence of disturbances in the process. The promising results from both RBF soft sensor control and the RBF predictive control demonstrated great potential of these techniques for the control of the crystallization process. In particular, both Moving Window and Golden Batch strategies performed the best results for an RBF soft sensor, and the Growing Window outperformed the remaining methodologies for predictive control.

Virtual unmodeled dynamic compensation and data-driven nonlinear generalized predictive control

For a class of discrete-time nonlinear dynamic systems, a nonlinear generalized predictive control method is proposed, which combines the conventional generalized predictive control with data-driven control, the feedback control with feedforward compensation, and the signal compensation with the regulator principle. The input-output dynamic equations of the closed-loop system are derived, and the performance of the proposed control algorithm is analyzed. The advantages and effectiveness of the proposed control algorithm are demonstrated through comparative experiments.

Low-complexity learning of Linear Quadratic Regulators from noisy data

This paper considers the Linear Quadratic Regulator problem for linear systems with unknown dynamics, a central problem in data-driven control and reinforcement learning. We propose a method that uses data to directly return a controller without estimating a model of the system. Sufficient conditions are given under which this method returns a stabilizing controller with guaranteed relative error when the data used to design the controller are affected by noise. This method has low complexity as it only requires a finite number of samples of the system response to a sufficiently exciting input, and can be efficiently implemented as a semi-definite programme.

Data-driven active flutter control of airfoil with input constraints based on adaptive dynamic programming method

The stable flight region can be extended by adding control flap at the wing trailing edge and combined with active control technology. We studied the active flutter control by considering the input constraints. By designing the data-driven optimal controller, the limit cycle oscillations of a typical two-dimensional airfoil wing can be suppressed with single trailing edge control surface. The traditional control methods always need a precise mathematical model of the system, which put high requirements on system modeling. In this study, a novel data-driven optimal controller is proposed by using the input–output data and without depending on the nonlinear system dynamic model. This model-free approach avoids the effects of modeling errors and system uncertainty. When the data-driven controller is applied, the limit cycle oscillation phenomenon of the airfoil wing is eliminated within several seconds. It can be seen from the numerical simulation result that the data-driven adaptive dynamic programming control method possess superiority and feasibility.