Data-driven controller unfalsification with analytic update applied to a motion system

Ellipsoidal unfalsified control is a plant-model-free, data-driven control design method. It recursively checks, using available data, whether the ability of a controller to meet a predefined performance requirement is (un)falsified. The set of unfalsified controllers is described by an ellipsoid in the control parameter space. The update of the ellipsoid employing new measurements can be computed analytically, hence, it is computationally cheap. This adaptive scheme is applied to an experimental motion system, namely to an industrial inkjet printer at a sample rate of 1 kHz. The results clearly show that the algorithm updates the control parameter set when the performance requirement is not met with the currently implemented one. The resulting closed-loop behavior resembles the predefined reference model in the dominant frequency range

On-line direct control design for nonlinear systems

An approach to design a feedback controller for nonlinear systems directly from experimental data is presented. Improving over a recently proposed technique, which employs exclusively a batch of experimental data collected in a preliminary experiment, here the control law is updated and rened during real-time operation, hence enabling an on-line learning capability. The theoretical properties of the described approach, in particular closed-loop stability and tracking accuracy, are discussed. Finally, the experimental results obtained with a water tank laboratory setup are presented

Neural Network Compensation Control for Output Power Optimization of Wind Energy Conversion System Based on Data-Driven Control

Due to the uncertainty of wind and because wind energy conversion systems (WECSs) have strong nonlinear characteristics, accurate model of the WECS is difficult to be built. To solve this problem, data-driven control technology is selected and data-driven controller for the WECS is designed based on the Markov model. The neural networks are designed to optimize the output of the system based on the data-driven control system model. In order to improve the efficiency of the neural network training, three different learning rules are compared. Analysis results and SCADA data of the wind farm are compared, and it is shown that the method effectively reduces fluctuations of the generator speed, the safety of the wind turbines can be enhanced, the accuracy of the WECS output is improved, and more wind energy is captured

Real-Time Variable Multidelay Controller for Multihazard Mitigation

High performance control systems (HPCS), including semiactive, active, and hybrid damping systems, are effective solutions to increase structural performance versus multihazard excitations. However, the implementation of HPCS within structural systems is still in its infancy, because of the complexity in designing a robust closed-loop control system that can ensure reliable and high mitigation performance. To overcome this challenge, a new type of controller with high adaptive capabilities is proposed. The control algorithm is based on real-time embedding of measurements to minimally represent the essential dynamics of the controlled system, therefore providing adaptive input space capabilities. This type of controller is termed an input-space dependent controller. In this paper, a specialized case of input-space dependent controller is investigated, where the embedding dimension is fixed, but the time delay used in the construction of the embedding varies with time. This constitutes a variable multidelay controller (VMDC), which includes an algorithm enabling the online selection of a time delay based on information theory. Here, optimal time delay selection is first studied and its applicability of the VMDC algorithm demonstrated. Numerical simulations are conducted on a single-degree-of-freedom (SDOF) system to study the performance of the VMDC versus different control strategies. Results show a significant gain in performance from the inclusion of an adaptive input space, and that the algorithm was robust with respect to noise. Simulations also demonstrate that critical gains in performance could be obtained from added knowledge in the system’s dynamics by comparing mitigation results with a linear quadratic regulator (LQR) controller. Additional simulations are conducted on a three degrees-of-freedom (3DOF) system, which consists of a model structure equipped with an actuator and subjected to nonsimultaneous multihazards. Results show enhanced mitigation performance of the VMDC versus LQR strategies when using limited-state feedback, validating the capability of the controller at mitigating vibrations based on limited knowledge and limited measurements, and thus its promise at multihazard applications

Unfalsified visual servoing for simultaneous object recognition and pose tracking

In a complex environment, simultaneous object recognition and tracking has been one of the challenging topics in computer vision and robotics. Current approaches are usually fragile due to spurious feature matching and local convergence for pose determination. Once a failure happens, these approaches lack a mechanism to recover automatically. In this paper, data-driven unfalsified control is proposed for solving this problem in visual servoing. It recognizes a target through matching image features with a 3-D model and then tracks them through dynamic visual servoing. The features can be falsified or unfalsified by a supervisory mechanism according to their tracking performance. Supervisory visual servoing is repeated until a consensus between the model and the selected features is reached, so that model recognition and object tracking are accomplished. Experiments show the effectiveness and robustness of the proposed algorithm to deal with matching and tracking failures caused by various disturbances, such as fast motion, occlusions, and illumination variation

Model-based and data-based frequency domain design of fixed structure robust controller: a polynomial optimization approach

L'abstract è presente nell'allegato / the abstract is in the attachmen

Model-Guided Data-Driven Optimization and Control for Internal Combustion Engine Systems

The incorporation of electronic components into modern Internal Combustion, IC, engine systems have facilitated the reduction of fuel consumption and emission from IC engine operations. As more mechanical functions are being replaced by electric or electronic devices, the IC engine systems are becoming more complex in structure. Sophisticated control strategies are called in to help the engine systems meet the drivability demands and to comply with the emission regulations. Different model-based or data-driven algorithms have been applied to the optimization and control of IC engine systems. For the conventional model-based algorithms, the accuracy of the applied system models has a crucial impact on the quality of the feedback system performance. With computable analytic solutions and a good estimation of the real physical processes, the model-based control embedded systems are able to achieve good transient performances. However, the analytic solutions of some nonlinear models are difficult to obtain. Even if the solutions are available, because of the presence of unavoidable modeling uncertainties, the model-based controllers are designed conservatively