Robust control examples applied to a wind turbine simulated model

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modeling and control become challenging problems. On the one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behavior. Therefore, the development of modeling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of the development of robust control strategies when applied to a simulated wind turbine plant. Extended simulations with the wind turbine benchmark model and the Monte Carlo tool represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics.Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modeling and control become challenging problems. On the one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behavior. Therefore, the development of modeling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of the development of robust control strategies when applied to a simulated wind turbine plant. Extended simulations with the wind turbine benchmark model and the Monte Carlo tool represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics

Robust and Decentralized Control of Web Winding Systems

This research addresses the velocity and tension regulation problems in web handling, including those found in the single element of an accumulator and those in the large-scale system settings. A continuous web winding system is a complex large-scale interconnected dynamics system with numerous tension zones to transport the web while processing it. A major challenge in controlling such systems is the unexpected disturbances that propagate through the system and affect both tension and velocity loops along the way. To solve this problem, a unique active disturbance rejection control (ADRC) strategy is proposed. Simulation results show remarkable disturbance rejection capability of the proposed control scheme in coping with large dynamic variations commonly seen in web winding systems. Another complication in web winding system stems from its large-scale and interconnected dynamics which makes control design difficult. This motivates the research in formulating a novel robust decentralized control strategy. The key idea in the proposed approach is that nonlinearities and interactions between adjunct subsystems are regarded as perturbations, to be estimated by an augmented state observer and rejected in the control loop, therefore making the local control design extremely simple. The proposed decentralized control strategy was implemented on a 3-tension-zone web winding processing line. Simulation results show that the proposed control method leads to much better tension and velocity regulation quality than the existing controller common in industry. Finally, this research tackles the challenging problem of stability analysis. Although ADRC has demonstrated the validity and advantage in many applications, the rigorous stability study has not been fully addressed previously. To this end, stability characterization of ADRC is carried out in this work. The closed-loop system is first reformulated, resulting in a form that allows the application of the well established singular perturbation method. Based on the decom

Robust and Decentralized Control of Web Winding Systems

This research addresses the velocity and tension regulation problems in web handling, including those found in the single element of an accumulator and those in the large-scale system settings. A continuous web winding system is a complex large-scale interconnected dynamics system with numerous tension zones to transport the web while processing it. A major challenge in controlling such systems is the unexpected disturbances that propagate through the system and affect both tension and velocity loops along the way. To solve this problem, a unique active disturbance rejection control (ADRC) strategy is proposed. Simulation results show remarkable disturbance rejection capability of the proposed control scheme in coping with large dynamic variations commonly seen in web winding systems. Another complication in web winding system stems from its large-scale and interconnected dynamics which makes control design difficult. This motivates the research in formulating a novel robust decentralized control strategy. The key idea in the proposed approach is that nonlinearities and interactions between adjunct subsystems are regarded as perturbations, to be estimated by an augmented state observer and rejected in the control loop, therefore making the local control design extremely simple. The proposed decentralized control strategy was implemented on a 3-tension-zone web winding processing line. Simulation results show that the proposed control method leads to much better tension and velocity regulation quality than the existing controller common in industry. Finally, this research tackles the challenging problem of stability analysis. Although ADRC has demonstrated the validity and advantage in many applications, the rigorous stability study has not been fully addressed previously. To this end, stability characterization of ADRC is carried out in this work. The closed-loop system is first reformulated, resulting in a form that allows the application of the well established singular perturbation method. Based on the decom

Lyapunov-function-based design of fuzzy logic controllers and its application on combining controllers

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Data-Driven and Model-Based Control Techniques for a Wind Turbine Benchmark Model

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high–fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Extended simulations with the wind turbine benchhmark model and the Monte–Carlo tool represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model–reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics

On the robustness of the slotine-Li and the FPT/SVD-based adaptive controllers

A comparative study concerning the robustness of a novel, Fixed Point Transformations/Singular Value Decomposition (FPT/SVD)-based adaptive controller and the Slotine-Li (S&L) approach is given by numerical simulations using a three degree of freedom paradigm of typical Classical Mechanical systems, the cart + double pendulum. The effects of the imprecision of the available dynamical model, presence of dynamic friction at the axles of the drives, and the existence of external disturbance forces unknown and not modeled by the controller are considered. While the Slotine-Li approach tries to identify the parameters of the formally precise, available analytical model of the controlled system with the implicit assumption that the generalized forces are precisely known, the novel one makes do with a very rough, affine form and a formally more precise approximate model of that system, and uses temporal observations of its desired vs. realized responses. Furthermore, it does not assume the lack of unknown perturbations caused either by internal friction and/or external disturbances. Its another advantage is that it needs the execution of the SVD as a relatively time-consuming operation on a grid of a rough system-model only one time, before the commencement of the control cycle within which it works only with simple computations. The simulation examples exemplify the superiority of the FPT/SVD-based control that otherwise has the deficiency that it can get out of the region of its convergence. Therefore its design and use needs preliminary simulation investigations. However, the simulations also exemplify that its convergence can be guaranteed for various practical purposes

Safety Assurance of Non-Deterministic Flight Controllers in Aircraft Applications

Loss of control is a serious problem in aviation that primarily affects General Aviation. Technological advancements can help mitigate the problem, but the FAA certification process makes certain solutions economically unfeasible. This investigation presents the design of a generic adaptive autopilot that could potentially lead to a single certification for use in several makes and models of aircraft. The autopilot consists of a conventional controller connected in series with a robust direct adaptive model reference controller. In this architecture, the conventional controller is tuned once to provide outer-loop guidance and navigation to a reference model. The adaptive controller makes unknown aircraft behave like the reference model, allowing the conventional controller to successfully provide navigation without the need for retuning. A strong theoretical foundation is presented as an argument for the safety and stability of the controller. The stability proof of direct adaptive controllers require that the plant being controlled has no unstable transmission zeros and has a nonzero high frequency gain. Because most conventional aircraft do not readily meet these requirements, a process known as sensor blending was used. Sensor blending consists of using a linear combination of the plant’s outputs that has no unstable transmission zeros and has a nonzero high frequency gain to drive the adaptive controller. Although this method does not present a problem for regulators, it can lead to a steady state error in tracking applications. The sensor blending theory was expanded to take advantage of the system’s dynamics to allow for zero steady state error tracking. This method does not need knowledge of the specific system’s dynamics, but instead uses the structure of the A and B matrices to perform the blending for the general case. The generic adaptive autopilot was tested in two high-fidelity nonlinear simulators of two typical General Aviation aircraft. The results show that the autopilot was able to adapt appropriately to the different aircraft and was able to perform three-dimensional navigation and an ILS approach, without any modification to the controller. The autopilot was tested in moderate atmospheric turbulence, using consumer-grade sensors and actuators currently available in General Aviation aircraft. The generic adaptive autopilot was shown to be robust to atmospheric turbulence and sensor and actuator random noise. In both aircraft simulators, the autopilot adapted successfully to changes in airspeed, altitude, and configuration. This investigation proves the feasibility of a generic autopilot using direct adaptive controller. The autopilot does not need a priori information of the specific aircraft’s dynamics to maintain its safety and stability arguments. Real-time parameter estimation of the aircraft dynamics are not needed. Recommendations for future work are provided

Design and Implementation of Adaptive PID and Adaptive Fuzzy Controllers for a Level Process Station

This proposed work proposes the design and real-time implementation of an adaptive fuzzy logic controller (FLC) and a proportional-integral-derivative (PID) controller for adaptive gain scheduling that can be configured for any complex industrial nonlinear application. Initially, the open-loop test of the single-input single-output (SISO) system, with nonlinearities and disturbances, is conducted to represent the mathematical model of the process around a set of equilibrium points. The adaptive controllers are then developed and deployed by using the national instruments reconfigurable input/output data acquisition device (NI RIO), NI myRIO-1900, and the control parameters are adapted in real-time corresponding to the changes in the process variable. The resulting servo and regulatory performance of the controllers are compared in MATLAB® software. The adaptive fuzzy controller is deduced to be the better controller as it can generate the desired output with quicker settling times, fewer oscillations, and negligible overshoot

An improved Lyapunov function based stability analysis method for fuzzy logic control systems

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