Micro Swirl Generation by Piezoelectric Actuator via a Bouc-Wen Model based Sliding Model Control

Author(s):  
Bin Jiang ◽  
Tadayoshi Aoyama ◽  
Wanfeng Shang
2021 ◽  
Vol 11 (21) ◽  
pp. 10369
Author(s):  
Štefan Chamraz ◽  
Mikuláš Huba ◽  
Katarína Žáková

This paper contributes toward research on the control of the magnetic levitation plant, representing a typical nonlinear unstable system that can be controlled by various methods. This paper shows two various approaches to the solution of the controller design based on different closed loop requirements. Starting from a known unstable linear plant model—the first method is based on the two-step procedure. In the first step, the transfer function of the controlled system is modified to get a stable non-oscillatory system. In the next step, the required first-order dynamic is defined and a model-based PI controller is proposed. The closed loop time constant of this first-order model-based approach can then be used as a tuning parameter. The second set of methods is based on a simplified ultra-local linear approximation of the plant dynamics by the double-integrator plus dead-time (DIPDT) model. Similar to the first method, one possible solution is to stabilize the system by a PD controller combined with a low-pass filter. To eliminate the offset, the stabilized system is supplemented by a simple static feedforward, or by a controller proposed by means of an internal model control (IMC). Another possible approach is to apply for the DIPDT model directly a stabilizing PID controller. The considered solutions are compared to the magnetic levitation system, controlled via the MATLAB/Simulink environment. It is shown that, all three controllers, with integral action, yield much slower dynamics than the stabilizing PD control, which gives one motivation to look for alternative ways of steady-state error compensation, guaranteeing faster setpoint step responses.


Author(s):  
Seungwoo Hong ◽  
Inseok Park ◽  
Myoungho Sunwoo

This paper proposes a model-based gain scheduling strategy of a Skogestad internal model control (SIMC)-based boost pressure controller for passenger car diesel engines. This gain scheduling strategy is proposed with a new scheduling variable to handle the nonlinear variable geometric turbocharger (VGT) plant characteristics. The scheduling variable is derived from the pressure ratio between the exhaust and intake manifolds and the exhaust air-to-fuel ratio to estimate the static gain of the VGT plant, which varies widely with change in the engine operating conditions. The proposed static gain model was designed with the scheduling variable, engine speed, and fuel injection quantity. Compared to the steady-state experimental data, the static gain model showed an R-squared value of 0.91. The boost pressure controller had the proportional-integral (PI) structure to allow for online calibration, and the PI gains were determined using the SIMC method. The proposed static gain model for the VGT plant was integrated into the SIMC control structure to obtain the appropriate control gains under wide engine operating area. The proposed control algorithm was compared with a fixed gain boost pressure controller through various step tests of the desired boost pressure. The fixed gain controller showed a large overshoot of 64% when the exhaust gas recirculation (EGR) operating condition was changed. In contrast, the proposed gain scheduled boost pressure controller reduced the overshoot to 12%. The model-based gain scheduling strategy successfully adjusted the control gains to achieve consistent control performance under various engine operating conditions.


2017 ◽  
Author(s):  
Carl R. Shapiro ◽  
Johan Meyers ◽  
Charles Meneveau ◽  
Dennice F. Gayme

Abstract. We investigate the use of wind farms to provide secondary frequency regulation for a power grid using a model-based receding horizon control framework. In order to enable real-time implementation, the control actions are computed based on a time-varying one-dimensional wake model. This model describes wake advection and wake interactions, both of which play an important role in wind farm power production. In order to test the control strategy, it is implemented in a large eddy simulation (LES) model of an 84-turbine wind farm using the actuator disk turbine representation. Rotor-averaged velocity measurements at each turbine are used to provide feedback for error correction. The importance of including the dynamics of wake advection in the underlying wake model is tested by comparing the performance of this dynamic-model control approach to a comparable static-model control approach that relies on a modified Jensen model. We compare the performance of both control approaches using two types of regulation signals, "RegA'" and "RegD", which are used by PJM, an independent system operator in the Eastern United States. The poor performance of the static-model control relative to the dynamic-model control demonstrates that modeling the dynamics of wake advection is key to providing the proposed type of model-based coordinated control of large wind farms. We further explore the performance of the dynamic-model control via composite performance scores used by PJM to qualify plants for regulation. Our results demonstrate that the dynamic-model controlled wind farm consistently performs well, passing the qualification threshold for all fast-acting RegD signals. For the RegA signal, which changes over slower time scales, the dynamic-model control leads to average performance that surpasses the qualification threshold, but further work is needed to enable this controlled wind farm to achieve qualifying performance for all regulation signals.


2018 ◽  
Vol 3 (1) ◽  
pp. 11-24 ◽  
Author(s):  
Carl R. Shapiro ◽  
Johan Meyers ◽  
Charles Meneveau ◽  
Dennice F. Gayme

Abstract. This paper is an extended version of our paper presented at the 2016 TORQUE conference (Shapiro et al., 2016). We investigate the use of wind farms to provide secondary frequency regulation for a power grid using a model-based receding horizon control framework. In order to enable real-time implementation, the control actions are computed based on a time-varying one-dimensional wake model. This model describes wake advection and wake interactions, both of which play an important role in wind farm power production. In order to test the control strategy, it is implemented in a large-eddy simulation (LES) model of an 84-turbine wind farm using the actuator disk turbine representation. Rotor-averaged velocity measurements at each turbine are used to provide feedback for error correction. The importance of including the dynamics of wake advection in the underlying wake model is tested by comparing the performance of this dynamic-model control approach to a comparable static-model control approach that relies on a modified Jensen model. We compare the performance of both control approaches using two types of regulation signals, “RegA” and “RegD”, which are used by PJM, an independent system operator in the eastern United States. The poor performance of the static-model control relative to the dynamic-model control demonstrates that modeling the dynamics of wake advection is key to providing the proposed type of model-based coordinated control of large wind farms. We further explore the performance of the dynamic-model control via composite performance scores used by PJM to qualify plants for regulation services or markets. Our results demonstrate that the dynamic-model-controlled wind farm consistently performs well, passing the qualification threshold for all fast-acting RegD signals. For the RegA signal, which changes over slower timescales, the dynamic-model control leads to average performance that surpasses the qualification threshold, but further work is needed to enable this controlled wind farm to achieve qualifying performance for all regulation signals.


Author(s):  
Shahid Mahmood ◽  
Ian A. Griffin ◽  
Peter J. Fleming ◽  
Arthur J. Shutler

The Rolls-Royce Inverse Model (RRIM) controller is a nonlinear, model-based fuel control algorithm. This paper compares the model-based design procedures and resulting performance of RRIM control to those of Classical Gain Scheduled (CGS) control for a three-spool gas turbine. It was observed that similar performance levels can be achieved using the RRIM with a significant decrease in tuning effort and design time when compared to CGS control. The RRIM controller also showed improved performance for the case of transient control. The paper indicates how and why the RRIM controller is robust across the operating envelope and highlights the practical advantages it affords to the industrial designer.


Author(s):  
Q Li

Parallel structure robots have been receiving growing attention from both academia and industry in recent years. This is due to their advantages over serial structure robots, such as high stiffness, high motion accuracy and a high load-structure ratio. Control of parallel robots, however, produces difficulties to control engineers due to the modelling errors arising from the highly non-linear and complex structures. This paper proposes a dual-model-based structure for error attenuation in the trajectory-tracking control of a parallel robot manipulator. In this design, a conventional model-based control algorithm employing an estimated robot dynamic model is first implemented in the inner loop of the control structure. Then, in order to reduce the unwanted effects caused by modelling erros, another model-based structure, developed based on the concept of the internal model control, is appended in the outer loop of the control structure as a compensator. A combination of these two model-based components results in a novel dual-model-based structure for parallel robot control. Sensitivity analyses show that the effects due to modelling errors and external disturbances can be significantly reduced by applying this new control structure without relying on a high-gain control solution. The effectiveness of this control design is successfully demonstrated by numerical studies on a planar parallel robot with 2 degrees of freedom.


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