Gain-scheduled Controller Research Articles

Optimised de-rated wind turbine response and loading through extended controller gain-scheduling

An increasing number of wind turbines are expected to be able to participate in the ancillary services that would normally be provided by conventional power plants, as the penetration of the wind energy in the electricity grid is increasing. Specifically, the turbines are needed to be able to control their active power based on the operator command. Constraining the possible output power, i.e. decreasing the power to less than the maximum or rated power, is therefore important for the wind turbine control system. When a turbine is operating under a de-rating strategy, the operating points of the turbine, i.e. the steady-state blade pitch and the tip speed ratio, change compared to the normal case and this needs to be considered within the controller design. Thus, this paper is to study the influence of the de-rating on the turbine controller gain-scheduling and its effect to power output and fatigue damage of the key turbine components. The results show that the wind turbine response is refined through tuning under the different down-regulation operation points, that translate into less fatigue damage of the key turbine components. For a typical multi-megawatt turbine, it is found that the main component lifetime damage can be reduced substantially. In numerical terms, there are up to 6.5% for the tower BM in fore-aft direction and 2.7% for the blade root flapwise direction, which can lead in a prolonged operational lifetime of the wind turbine.

Robust PI, gain scheduled and PSS controller design using LMI regions: Comparative studies

Abstract This paper considers development of a new design procedure for PI and Gain scheduled robust controller with PSS for turbogenerator using LMI regional regions. The obtained PI+PSS and GSC+PSS robust controllers with output and state derivative feedback for uncertain polytopic turbogenerator model ensure that all closed -loop eigenvalues lay in the prescribed LMI region. The proposed method is to guarantee the less conservativeness with respect to previous methods due to introducing a design procedure based on new auxiliary matrices. The effectiveness of this procedure is illustrated by the examples of two controllers for turbogenerator.

Output feedback robust H∞ control for spacecraft rendezvous system subject to input saturation: A gain scheduled approach

In this paper, the problem of output feedback robust H∞ control for spacecraft rendezvous system with parameter uncertainties, disturbances and input saturation is investigated. Firstly, a full-order state observer is designed to reconstruct the full state information, whose gain matrix can be obtained by solving the linear matrix inequality (LMI). Subsequently, by combining the parametric Riccati equation approach and gain scheduled technique, an observer-based robust output feedback gain scheduled control scheme is proposed, which can make full use of the limited control capacity and improve the control performance by scheduling the control gain parameter increasingly. Rigorous stability analyses are shown that the designed discrete gain scheduled controller has faster convergence performance and better robustness than static gain controller. Finally, the performance and advantage of the proposed gain scheduled control scheme are demonstrated by numerical simulation.

Micro-lens fabrication by a long-stroke precision stage with switching control based on model response prediction

This paper develops an automatic control switching mechanism for a long-stroke precision stage intended to improve micro-lens fabrication. The stage consists of a piezoelectric transducer (PZT) stage and a motor stage that achieves centimeter-length travel distances with nanometer precision. We obtained the PZT stage model by experiments and then applied robust loop-shaping techniques to design two controllers: one gave fast responses and the other gave smooth responses. The two controllers were further simplified as robust proportional-integral-derivative (PID) controllers for hardware implementation. We then developed a control switching mechanism, based on model response prediction, to combine the merits of these two controllers. For the motor stage, we derived the stage model by experiments and designed a gain-scheduling controller that regulated the stage to move at the fastest speed for long travel distances until it was within the stroke of the PZT stage. The two stages were then combined with a double-loop control structure to provide a travel range of 10 cm with a precision of 1.23 nm. We implemented the designed control mechanisms and structures for experimental verification. The combined stage was also integrated with a two-photon polymerization (TPP) system to fabricate micro-lenses. The optical properties of the micro-lenses were evaluated to demonstrate the effectiveness of the proposed control in precision positioning for long travel distances.

Guidance and control of standoff air-to-surface carrier vehicle

ABSTRACTThis paper presents an open framework, through which, conventional general purpose aerial munitions can be converted into smart munitions. The retrofit consists of a smart adaptation kit (SAK) having a dedicated Guidance and Control Module (GCM). The adaptation kit along with the GCM ensures that the SAK glide optimally towards the designated target. To reduce cost, the number of control surfaces of the SAK has been kept to a bare minimum, which resulted in an under actuated system. The methodology proposed utilises the theory of gain-scheduled control and leads to an efficient procedure for the design of the controllers, which accurately track reference trajectories defined in an inertial reference frame. The paper illustrates the application of this procedure to the design of stabilisation and tracking controller for the SAK. The design phase is summarised, and the performance of the resulting controllers is assessed in simulation using dynamic model of the vehicle. Simulation results show that apart from improved circular error probable (CEP) of hitting the target, munition ballistic range has also significantly increased with the proposed modification.

Design of gain-scheduling PID controllers for Z-source inverter using iterative reduction-based heuristic algorithms

This paper presents gain-scheduling control strategy for Z-source inverter used in traction motors. An iterative reduction-based heuristic algorithms (IRHA) is introduced for optimization of controller parameters. Since it is difficult to implement a stabilizable optimization with the conventional heuristic algorithm, optimum design can be achieved via creating stable sets by employing IRHA. In this optimization method, a new reduced subset is created at the end of each iteration, thereby reducing computation complexity. The proposed scheme adjusts proportional, integral, differential and derivative kick coefficients (Kp, Ki, Kd and Td). In this study, various PID controllers are designed using IRHA and H∞ norm-based optimization methods. Furthermore, in order to overcome non-linearity which exists in the nature of ZSI, gain scheduling is resorted to. The effectiveness of the proposed gain-scheduled controller structure is demonstrated over different input values and voltage fluctuations.

Fuzzy cognitive network-based maximum power point tracking using a self-tuned adaptive gain scheduled fuzzy proportional integral derivative controller and improved artificial neural network-based particle swarm optimization

The increased demand for electrical energy has driven the development of renewable energy sources. In particular, the conversion of solar energy into electrical energy using photovoltaic (PV) systems has become popular because of its simplicity and low cost. However, the nonlinear characteristics and power fluctuations due to changes in the temperature and irradiation hinder the maximum utilization of the power with a PV system. Thus, the maximum power point tracking (MPPT) control technique is used to extract the maximum available power from PV arrays. Due to insolation and variations in temperature in a PV system, the conventional MPPT techniques are readily trapped by local maxima to significantly reduce the conversion efficiency. In order to overcome this issue, we developed a novel perturb and observe algorithm based on an adaptive fuzzy PID controller with an improved artificial neural network-based particle swarm optimization method for tracking the maximum power point with high tracking speed as well as maintaining the system's stability. In addition, we used a fuzzy cognitive network to maintain the equilibrium state, which is essential for improving the conversion efficiency. Simulation results and performance evaluations using our proposed method demonstrated its suitability for applications in PV systems.

Observer-based state-feedback control design for LPV periodic discrete-time systems

This paper proposes new linear matrix inequality conditions to the observer-based control design problem for Linear Parameter Varying (LPV) periodic discrete-time systems. The time-varying parameters are considered to be available online (measured or estimated). In this way, periodic gain scheduling observers and controllers can be designed. Less conservative results in terms of the H∞ performance can be obtained if the variation rates of the parameters are known, otherwise arbitrarily fast variation can be considered. The H∞ guaranteed cost from the disturbance input to the error signal has been used for the observer design, and the state feedback control design considers the minimization of the H∞ bound from both error signal and input noise to the output of the controlled system. In this way, the designed controller provides robustness against inaccurate estimation. The analysis of the augmented system composed by the observer and controller is performed and numerical experiments illustrate the efficacy of the proposed method.

LMI relaxations for robust gain‐scheduled control of uncertain linear parameter varying systems

This study deals with the problem of robust gain-scheduled dynamic output feedback control for uncertain discrete-time linear parameter varying systems. The obtained controller guarantees an upper bound on the induced $l_2$l 2 -gain performance of the closed-loop system. The system matrices are assumed to depend polynomially on both the scheduling and uncertain parameters which are supposed to belong to intervals with a priori known bounds. To formulate the design problem in a linear matrix inequality (LMI) setting, a required auxiliary matrix is initially determined using a necessary condition for finding a gain-scheduled controller by the proposed method. Then, a robust gain-scheduled controller is designed by LMI conditions combined with a scalar search using the obtained auxiliary matrix. The method is applied to an inverted pendulum on a cart to illustrate the benefits and applicability of the proposed method.

Enhanced Predictor-Based Control Synthesis for Discrete-Time TS Fuzzy Descriptor Systems With Time-Varying Input Delays

We present a novel predictor-feedback gain-scheduled control synthesis method for discrete-time Takagi-Sugeno (TS) fuzzy descriptor systems with unknown time-varying input delays. The goal is to reduce the computational complexity in the control design by keeping the descriptor form whilst improving the closed-loop robust performance. It is worth mentioning the following aspects: 1) our approach easily allows dealing with parametric uncertainties on the descriptor matrix, which are generally difficult to handle via matrix inversion; and 2) the control design problem is casted into linear matrix inequality conditions by using nonquadratic Lyapunov functions, the Projection Lemma and small gain theory. Finally, three examples are provided to show the effectiveness of the proposed method.

Comparative Analysis of Evolutionary Computation Based Gain Scheduling Control for Ball and Plate Stabilization System

The platform stabilization systems used in marine, airborne or land vehicle applications are controlled with very different control methods basically including linear, nonlinear and artificial intelligence-based design techniques. Nowadays, evolutionary computation based optimization algorithms also provide new opportunities to engineers in order to design a gain scheduling controller. In this study, an evolutionary computation based gain scheduling controller is proposed for a ball and plate system so as to examine its control performances on a stabilization system. For this purpose, the swarm intelligence based Particle Swarm Optimization (PSO) and evolutionary algorithm based Differential Evolution (DE) algorithms are chosen due to their better performance than the other evolutionary computation algorithms. The results are comparatively investigated by using time domain and frequency domain analysis methods. Additionally, the robustness analysis is also applied to examine the tuning performances of these controllers in case of changing system parameters in the range of ±50%.

Neural network based controllers for the oil well drilling process

The oil well drilling is a complex process, requiring that the bottomhole pressure be maintained within an operational window, limited by fracture pressure and pore pressure (and/or wellbore collapse). During the drilling process, there are several phenomena that directly influence the annulus bottomhole pressure: kick, lost circulation, inefficient removal of solids, well length increase, reservoir parameters and pipe connection procedure (surge and swab effects). The main objective of this work is to develop neural network controllers able to regulate the annulus bottomhole pressure under pipe connection procedure, kick and lost circulation disturbances. For this purpose, an experimental drilling unit was built, depicting the oil well drilling process primordial characteristics. Besides, real oil well offshore data were employed for validating the neural network controllers. Neural networks were built for predicting the direct plant dynamics (inverse neural network controller) and the inverse plant dynamics (direct neural network controller). It is noteworthy to mention that direct plant dynamics neural modelling was employed as the nonlinear model of a gain scheduling control approach; in addition, the inverse plant dynamics neural modelling constituted a nonlinear controller, directly predicting the manipulated variable. Both controllers employed real-time neural network training, being, therefore, adaptive schemes for regulating the oil well drilling process, which nature is inherently transient.

Downrange manoeuvre and oscillation suppression of a self-regulating centrifugally deployed flexible heat shield using a controlled reaction wheel

A recent study has introduced a flexible deployable heat shield that passively deploys and stiffens due to centrifugal forces generated from a self-regulated autorotation. This paper demonstrates that the heat shield is similar to a PI controlled second order nonlinear system, which explains why the deployment is accompanied by a limit cycle structural oscillation that persists throughout a simulated re-entry. The heat shield design offers a unique capability to actively adjust the deployment using conventional attitude control devices. This operation is explored by simulating the re-entry of a CubeSat-sized vehicle equipped with an off-the-shelf reaction wheel controlled by a switching phase shift controller and gain-scheduled controllers. The effects of the control parameters are investigated, and successful oscillation suppression as well as an open-loop downrange manoeuvre of over 300 km is predicted for re-entry from low earth orbit.

A Comparison of LPV Gain Scheduling and Control Contraction Metrics for Nonlinear Control

Gain-scheduled control based on linear parameter-varying (LPV) models derived from local linearizations is a widespread nonlinear technique for tracking time-varying setpoints. Recently, a nonlinear control scheme based on Control Contraction Metrics (CCMs) has been developed to track arbitrary admissible trajectories. This paper presents a comparison study of these two approaches. We show that the CCM based approach is an extended gain-scheduled control scheme which achieves global reference-independent stability and performance through an exact control realization which integrates a series of local LPV controllers on a particular path between the current and reference states.

Gain-scheduled Control Using Unscented Kalman Filter

Gain-scheduled Control Using Unscented Kalman Filter

Application of LQG and H∞ Gain Scheduling Techniques to Active Suppression of Flutter

Abstract Aircraft flutter behaviour is highly dependent on flight conditions, such as airspeed, altitude and Mach number. Thus when closed-loop control is applied to suppress flutter, if these variations are taken into account then improved performance can be obtained if the controller has a certain degree of adaptivity to the variations. In this paper, a Linear Parameter-Varying (LPV) model of a rectangular wing including a trailing-edge control surface is considered. Two different gain scheduling controllers based on LQG and H∞ techniques are designed to suppress flutter and reject perturbations. Simulations of the closed-loop system show that gain scheduling techniques are capable of fully stabilizing the system over the full range of the considered air velocity, and they increase flutter speed by more than 90%.

Predictive gain scheduled control for Active Mass Damper considering physical constrains

Predictive gain scheduled control for Active Mass Damper considering physical constrains

Air-management and fueling strategy for diesel engines from multi-layer control perspective

This paper proposes a novel control design procedure for air management and fueling strategy (AMFS) of diesel engines in lights of a multi-layer control structure (MLCS). Furthermore, novel sufficient stability conditions in the form of linear matrix inequalities are derived (using slack variables to reduce the conservativeness) for grid-based linear parameter-varying systems. The gain-scheduled controller for AMFS is designed to track a reference torque trajectory requested by higher control layers from MLCS, with the objective of minimizing diesel consumption and pollutants' emissions. For controller design a reduced order grid-based linear parameter-varying model is obtained from the detailed benchmark model published by Eriksson et al. (2016). The controller is validated on the benchmark model using the road profile Soderalje-Norrkoping.

A gain-scheduling control strategy and short-term path optimization with genetic algorithm for autonomous navigation of a sailboat robot

The development of a navigation system for autonomous robotic sailing is a particularly challenging task since the sailboat robot uses unpredictable wind forces for its propulsion besides working in a highly nonlinear and harsh environment, the water. Toward solving the problems that appear in this kind of environment, we propose a navigation system which allows the sailboat to reach any desired target points in its working environment. This navigation system consists of a low-level heading controller and a short-term path planner for situations against the wind. For the low-level heading controller, a gain-scheduling proportional-integral (GS-PI) controller is shown to better describe the nonlinearities inherent to the sailboat movement. The gain-scheduling-PI consists of a table that contains the best control parameters that are learned/defined for a particular maneuver and perform the scheduling according to each situation. The idea is to design specialized controllers which meet the specific control objectives of each application. For achieving short-term path-planned targets, a new approach for optimization of the tacking maneuvering to reach targets against the wind is also proposed. This method takes into account two tacking parameters: the side distance available for the maneuvering and the desired sailboat heading when tacking. An optimization method based on genetic algorithm is used in order to find satisfactory upwind paths. Results of various experiments verify the validity and robustness of the developed methods and navigation system.

Longitudinal, Near-Surface Maneuvering of a Prolate Spheroid

A motion model for a prolate spheroid moving beneath a calm free surface is used to develop a gain-scheduled controller for longitudinal, near-surface maneuvering. The motion model that is used for control design is the impulsive (memory-free) component of a Lagrangian mechanical system model that was recently developed for submerged vessels maneuvering near a free surface. A simple representation of memory effects is included as a perturbation to examine the effectiveness of the feedback controller designed using the simpler model.