Gain Scheduling Strategy Research Articles

A Bio-Inspired Glucose Controller Based on Pancreatic β-Cell Physiology

Control algorithms for closed-loop insulin delivery in type 1 diabetes have been mainly based on control engineering or artificial intelligence techniques. These, however, are not based on the physiology of the pancreas but seek to implement engineering solutions to biology. Developments in mathematical models of the β-cell physiology of the pancreas have described the glucose-induced insulin release from pancreatic β cells at a molecular level. This has facilitated development of a new class of bio-inspired glucose control algorithms that replicate the functionality of the biological pancreas. However, technologies for sensing glucose levels and delivering insulin use the subcutaneous route, which is nonphysiological and introduces some challenges. In this article, a novel glucose controller is presented as part of a bio-inspired artificial pancreas. A mathematical model of β-cell physiology was used as the core of the proposed controller. In order to deal with delays and lack of accuracy introduced by the subcutaneous route, insulin feedback and a gain scheduling strategy were employed. A United States Food and Drug Administration-accepted type 1 diabetes mellitus virtual population was used to validate the presented controller. Premeal and postmeal mean ± standard deviation blood glucose levels for the adult and adolescent populations were well within the target range set for the controller [(70, 180) mg/dl], with a percent time in range of 92.8 ± 7.3% for the adults and 83.5 ± 14% for the adolescents. This article shows for the first time very good glucose control in a virtual population with type 1 diabetes mellitus using a controller based on a subcellular β-cell model.

Towards neutral steer and sideslip reduction for four-wheeled electric vehicles

This paper proposes an approach to achieving both neutral steer and sideslip reduction for four-wheeled electric vehicles. The control problem is reduced to constructing a servo system tracking appropriate reference input, where the tracking is realized in the framework of H ∞ control. To deal with time-varying vehicle velocity for practical purpose, a gain scheduling strategy is developed to obtain the controller, where the lower and upper bounds of the velocity are used to obtain a polytopic range for the parameters in the system coefficient matrices. A numerical example is given to show validity of the proposed approach.

Effects of multiple MR dampers controlled by fuzzy-based strategies on structural vibration reduction

Fuzzy logic based control has recently been proposed for regulating the properties of magnetorheological (MR) dampers in an effort to reduce vibrations of structures subjected to seismic excitations. So far, most studies showing the effectiveness of these algorithms have focused on the use of a single MR damper. Because multiple dampers would be needed in practical applications, this study aims to evaluate the effects of multiple individually tuned fuzzy-controlled MR dampers in reducing responses of a multi-degree-of-freedom structure subjected to seismic motions. Two different fuzzy-control algorithms are considered, a traditional controller where all parameters are kept constant, and a gain-scheduling control strategy. Different damper placement configurations are also considered, as are different numbers of MR dampers. To determine the robustness of the fuzzy controllers developed to changes in ground excitation, the structure selected is subjected to different earthquake records. Responses analyzed include peak and root mean square displacements, accelerations, and interstory drifts. Results obtained with the fuzzy-based control schemes are compared to passive control strategies.

Linear fractional transformation gain-scheduling flight control preserving robust performance

In this article, a gain-scheduling flight controller design based on a blending/interpolating methodology and an optimal linear fractional transformation (LFT)-based control technique is proposed to achieve robust performances over a wide range of aircraft operating conditions. Representing the non-linear system dynamics into an uncertain LFT system form, our gain-scheduling strategy designs a limited number of robust linear controllers covering the whole range of operating conditions. Using these fixed controllers in a robust performance control set-up, we derive a blending/interpolating scheduling controller, which achieves the desired performance for the entire flight envelope. Our approach offers the benefit of facilitating controller design and provides proofs of robust stability and performance through an uncertain LFT robust performance formulation. In addition, tools analysing the robustness of the closed-loop gain-scheduling system are also provided. Non-linear simulations based on our proposed approach for a B1 flexible aircraft for a limited flight but reasonable flight envelope show very good performance results in both time and frequency domain responses.

Gain-Scheduling-Based State Feedback Integral Control for Networked Control Systems

This paper presents a new controller design method for networked control systems (NCSs), which is motivated by that static state feedback control (SSFC) shows an offset in the plant output when there is nonzero disturbance. Different from the existing SSFC results, the proposed method constructs a gain-scheduling-based state feedback integral controller, where an integral action is introduced to address the nonzero disturbance issue, and most especially, a gain scheduling strategy is employed to improve the control performance of NCSs. Moreover, the obtained results are also extended to the output feedback case. Simulation and experimental results are given to demonstrate the effectiveness of the proposed approach.

Fractional Gain Scheduled Controller for a Networked Smart Wheel: Experimental Results

AbstractThe performance of Networked Control Systems (NCS) is directly affected mainly by time-varying network delays, so a specific approach to compensate their effects is usually required during the controller design process. This paper deals with the control of an experimental platform over the Internet by applying a novel fractional gain scheduling strategy which enables an existing fractional order PI controller for efficient control over the network. The proposed controller introduces delay-adaptive gains, which are optimal with respect to the current network condition. Experimental results show the effectiveness of the proposed controller, providing significantly better system performance, especially when the network-induced delay is increased in certain circumstances.

Gain Scheduled Control of an Air Conditioning System Using the Youla Parameterization

Vapor compression systems form the basis for the majority of air conditioning and refrigeration systems. A primary control challenge addressed here is the coupled nonlinear multiple-input-multiple-output (MIMO) dynamics associated with the multiphase heat and mass transfer in the primary refrigerant loop. This paper develops a MIMO gain scheduled control strategy to regulate system efficiency while meeting changing demands for cooling capacity. An approach based on the Youla parameterization is shown to be a generalization of the more common local controller network method, while exposing several degrees of design freedom that can be exploited to improve stability. The challenge of guaranteeing stability of the nonlinear closed loop systems, despite endogenous and arbitrarily fast gain scheduling, is addressed. Experimental results confirm the effectiveness of the proposed approach.

Supervised Gain-Scheduling Multimodel versus Linear Parameter Varying Internal Model Control of Open-Channel Systems for Large Operating Conditions

In this paper, two internal model control (IMC) controllers using gain-scheduling techniques are proposed and compared for open-channel systems that allow to deal with large operating conditions. In particular, in one side, a linear parameter varying (LPV) model for an open-flow channel system based on a second-order delay Hayami model is proposed. This model will allow one to design a classic gain-scheduling strategy for the IMC controller. On the other side, the LPV model is discretized in a set of linear time invariant (LTI) models corresponding to different operating points. For each LTI model a LTI IMC controller is designed off-line. Then, a supervised gain-scheduler detects on-line which is the LTI model that represents better the open-flow channel system at the current operating point and decides which is the LTI controller that should be used. Finally, both approaches will be applied to a simulated open canal: the Lunax gallery located at Gascogne, France.

An Improved High-Resolution Rotor Position Estimation Using Gain Scheduled Speed Observer in PMSM Drives with Hall-Effect Position Sensors

This paper presents an improved method for high-resolution rotor position estimation in the permanent magnet synchronous motor (PMSM) drives with low-resolution Hall-effect sensors. The proposed method adopts a gain-scheduled full-order speed observer. Since the quantized position signal, which is obtained from Hall-effect sensors, is basically used as the input of the observer, the sixth-order harmonics are essentially included in the estimated position. To eliminate the harmonic components, the quantized position is linearized by a linear extrapolation based on the estimated average speed and futhermore the speed-depentent observer gain scheduling strategy is developed. The observer gain is also scheduled by considering the motor acceleration to improve the dynamic performance according to the changes of the motor speed and load. Several experiments are performed for 800W PMSM drive and the results demonstrate the effectiveness of the proposed method.

Gain-scheduling fuzzy temperature controller for one-way input system

In many chemical and semiconductor manufacturing processes, temperature is an important control parameter for obtaining the desired product quality. Generally, the temperature control system has non-linear time-varying, slow response, time-delay and one-way control input characteristics. It is difficult to estimate accurately the dynamic model and design a general-purpose temperature controller to achieve good control performance. Here a model-free intelligent gain-scheduling fuzzy control strategy is proposed to design a temperature controller for an iron closed chamber with heater input only. The concept of gain scheduling is employed to adjust the mapping ranges of fuzzy membership functions during the control process for improving the control performance. The experimental results show that the steady-state errors of the step input responses are always less than 0.2°C without overshoot by using this control scheme. It is suitable for industrial temperature control systems.

Multivariable control strategy based on bifurcation analysis of an industrial gas-phase polymerization reactor

In an industrial gas-phase polyethylene reactor, the safe operating range of temperature is rather narrow. Even within this temperature range, temperature excursions must be avoided because they can result in low catalyst productivity and significant changes in product properties. If the manipulated variable for temperature control saturates (i.e., the cooling water valve position is completely open), then the reactor operates without a feedback temperature controller, leading to oscillatory behavior and limit cycles. In this work, it has been demonstrated that the saturation in the manipulated variable and the complex non-linear dynamic behavior are removed when auxiliary manipulated variables, obtained by bifurcation analysis, are used in a multivariable control strategy for the reactor temperature control. Two control structures are proposed and compared considering their impact in the reactor production and polymer melt index. In the first control structure, the designed PID controller for the reactor temperature is considered and a switching strategy with a PI controller for the auxiliary manipulated variables is included. In the second control structure, the designed PID controller for the reactor temperature is also used, however, a MPC controller for the auxiliary manipulated variables is considered. The results suggest that the use of gain-scheduling strategy in the PID temperature controller with a MPC controller for the auxiliary manipulated variables avoids the saturation of the manipulated variable and, hence, the undesired non-linear dynamic behavior, reducing the production loss and improving the product quality.

The Heterogeneous Systems Integration Design and Implementation for Lane Keeping on a Vehicle

In this paper, an intelligent automated lane-keeping system is proposed and implemented on our vehicle platform, i.e., TAIWAN i TS-1. This system challenges the online integrating heterogeneous systems such as a real-time vision system, a lateral controller, in-vehicle sensors, and a steering wheel actuating motor. The implemented vision system detects the lane markings ahead of the vehicle, regardless of the varieties in road appearance, and determines the desired trajectory based on the relative positions of the vehicle with respect to the center of the road. To achieve more humanlike driving behavior such as smooth turning, particularly at high levels of speed, a fuzzy gain scheduling (FGS) strategy is introduced to compensate for the feedback controller for appropriately adapting to the SW command. Instead of manual tuning by trial and error, the methodology of FGS is designed to ensure that the closed-loop system can satisfy the crossover model principle. The proposed integrated system is examined on the standard testing road at the Automotive Research and Testing Center (ARTC) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and extra-urban highways.

Optimal gain scheduling control strategy for parallel HEV based on traffic conditions

This paper presents gain scheduling of control strategy for parallel hybrid electric vehicles, based on traffic conditions. Electric assist control strategy (EACS) is employed, with different parameters for different traffic conditions. The parameters of the EACS are optimised and scheduled for different traffic conditions of the TEH-CAR driving cycle. TEH-CAR is a driving cycle which is developed, based on the experimental data collected from the real traffic conditions in the city of Tehran. The objective of the optimisation is to minimise the fuel consumption and emissions over the driving cycle, while enhancing or maintaining the driving performance characteristics of the vehicle. A genetic algorithm (GA) is used to solve the optimisation problem, and the constraints are handled by using penalty functions. The results from the computer simulation show the effectiveness of the approach and reduction in fuel consumption and emissions, while ensuring that the vehicle performance is not sacrificed.

Implementation Issues and Performance Evaluation of Sinusoidal, Surface-Mounted PM Machine Drives With Hall-Effect Position Sensors and a Vector-Tracking Observer

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents the implementation and evaluation of a high-resolution position estimation system for sinusoidal, surface phase modulation machines based on Hall-effect sensors and a vector-tracking observer. First, the tuning of the observer is presented and a speed-dependent gain scheduling strategy is proposed. Then various harmonic decoupling strategies are investigated to improve the performance of the observer, particularly at low speeds. Stability analysis is performed leading to the definition of local stability limits, within which the actual position is tracked with bounded estimation error. Both simulation and experimental testing illustrate the performance and limitations of the proposed observer topology and of the drive when this observer is used for state feedback. </para>

Robust Pressure Control in a Mobile Robot for Spraying Tasks

This article presents a robust control system aimed at regulating the output pressure of a spraying system mounted on a mobile robot for fumigation tasks despite changes in the vehicle velocity; that is, the pressure setpoints are calculated based on the actual velocity of the vehicle at each sampling time and on a predefined volume of pesticides to apply based on the crop growth state, and are then used in a feedback loop for pressure control purposes, where the controller accounts for uncertainty in the system. This article focuses on the dynamic modeling of the spraying system based on the reaction curve method (analysis of the response to open loop steps) and the development and test of a robust control strategy to achieve the desired pressure profile. Quantitative feedback theory has been used as a robust control technique to cope with system uncertainties and to improve the gain-scheduling strategy used in previous works. Illustrative results are shown and commented.

Fuzzy gain scheduling for parallel parking a car-like robot

This brief proposes a fuzzy gain scheduling strategy with an application on parallel parking car-like robots. First, the fuzzy gain scheduling strategy is introduced as a combination of a local path tracking controller and fuzzy rule based techniques. In light of human driver experience in parallel parking, the control goal is achieved by repeatedly scheduling parameters and tracking local paths. Meanwhile, a time-varying fuzzy sliding mode controller (TFSC) is developed as the local tracking controller to guarantee robust performance and fast tracking response for a segment of preplanned reference path. Different to traditional gain scheduling, the overall controller combining the TFSC and a fuzzy gain scheduler has advantages in regards of 1) a small data base; 2) an enlarged workspace of interest; and 3) allowing zero velocity crossing. Then, the scenario of parallel parking car-like robots is implemented in presence of nonholonomic and input saturation constraints. Finally, numerical simulation and practical experiment are carried out to show the expected performances.

A generic model-based warning system for ground-level ozone monitoring

The paper describes a model based to develop a highly reliable operational public warning system for air pollution monitoring in Bordeaux, France. The developed methodology is based on hard and soft computing techniques. The proposed method combines new adaptive nonlinear state-space modelling techniques, a gain scheduling strategy and multi-layer perceptron neural networks. The warning system provides additional information regarding the duration of a smog episode that is very important for assessments of human health hazards and negative environmental effects. Experimental results are presented for ground-level ozone concentrations in Bordeaux metropolitan.

Gain scheduling control of functional electrical stimulation for assisted standing up and sitting down in paraplegia: a simulation study

AbstractThis paper reports on a simulation study that concerns the design of a non‐linear controller for the standing up and the sitting down of a paraplegic patient by means of functional electrical stimulation. The simulations refer to a specific experimental device developed at the Fondazione Don Gnocchi (Italy). This is a seesaw, with the patient on one side and a weight on the other side. The patient is seated so that its posture can be fully known in real‐time by continuously monitoring the knee joint angle. By delivering a suitable electrical stimulation to the quadriceps muscles groups, the patient can be raised and made to sit via smooth movements. Hitherto, the only feedback control law, which has been implemented in this area, is based on a PID controller and usually provides poor tracking performances. Hence, in this work, a non‐linear gain scheduling controller has been designed and tested in a series of simulation experiments. The controller is tuned following a gain scheduling strategy: a set of local linear quadratic controllers is designed using a set of linear tangent models. A global non‐linear gain scheduled controller is then obtained via interpolation. The gain‐ scheduled controller is implemented following an advanced strategy that guarantees that the so‐called linearization property holds. Copyright © 2004 John Wiley &amp; Sons, Ltd.

Extended Influence Coefficient Method for Rotor Active Balancing During Acceleration

Imbalance-induced vibration of rotating machineries is an important factor limiting the performance and fatigue life of a rotor system. Particularly, the severe resonant vibration of a rotor when it passes through its critical speeds could damage the rotor system. To avoid this peak vibration, this paper presents an active balancing method to offset the imbalance of the rotor system during acceleration by using an electromagnetic balancer. In this method, “instantaneous” influence coefficients at different speeds are obtained and stored in a look-up table. Then, a gain scheduling strategy is adopted to suppress the imbalance-induced vibration during acceleration based on the “instantaneous” influence coefficient table. A comprehensive testbed is built to validate this scheme, and the validation results are presented.

Relaminarization of Reτ=100 turbulence using gain scheduling and linear state-feedback control

The first successful application of linear full-state feedback optimal control theory to consistently relaminarize turbulent channel flow at Reτ=100 with full state information and gain scheduling is reported. The actuation is zero-net mass-flux blowing and suction on the channel walls. Two key issues central to the success of this strategy are: (a) the choice of the mean-flow profile about which the equations are linearized for the computation of the linear feedback gains, and (b) the choice of an objective function which targets the control effort on the flow perturbations of interest. A range of mean-flow profiles between the laminar and fully turbulent profiles and a weighted energy measure which targets flow perturbations in the near-wall region were found to provide effective feedback gains. A gain-scheduling strategy to tune the feedback gains to the nonstationary mean-flow profile is introduced, resulting in consistent relaminarization of the turbulent flow in all realizations tested.