Gain Scheduling Research Articles

Variable impedance control for synchronizer position tracking based on gain scheduling

Achieving fast and stable synchronizer position tracking control can greatly improve the power and comfort of transmission shifting. In order to further improve the shift speed and stability of the synchronizer, this article takes the synchronizer control of a three-speed seamless transmission as the research object and proposes a variable impedance control method based on gain scheduling. Firstly, dynamic models of the shift actuator and synchronizer was established. Then, an impedance outer loop controller and a position inner loop controller were designed. In the impedance outer loop, adjust the synchronizer reference position based on the difference between the reference shift force and the actual shift force. At the same time, based on the actual control requirements of different motion stages of the synchronizer, the optimal impedance control parameters for each stage are selected for gain scheduling control. In the position inner loop, a prescribed performance-active disturbance rejection control is adopted, which solves the problem of system disturbance while ensuring the instantaneous performance of position tracking. Finally, simulations and bench tests were conducted on the position tracking control of synchronizer. The simulation and bench test results show that the control strategy proposed achieves fast and stable synchronizer position tracking, with superior control performance, which preliminarily verifies the effectiveness of the control strategy.

Modeling and control of heating and heat circulation in direct air capture system

Direct air capture (DAC) is a critical technology for mitigating climate change. However, the high heat consumption of temperature vacuum swing adsorption (TVSA)-based DAC processes hinders its widespread deployment. This study focuses on developing a control strategy to optimize the energy efficiency of the TVSA heating phase. A novel adsorbent temperature estimation method, validated through experimental data, was integrated into a cascaded PI controller with a fuzzy gain scheduler (FGS). Experimental results demonstrate that the proposed control strategy effectively regulates the heating process, achieving a potential energy saving of up to 14%. This work contributes to enhancing the feasibility and sustainability of DAC technologies.

Large-Scale Solar-Powered UAV Attitude Control Using Deep Reinforcement Learning in Hardware-in-Loop Verification

Large-scale solar-powered unmanned aerial vehicles possess the capacity to perform long-term missions at different altitudes from near-ground to near-space, and the huge spatial span brings strict disciplines for its attitude control such as aerodynamic nonlinearity and environmental disturbances. The design efficiency and control performance are limited by the gain scheduling of linear methods in a way, which are widely used on such aircraft at present. So far, deep reinforcement learning has been demonstrated to be a promising approach for training attitude controllers for small unmanned aircraft. In this work, a low-level attitude control method based on deep reinforcement learning is proposed for solar-powered unmanned aerial vehicles, which is able to interact with high-fidelity nonlinear systems to discover optimal control laws and can receive and track the target attitude input with an arbitrary high-level control module. Considering the risks of field flight experiments, a hardware-in-loop simulation platform is established that connects the on-board avionics stack with the neural network controller trained in a digital environment. Through flight missions under different altitudes and parameter perturbation, the results show that the controller without re-training has comparable performance with the traditional PID controller, even despite physical delays and mechanical backlash.

Neural operators for robust output regulation of hyperbolic PDEs

The recently introduced neural operator (NO) has been employed as a gain approximator in the backstepping stabilization control of first-order hyperbolic and parabolic partial differential equation (PDE) systems. Due to the global approximation ability of the DeepONet, the NO provides approximate spatial gain function with arbitrary accuracy. The closed-loop system stability can be ensured by the backstepping controller involving the approximate gain with sufficiently small error. In this paper, the NO theory is leveraged to solve the robust output regulation problem for a class of uncertain hyperbolic PDE systems under the design framework of backstepping-based regulator. The NO is trained offline on a dataset containing a sufficient number of system parameters and corresponding prior solutions of the kernel equation, so as to generate feedback gain for the robust regulator. Once the NO is trained, the kernel equation does not need to be solved ever again, for any new system parameters that do not exceed the range of the training set. Based on the internal model principle, the regulator is inherently robust to a degree of parameter uncertainty and error in approximate gain. Therefore, the tracking error can still converge to 0 if the extended regulator equations are solvable and the parameter uncertainty leads to an asymptotically stable origin. We provide a series of theory proofs and a numerical test under the approximate control and observation gains to demonstrate the robust regulation problem. It is shown that the NO is almost three orders of magnitude faster than PDE solver in generating kernel function, and the loss remains on the order of 10−4 in the test. This provides an opportunity to use the NO methodology for accelerated gain scheduling regulation for PDEs with time-varying system parameters.

Comparison between an Adaptive Gain Scheduling Control Strategy and a Fuzzy Multimodel Intelligent Control Applied to the Speed Control of Non-Holonomic Robots

The main objective of this work is to address problems related to the speed control of mobile robots with non-holonomic constraints and differential traction—specifically, robots for football games in the VSS (Very Small Size) category. To achieve this objective, an implementation and comparison is carried out between two control strategies: an adaptive control strategy by gain scheduling and a fuzzy multimodel intelligent control strategy. The mathematical models of the wheel motors for each operating range are approximated by a first-order system since data acquisition is performed using the step response. Tuning of the proportional and integral gains of the local controllers is carried out using the root locus technique in discrete time. For each mathematical model obtained for an operating range, a local controller is tuned. Finally, with the local controllers in hand, the implementation of and comparison between the gain scheduling adaptive control strategy and the fuzzy multimodel intelligent control strategy are carried out, in which the control strategies are programmed into the low-level code of a non-holonomic robot with a differential drive to verify the performance of the speed tracking dynamics imposed on the wheel motors to improve robot navigation during a robot football match.

Dynamic PI-PD cascaded MPPT controller for SPV system with battery charging circuit

This paper proposes a dynamic maximum power point tracking (MPPT) controller for a solar photovoltaic (SPV) system with a battery charging circuit. The voltage and current, and consequently the maximum available power of SPV panels vary based on environmental conditions. To operate SPV system at maximum power point under different weather conditions, a cascaded (PI-PD) controller with PSO gain scheduling is suggested in this paper. Also, the FOPI control is applied to an accurate dynamic model of the buck converter to function as a charge controller. For tuning the FOPI controller parameters, a stochastic inertia weight GWO algorithm is employed which maintains an appropriate balance between detection and hunting strategies, and gives the fittest wolf position during iterations. The proposed algorithm is compared with the original GWO algorithm to show its superiority. The accuracy of the proposed cascaded controller used in the SPV system to find MPP ranges from 96.05% to 98.87%. The goal of this study is to operate the SPV panel at maximum power point under variable atmospheric conditions to increase efficiency at a lower cost. It also provides appropriate current and voltage for faster battery charging, thereby increasing the life span of the battery. The system is implemented and analyzed in MATLAB/Simulink, and results are validated.

Enhancing Stability in Autonomous Control Systems Through Fuzzy Gain Scheduling (FGS) and Lyapunov Function Analysis

Enhancing Stability in Autonomous Control Systems Through Fuzzy Gain Scheduling (FGS) and Lyapunov Function Analysis

Wavelet LQR based gain scheduling for power regulation and vibration control of floating horizontal axis wind turbine with double-pitched rotor

This paper introduces a wavelet-based linear quadratic regulator (LQR) control strategy for double-pitched rotor systems of an offshore horizontal axis wind turbines. A comprehensive aero-servo-elastic model incorporating bending-torsion coupling is developed to assess the performance of the proposed controller under combined turbulent wind and wave loading. The weight matrices of the classical LQR algorithm are optimized using a nonlinear optimization scheme. Additionally, frequency-dependent gain scheduling is proposed using wavelet transform where the analytic Morse wavelet serves as the basis function. The resulting LQR algorithm operates in the time-frequency domain and solves scale-dependent Algebraic Riccati equations to determine optimal gains. Numerical simulations demonstrate the superior performance of the proposed gain scheduling compared to the classical LQR approach. The effectiveness of the algorithm is evaluated across various flow conditions and wind-wave misalignment, with benchmark results used for performance comparison.

Trajectory Tracking Control of Fixed-Wing Hybrid Aerial Underwater Vehicle Subject to Wind and Wave Disturbances

The hybrid aerial underwater vehicle (HAUV) could operate in the air and underwater might provide a great convenience for aerial and underwater exploration, and the fixed-wing HAUV (FHAUV) has time, space and cost advantages in future large-scale applications, while the large difference between the aerial and underwater environments is a challenge to control, especially in the air/water transition. However, for FHAUV control, there is a lack of research on phenomena or problems caused by large changes in the air/water transition. In addition, the effects of wind, wave, other factors and conditions on motion control are not investigated. This paper presents the first control study on the above issues. The motion model of FHAUV is developed, with the effects of wind and wave disturbances. Then, this paper improves a cascade gain scheduling (CGS) PID for different media environments (air and water) and proposes a cascade state feedback (CSF) control strategy to address the convergence problem of FHAUV control caused by large speed change in the air/water transition. In the comparisons of the two control schemes in various tracking cases including trajectory slopes, reference speeds, wind and wave disturbances, CSF has a better control effect, convergence rate and robustness; the key factors and conditions of the air/water transition are investigated, the critical relations and feasible domains of the trajectory slopes and reference speeds that the FHAUV must meet to successfully exit the water and enter the air are obtained, the critical slope decreases as the reference speed increases, and the feasible domain of CSF is larger than that of CGS, revealing that CSF is superior than CGS for exiting the water.

Modified active disturbance rejection control based on gain scheduling for circulating fluidized bed units

Circulating fluidized bed (CFB) units are extensively operated in China due to their wide fuel adaptability, low emissions and high combustion efficiency. Nevertheless, CFB units are facing many control challenges due to their strong nonlinearity and large lag characteristics. To handle these control challenges, a modified active disturbance rejection control based on gain scheduling is structured in this paper. Firstly, a decentralized active disturbance rejection control strategy based on gain scheduling is designed by analyzing control difficulties of CFB units. Then, the parameter tuning and scheduling methods are provided, and the convergence of the extended state observer during the scheduling process is derived theoretically. The advantages of the proposed method in tracking performance, disturbance rejection performance, and ability to reject fuel quality fluctuation and uncertain heat transfer coefficients are illustrated by comparative simulations. Finally, the proposed control strategy is practically applied to the main steam pressure system of a 300 MW CFB unit. Running data demonstrate that it has a faster tracking performance and better disturbance rejection ability in [50∼100]% of the rated load, where the hourly average integral absolute error index has decreased obviously, and it shows a good field application prospect.

Semi-decentralized temperature control in district heating systems

The supply temperature in district heating systems has traditionally been controlled using feedforward – a robust and well-validated approach for district heating networks with few producers and relatively high supply temperatures. The transition towards lower temperature district heating networks allows for efficient reuse of excess heat from, e.g., industrial processes and data centers. Excess heat is often intermittent, cannot always be assumed to be possible to control with a centralized controller, and can cause temperature disturbances in the grid. Closing the loop using PID control is challenging due to the process’s time-varying nature and long time delays. Model Predictive Control (MPC) suffers from a higher complexity, long computational times, and the need for a well-validated and maintained centralized model. The paper suggests a semi-decentralized approach using the Smith predictor with an event-driven assignment of active controllers and sensors. A reduced order model based on a more comprehensive state space model is derived and used for gain scheduling and input–output pairing using the normalized relative gain array. The focus is on temperature disturbance rejection, and appropriate tuning rules and controller structures are suggested. Simulation results show that the proposed control structure can handle various types of temperature disturbances, even in the presence of model estimation errors.

Control system optimisation of biodiesel-based gas turbine for ship propulsion

<p>Reducing a gas emission of shipping transportations become a main goal of international maritime organization to achieve a clean energy. One of best scenarios to achieve this goal is to shift a fossil fuel to a renewable energy-based fuel of a ship propulsion. This paper studies an optimization of a control system of the renewable-based small gas turbine engine for the ship propulsion. Proposed control system consists of a proportional-integral with engine performance limiters to avoid an engine damage. Proportional-integral gains are tuned by a whale optimization algorithm. A gain scheduling analysis of a step response is performed to obtain a searching area of tuning parameters and values of constant gains. In this step, the gains are modeled as function of plant variables. After the searching area is obtained, the proportional-integral gains are optimized using the whale optimization algorithm while the additional gains are set as constant values. Using this scenario, stable and optimal gains have been successfully achieved. Results show that the proposed method has better performance than that of the previous methods, i.e. gain scheduling and gain scheduling optimized by the whale optimization algorithm. The proposed method has lowest fitness value and does not have an overshoot problem.</p>

Fuzzy Gain Scheduling PI Controller Based PSS for SMIBS

Fuzzy Gain Scheduling PI Controller Based PSS for SMIBS

A control-oriented LPV model of large-scale offshore wind turbines for deloading operation

ABSTRACT This article develops a new control-oriented linear parameter varying (LPV) model, which is a linear state-space model with varying parameters aimed at efficiently representing large-scale offshore wind turbines (LOWTs). Firstly, the LPV model is constructed by the mechanism analysis of a 15-MW direct-drive permanent magnet wind turbine. Subseuently, based on the operational data from FAST, the parameters with unknown values in the LPV model are determined by gain scheduling and parameter estimation. Furthermore, a deloading operation for the active output power regulation of LOWTs is proposed based on coordinating the rotor over-speed control (ROC) and the pitch angle control (PAC).

Hierarchical gain scheduling based tilt angle guided robust control during mode transition for tilt-rotor unmanned aircraft vehicle

Tilt-rotor unmanned aircraft vehicle has the potential to combine vertical take-off and landing capability with efficient, high-speed cruise flight. However, the mode transition process is risky owing to the internal continuous time change of aerodynamic and external uncertain wind disturbances. Gain scheduling between two modes is commonly used to achieve mode transition control. However, the optimal scheduling parameters to allocate the manipulated variables for the stable transition have not been determined to data. Focusing on this problem, a gain scheduling-based tilt angle guided robust control method for mode transition is proposed. The dynamic model is first built and analyzed based on a newly developed 360-kg tilt-rotor unmanned aircraft vehicle. Based on this model, the transition guided curve is mapped with respect to velocity and tilt angle, and it is introduced to the gain scheduling method to optimally achieve the allocation of the manipulated variables. Finally, the feasibility and validity are verified in a simulation experiment. Furthermore, the robustness is verified in simulated wind disturbance.

Fuzzy Modeling Framework Using Sector Non-Linearity Techniques for Fixed-Wing Aircrafts

This paper presents a mathematical modeling approach utilizing a fuzzy modeling framework for fixed-wing aircraft systems with the goal of creating a highly desirable mathematical representation for model-based control design applications. The starting point is a mathematical model comprising fifteen non-linear ordinary differential equations representing the dynamic and kinematic behavior applicable to a wide range of fixed-wing aircraft systems. Here, the proposed mathematical modeling framework is applied to the AIRBUS A310 model developed by ONERA. The proposed fuzzy modeling framework takes advantage of sector non-linearity red techniques to recast all the non-linear terms from the original model to a set of combined fuzzy rules. The result of this fuzzification is a more suitable mathematical description from the control system design point of view. Therefore, the combination of this fuzzy model and the wide range of control techniques available in the literature for such kind of models, like parallel and non-parallel distributed compensation control laws using linear matrix inequality optimization, enables the development of control algorithms that guarantee stability conditions for a wide range of operations points, avoiding the classical gain scheduling schemes, where the stability issues can be extremely challenging.

Force tracking control for hydraulically actuated adaptive high-rise buildings

Adaptive high-rise buildings comprise sensors, a control unit and actuators and are able to actively compensate disturbances, allowing the realization of significantly lighter structures compared to conventional constructions. This publication presents the development of a force tracking controller for hydraulic actuators on adaptive high-rise buildings. First, nonlinear models of a single- and a double-acting hydraulic cylinder, used for two different ways of actuation in the structure, are derived. The characteristic curve for the valve cross-section is determined through experimental system identification. Second, a flatness-based input/output linearization is applied to both actuator models. A gain scheduling algorithm sets the proportional gain of the force tracking controller based on a measure reflecting the dynamics of the reference signal and thus avoiding control spillover due to mechanical coupling between the actuator and the structure. The force tracking controller is tested experimentally on the worldwide first adaptive building and its tracking behavior is analyzed based on the cutoff frequency of the closed loop system.

Research on Path Tracking Fault-Tolerant Control Strategy for Intelligent Commercial Vehicles Based on Brake Actuator Failure

With the development of safety technologies for intelligent commercial vehicles, electronic pneumatic braking systems (EBSs) have been widely used. However, EBS actuators may fail during vehicle operation and thus create safety problems. For this reason, we propose a path-tracking fault-tolerant control strategy under EBS actuator failure in intelligent commercial vehicles. First, in order to be able to characterize different types of brake actuator faults during the EBS differential braking process of a vehicle, a comprehensive fault coefficient for calculating the degree of fault is designed, and a BES generalized fault model is established. Second, the faults are introduced into the fault-tolerant controller through the comprehensive fault coefficients for braking torque calculation and braking pressure allocation. Thus, a vehicle path model with the complete fault coefficients as variable parameters is established. Then, based on the LPV system gain scheduling, a path-tracking LPV/H∞ fault-tolerant controller under EBS actuator faults in commercial vehicles is designed, which is used to solve the safety problem arising from sudden EBS actuator faults. Finally, we conducted experimental validation through hardware-in-the-loop tests. The results demonstrate that the control strategy designed in this paper redistributes the braking torque and synergizes with the steering system to enhance vehicle stability, thereby improving vehicle safety in the EBS failure mode.

A GRU network framework towards fault-tolerant control for flight vehicles based on a gain-scheduled approach

This paper introduces a novel approach to enhance the fault-tolerant control of flight vehicles by incorporating a gated recurrent unit (GRU) neural network within a gain-scheduled framework. Gain scheduling, a well-established technique for achieving active fault-tolerant control, enables the selection of control gains from a predefined set based on specific faults. An improved terminal sliding mode controller is derived, and appropriate parameters are chosen to formulate a gain-scheduled fault-tolerant controller. Based on it, training data is generated by comprehensive simulations on a Winged-Cone configuration flight vehicle. The GRU neural network architecture is designed and trained to function as the flight controller. The effectiveness of the proposed GRU network controller is demonstrated through a series of simulations.

High Bandwidth Current Regulator Design for PMSM Based on Frequency Sweep Correction

How to automatically correct controller para- metric mismatch and improve motor current closed-loop bandwidth in gain scheduling is an important issue for general motor drives. This paper firstly introduces the frequency domain tuning method of current controller. Then, the time delay identification and model parameters self-correction method based on frequency sweep is introduced to avoid the influence caused by parameter mismatch between plant and controller model. On this basis, phase lead module combined with active resistance term, is designed as a prefilter for current regulator, so as to achieve faster current dynamic response and improve disturbance rejection. Finally, the parameter setting for proposed current regulator is introduced to better practice. Experiments for different permanent magnet synchronous machines are carried out on a miniature drive system, the results fully verify the effectiveness of proposed scheme.