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In this paper, the design problem of Gain‐Scheduled Output‐Feedback (GSOF) controllers using inexact scheduling parameters for morphing aircraft during the wing transition process is addressed. Both the stability of the closed‐loop system and the L2 gain performance can be guaranteed under the controller based on measured (not actual) scheduling parameters. Firstly, the linear parameter‐varying (LPV) model of morphing aircraft is established by Jacobian linearization and the additive uncertainty is introduced into the scheduling parameters. By employing non‐linear transformations, the problem is formulated as the solution to a set of parameter‐dependent linear matrix inequalities (LMI) with a single‐line search parameter. Finally, the robustness of the flight control system to the wing transition process is verified under the condition of both the uncertainty of aerodynamic parameters and of scheduling parameters.

The time-varying and rigid-flexible coupling dynamic behaviors of ball screw feed drives (BSFD) are the main reasons that affect their tracking and positioning accuracy. The traditional PID control strategy cannot overcome the impacts resulted from these factors. A linear parameter varying (LPV) model based gain scheduling (GS) control method is proposed. Considering the time-varying and rigid-flexible coupling dynamic characteristics of a BSFD, its LPV model is established through identification experiments of the rigid-body transfer function, Stribeck friction, and elastic-body transfer function. Based on the LPV model, an output feedback GS control strategy is proposed, and a tuning method of the controller parameters is summarized. The comparison experiments between the GS and PID control strategies prove that the GS control strategy can ensure the consistency of tracking and positioning accuracy on the entire feed stroke of the BSFD. This work is of great significance for improving the machining accuracy reliability and accuracy retention of CNC machine tools.

COFLEX (COntrol scheme for FLEXible wind turbines) has recently been introduced to account for structural-flexibility-induced performance variations in modern wind turbines. It does so by leveraging the separation of tip-speed ratio into rotational speed and wind speed in turbine performance representation and by utilizing a set-point optimization strategy. The original work of COFLEX focuses on generating the nominal operation set-point, taking structural loads into account. However, its potential to account for derating cases useful for grid active power control and induction control, for instance, has not yet been exploited. This paper extends the capabilities of COFLEX by incorporating two derating scenarios representing two extremes, namely the max-Ω and min-Ct, in the optimal set-point generation, including the generation of intermediate strategies. Additionally, due to the differences in aerodynamic sensitivities, dynamic controller gain calibration laws are established in this work, and controller gain scheduling is employed accordingly. This ensures consistent torque and collective pitch controller performance throughout the time-series validation of the generated derating setpoints. Compared with the original COFLEX formulation, the present extension explicitly enables grid-interactive and multi-objective derating operation by combining strategy selection, weighted trade-off optimization, and scheduled feedback gain calibration in one framework. The controller gain scheduling is performed using the mid-fidelity simulator HAWC2 with the IEA-15MW reference wind turbine.

The use of quadrotor helicopters is highly demanded in both military and commercial fields. Depending on the application area, special requirements may be needed for the maneuverability of these unmanned aerial vehicles. Traditional controller structures cannot meet these demands, leading developers to seek innovative solutions. In this study, a gain-scheduling controller is proposed to maintain the performance requirements for the attitude control of quadrotor helicopters. In this context, different operating points are selected based on a proposed rule in the working region of the quadrotor helicopter, and a controller design is carried out after a necessary transformation of the linear models. Furthermore, the robustness of quadrotors against time-varying disturbances is ensured through the implementation of a supervisor neural network (NN) controller. Specifically, a radial basis function NN (RBFNN) is employed, trained using gradient descent-based supervised learning. The response of the proposed controller is compared numerically with the traditional proportional-derivative controller’s response, where the proposed controller, which is not so different from conventional controllers in terms of processor power, provides the desired performance criteria in a wide operating region. Finally, the gain-scheduling controller improved with the feedforward NN controller is tested under time-varying disturbances, resulting in a significant reduction in errors.

A multi-parameter continuous gain scheduling sliding mode control method based on load observation (LO-AGSMC) is proposed to address the problem of traditional sliding mode controllers being difficult to balance the no-load stability and loading robustness of permanent magnet synchronous motors due to fixed parameters. This method estimates the load in real time through speed tracking error and its derivative, and generates continuous scheduling variables based on it. Then, multiple key parameters such as sliding surface coefficient, switching gain, and boundary layer thickness are adaptively adjusted in coordination, forming a continuously changing full condition control characteristic. Simulation results show that compared to traditional SMC control, the proposed method can effectively eliminate no-load chattering; Compared with traditional PI control, it greatly reduces the recovery time during sudden loading and unloading, providing a better performance solution for variable condition motor drive.

Gain scheduling control of hypersonic vehicles (HVs) in full envelope is studied utilizing switching polytopic linear parameter-varying (LPV) method. Envelope division and a new convex decomposition strategy with optimal gap metric are used to establish the switching polytopic LPV system of HVs. Sufficient conditions for stability assessment and controller synthesis for the switching polytopic LPV system are presented by average dwell time (ADT) and multiple parameter-dependent Lyapunov functions (MPDLF). To reduce computation burden and simplify the realization of controller, limited number of linear matrix inequalities (LMIs) are derived to dealing with switching polytopic LPV controllers by constructing the intermediate controller variables are depend affinely on the scheduling parameter. Furthermore, to make the proposed method have better engineering value, the control matrix of LPV system studied in this paper may not have to be a constant matrix. Simulation results show the effectiveness of the switching polytopic LPV control method for HVs.

Thermal regulation using Peltier cells presents challenges due to high inertia, memory effects, and energy constraints in embedded systems. This paper introduces the FOPID with Adaptive Supervisor (FOPID-AS) scheme, combining a PSO-optimized fractional-order controller (FOPID) with a deterministic rule-based gain-scheduling supervisor. Experimental validation compares four strategies: PID, Fuzzy-PID, static FOPID, and the proposed FOPID-AS. During the transient phase (t<105 s), FOPID-AS reaches the ±0.5 °C tolerance band in 31.20 s, with an ITAE of 6612.97 and transient energy consumption of 0.18 Wh, outperforming PID, Fuzzy-PID, and FOPID in speed and tracking quality. In steady state (t≥105s), FOPID-AS exhibits steady-state error ess = 0.08 °C, σss = 0.10 °C, and peak-to-peak ripple of 0.67 °C, with steady-state energy consumption of 0.30 Wh, showing lower dispersion than PID and comparable values to the other fractional controllers, while maintaining low computational load suitable for real-time applications.

In this paper, a hybrid stepper motor (HSM) model, open-loop control and closed-loop control are explained. To overcome the stepping nature of the HSM open-loop configuration, the closed-loop control is an essential task and is obtained with the help of the HSM mathematical model. Effective closed-loop control strategies are performed based on the field oriented control (FOC) after performing a Park transform. Those strategies are optimized PI controller and optimized gain scheduling PI controller in which the controller’s parameters are tuned optimally using a particle swarm optimization algorithm (PSO) with the integral absolute error (IAE), integral time absolute error (ITAE), and integral time square error (ITSE) performance indexes mean value as an objective function. Additionally, operation under no-load and loaded conditions are tested and compared with the traditional PI controller. The obtained results show that, under loaded conditions, the optimized gain scheduling PI controller gives better performance due to its adaptive nature.

In this paper, a general type-2 fuzzy gain scheduling PID (GT2FGS-PID) controller is presented to achieve self-balance adjustment of the power-line inspection (PLI) robot system. As the PLI robot system is an under-actuated nonlinear system, obtaining the full information of the four-state variables is necessary to balance the PLI robot. However, as the number of input variables increases, the number of control rules increases exponentially, making the design of the fuzzy controller extremely complex. Therefore, the proposed controller prevents the problem of rule explosion using information fusion and then simplifies the control design. Moreover, the particle swarm optimization algorithm is used to select improved controller parameters and make the controller achievable. In this paper, the control performance and anti-interference ability of the traditional PID control, type-1 fuzzy control, interval type-2 fuzzy control, and general type-2 fuzzy control methods are compared. By means of numerical simulation, we can conclude that the GT2FGS-PID controller exhibits superior stability and robustness over other controllers for the PLI robot system.

This article is devoted to the design of a real-time gain scheduling (adaptive) proportional–integral (PI) controller for the displacement volume regulation of a swash plate-type axial piston pump. The pump is intended for open circuit hydraulic drive applications without “secondary control”. In this type of pump, the displacement volume depends on the swash plate swivel angle. The swash plate is actuated by a hydraulic-driven mechanism. The classical control device is a hydro-mechanical type, which can realize different control laws (by pressure, flow rate, or power). In the present development, it is replaced by an electro-hydraulic proportional spool valve, which controls the swash plate-actuating mechanism. The designed digital gain scheduling controller evaluates control signal values applied to the proportional valve. The digital controller is based on the new linear parameter-varying mathematical model. This model is estimated and validated from experimental data for various loading modes by an identification procedure. The controller is implemented by a rapid prototyping system, and various real-time loading experiments are performed. The obtained results with the gain scheduling PI controller are compared with those obtained by other classical PI controllers. The developed control system achieves appropriate control performance for a wide working mode of the axial piston pump. The comparison analyses of the experimental results showed the advantages of the adaptive PI controller and confirmed the possibility for its implementation in a real-time control system of different types of variable displacement pumps.