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Copyright of Ingenius, Revista Ciencia y Tecnología is the property of Universidad Politecnica Salesiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work addresses issues that appear from wind turbines equipped with frequency converters and presents a new fixed-speed wind turbine (FSWT) concept based on a split power transmission, along with a Mechatronic Control Model. The wind turbine rotor drives the first transmission shaft, while a servomotor with variable speed powers the second input. The differential gear transmission output is linked to the electric grid through an asynchronous generator. To optimize the power extracted from the wind energy while minimizing excessive dynamic loads on the wind turbine, a mechatronic control model of a 750 kW-FSWT is applied using a proportional and integral controller (PI). The paper suggests employing neural networks and evolutionary algorithms for determining appropriate PI gains. For collective servomotor control of a 750 kW-FSWT, a radial basis function (RBF) neural networks based on PI controller is proposed. To acquire an optimum dataset for RBF training, the particle swarm optimization (PSO) evolutionary algorithm is harnessed. The robustness and effectiveness of the proposed model and controller are verified and confirmed through simulation results. Hands-on experience is conducted using the 20-sim software package. [ABSTRACT FROM AUTHOR]

Linearization remodeling and state-feedback control for a class of autonomous nonlinear systems based on equilibrium manifold expansion (EME) are visited and explicated in this paper, including linearization approximation, state-feedback stabilization and state estimation. More precisely, firstly, EME linearized remodels of nonlinear systems are explained and their existence is validated rigorously; secondly, EME-based state-feedback control and observer design are developed analytically with EME remodeling and gain scheduling; thirdly, stabilization under EME-based state feedback and observers are tackled, respectively; finally, feasibility and efficiency of the EME approach are illustrated by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper presents an active vibration control methodology to suppress the vibration of a square laminated composite plate. The plate undergoes large deflection, where the dynamics of the system is nonlinear. The main layer of the composite plate is made of a graphite/epoxy composite (T300/976) with angle orientations of [-45/45/-45/45], with a total thickness of 1mm, in addition to two piezoelectric layers of type PZT G1195 N, each with a thickness of 0.1 mm. The two piezoelectric layers cover the entire area of the upper and lower surfaces of the laminated composite plate where the upper layer senses vibration and feeds it back to the controller, whereas the lower layer acts as an actuator which receives a voltage signal from the controller. The proposed control method involved using a Proportional-Integral-Derivative (PID) controller with a gain scheduling strategy where the system nonlinearity is addressed by changing the controller parameters momentarily, with optimal tuned parameters selected according to the dynamic characteristics during the vibration of the plate. The numerical model of the laminated composite plate is formulated using the finite element method (FEM) for large deflection vibrating composite plates. The controller performance was tested by simulating the vibration of the plate under the action of the controller for four cases with different vibration modes, and the results showed the effectiveness of the proposed PID controller with gain scheduling strategy. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article proposes a gain-scheduling procedure based on quasi-LPV modeling for a nonlinear coupled tank system to track the liquid level with zero steady-state error. The nonlinearities are directly represented by a parameter vector that varies within a bounded set constrained by the physical limits of the tank system levels. This approach enables accurate nonlinear system modeling using a linear parameter-varying model. State-feedback linear controllers are designed at the extreme vertices of the bounded set. The global controller is derived as the weighted average of local controller contributions, with the weighting determined by the instantaneous values of the parameter vector. Two interpolation mechanisms are proposed to implement this weighted averaging of the linear controllers. The results confirm the effectiveness of the proposed method in achieving accurate liquid level tracking.

This paper focuses on the design and comparative analysis of speed-dependent lateral control systems for autonomous vehicles, focusing on optimizing vehicular dynamics and passenger comfort to ensure stability and safety. Adapting control systems to varying speeds becomes crucial for maintaining stability and maneuverability as autonomous technologies progress. This study evaluates their effectiveness in real-time navigation scenarios within a simulated environment by applying gain-scheduled linear quadratic regulators and model predictive control. The results show that while traditional controllers, such as Pure Pursuit, perform adequately under constant speed conditions, adaptive model-based algorithms significantly enhance the performance, especially in dynamic driving situations involving speed variations. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the longitudinal vehicle speed is set as a constant value in traditional vehicle research, a robust gain scheduling fault diagnosis algorithm is designed based on an observer. Firstly, by taking the longitudinal vehicle speed as the scheduling variable, while considering actuator fault, model uncertainty and external interference, a mathematical model using linear parameter varying (LPV) is established on the basis of the traditional vehicle model. Then, the state of the actuator fault is reconstructed, and a robust gain scheduling observer with variable weight factor is designed to achieve dual estimation of vehicle state information and actuator fault signals. Finally, joint simulations using MATLAB/Simulink and Carsim are carried out under double lane change and snake maneuvers. The results show that the designed actuator fault diagnosis algorithm can effectively deal with the time‑varying characteristics of vehicle longitudinal speed, and track the vehicle state and fault information timely and accurately under complex working conditions, which breaks through the limitations of traditional vehicle fault diagnosis algorithms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Presented is a controller design methodology for practical ASAF (Active Suppression of Airfoil Flutter) applications. The approach utilizes a heuristic evolutionary optimization Genetic Algorithm (GA) combined with Latin Hypercube Sampling (LHS) to synthesize a gain scheduling controller for a four-degree-of-freedom aeroelastic airfoil model, accounting for uncertainties and nonlinearities. The objective is to design a robust controller that effectively suppresses flutter while optimizing performance metrics, specifically time-domain constraints and settling time. The LHS-GA method bridges the gap between traditional frequency-domain approaches and computationally expensive online optimization methods. Unlike frequency-domain methods that rely on trial-and-error or conservative assumptions, LHS-GA automatically synthesizes a robust static controller by directly addressing time-domain constraints and performance metrics. It eliminates the need for online optimization, ensuring robust stability under parametric uncertainty. This novel approach provides a systematic and efficient solution for designing controllers, offering an alternative to online optimization control methods and offline conservative frequency domain methods. By employing LHS, the proposed method efficiently explores the uncertain parameter space, promoting robustness in the solutions. The heuristic evolutionary optimization method selects optimal controllers by mapping the weight functions of the $H_{\infty } $ synthesis to time-domain metrics. Two case studies with different time-domain constraints are presented, demonstrating the approach capability to handle uncertainty and synthesize a robust controller that meets time-domain constraints and optimizes performance metrics. Finally, the results are compared against a traditional LQR controller design and a conventional $H_{\infty } $ controller design highlighting the advantages of the proposed method.

This paper focuses on the design and comparative analysis of speed-dependent lateral control systems for autonomous vehicles, focusing on optimizing vehicular dynamics and passenger comfort to ensure stability and safety. Adapting control systems to varying speeds becomes crucial for maintaining stability and maneuverability as autonomous technologies progress. This study evaluates their effectiveness in real-time navigation scenarios within a simulated environment by applying gain-scheduled linear quadratic regulators and model predictive control. The results show that while traditional controllers, such as Pure Pursuit, perform adequately under constant speed conditions, adaptive model-based algorithms significantly enhance the performance, especially in dynamic driving situations involving speed variations.

In order to reduce the asymmetric load acting on the blades of MW-class wind turbines, it is necessary to apply an individual pitch controller that independently adjusts the pitch angles of the three blades. This paper takes a new look at the relationship between the individual pitch controller applied to MW-class wind turbines and the vibration mode of the blades. The purpose of this study is to propose a method in which the individual pitch controller further reduces the 1P component of the bending moment in the out-of-plane direction acting on the blade, without exciting the in-plane vibration mode of the blade within the entire wind speed range, from the rated wind speed to the cut-out wind speed. To this end, a problem related to the excitation of the blade's vibration mode that may occur when applying the individual pitch controller to an NREL 5 MW wind turbine is examined, and a method that uses gain scheduling to overcome this problem is presented. It is confirmed that it is possible to solve the problem of exciting the first-order vibration mode in the in-plane direction of the blade that can occur in the high wind speed range by applying the proposed gain scheduling method to the individual pitch controller aimed at reducing the 1P component of the out-of-plane bending moment of the blade. [ABSTRACT FROM AUTHOR]

Summary: This article presents a model predictive control for dynamic positioning (DP) of floating caisson at different draughts. This article focuses on the maneuvres related to the control of the spatial situation of the caisson in connexion with the final caisson reference point and the process of descending to the seabed desired location. Different models of the caisson in order to perform realistic simulations and to test the robustness of the controller are considered. A gain scheduling model predictive control (GSMPC) has been implemented, with parameter switch among four model predictive control (MPC) controllers. In this way, a specific MPC for the dynamics of each draught will be used in each caisson model corresponding to a draught. Furthermore, a Kalman filter has been implemented to remove the disturbances. Finally, in the simulations results it is compared the performance of the proposed controller with a clasic one and also it is checked how the proposed controller is able to position the system in all draughts with stability and accurate dynamic control of the system. [ABSTRACT FROM AUTHOR]

Nowadays, high speed and high power density Brushless Direct Current (BLDC) motors have been widely utilized in the industrial area. Moreover, the design of motor simulation strategies is used in the drive system, which controls the complicated problems in the BLDC motors. However, speed regulation is a vital challenge since it affects the controller performance; the Proportional-Integral-Derivative (PID) controller is used in mechanical concerns. Therefore, this study introduces the novel Wavelet-based Fuzzy Adaptive Hybrid Bat-Vulture PID (WFA-HBVPID) controller to control the BLDC motor acceleration. Also, the developed WFA-HBVPID controller organizes the loads in the BLDC motor while verifying the gain scheduling conditions. Furthermore, this proposed PID controller is implemented using MATLAB/Simulink. Here, the performance of the motor is assessed in two ways, i.e., with hybrid optimization and without hybrid optimization. In addition, the efficiency of the developed controller has been checked over the time domain specifications like settling time, rise time, peak overshoot, and gain. To calculate the presented controller efficiency, the performances of the controller were compared with existing techniques. From the comparison of the outcomes, it is found that the proposed controller has less computation time and error rate. [ABSTRACT FROM AUTHOR]

In the practical control of nonlinear valve systems, PID control, as a model-free method, continues to play a crucial role thanks to its simple structure and performance-oriented tuning process. To improve the control performance, advanced gain-scheduling methods are used to schedule the PID control gains based on the operating conditions and/or tracking error. However, determining the scheduled gain is a major challenge, as PID control gains need to be determined at each operating condition. In this paper, a model-assisted online optimization method is proposed based on the modified Non-Dominated Sorting Genetic Algorithms-II (NSGA-II) to obtain the optimal gain-scheduled PID controller. Model-assisted offline optimization through computer-in-the-loop simulation provides the initial scheduled gains for an online algorithm, which then uses the iterative NSGA-II algorithm to automatically schedule and tune PID gains by online searching of the parameter space. As a summary, the proposed approach presents a PID controller optimized through both model-assisted learning based on prior model knowledge and model-free online learning. The proposed approach is demonstrated in the case of a nonlinear valve system able to obtain optimal PID control gains with a given scheduled gain structure. The performance improvement of the optimized gain-scheduled PID control is demonstrated by comparing it with fixed-gain controllers under multiple operating conditions. [ABSTRACT FROM AUTHOR]

A methodology for the synthesis of gain scheduling controllers, capable of stabilising a class of nonlinear systems, is proposed in this paper. The technique is based on the spatial discretization of the state-space, considering only a subset of the states, generating an approximate dynamics dependent on time-varying parameters. The relationship between the stability of both the original and approximation dynamics is shown through a convex condition, which is then used to develop the proposed synthesis methodology. The computed controllers assure the local stability within a known region and can be obtained by the resolution of a set of convex conditions. A series of numerical experiments illustrate the validity of the technique. [ABSTRACT FROM AUTHOR]

In this study, a novel framework for the flight control of a morphing unmanned aerial vehicle (UAV) based on linear parameter-varying (LPV) methods is proposed. A high-fidelity nonlinear model and LPV model of an asymmetric variable-span morphing UAV were obtained using the NASA generic transport model. The left and right wing span variation ratios were decomposed into symmetric and asymmetric morphing parameters, which were then used as the scheduling parameter and the control input, respectively. LPV-based control augmentation systems were designed to track the normal acceleration, angle of sideslip, and roll rate commands. The span morphing strategy was investigated considering the effects of morphing on various factors to aid the intended maneuver. Autopilots were designed using LPV methods to track commands for airspeed, altitude, angle of sideslip, and roll angle. A nonlinear guidance law was coupled with the autopilots for three-dimensional trajectory tracking. A numerical simulation was performed to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we present an LMI-based approach for comfort-oriented cruise control of an autonomous vehicle. First, vehicle longitudinal dynamics and a corresponding parameter-dependent state-space representation are explained and discussed. An LMI-based polytopic LQR controller is then designed for the vehicle speed to track the reference value in the presence of noise and disturbances, where the scheduling parameters are functions of the vehicle mass and the speed itself. An appropriate disturbance force compensation term is also included in the designed controller to provide a smoother response. Then we detail how the reference speed is calculated online, using polynomial functions of the given desired comfort level (quantified by the vertical acceleration absorbed by the human body) and of the road type characterized by road roughness. Finally, time-domain simulations illustrate the method's effectiveness. [ABSTRACT FROM AUTHOR]

This study is concerned with proposing much more effective Luenberger-like fuzzy observers than relevant multiinstant observers reported in recent literature, in other words, less conservative results can be given while less computing resource is consumed as an important premise. For the first time, the variation bound of two adjacent time-variant Lyapunov matrices encountered in the synthesis of fuzzy observer is characterized by a contraction factor, and thus the key replacement of the lagged term is developed in order to construct an effective synchronization framework of fuzzy observer synthesis. Since there only exist the current-time normalized fuzzy weighting functions in the obtained synchronization framework, the conservatism caused by the mismatch of different sampling instants can be avoided. Based on this, a lightweight gain-scheduling law is designed in which a large number of redundancy gains in recent multiinstant observers can be condensed in order to save computing resource to the greatest extent. Finally, two benchmark examples are borrowed for validating the superiority of our proposed results. [ABSTRACT FROM AUTHOR]

In this study, a pitch H-infinity control algorithm was developed for variable-speed–variable-pitch (VSVP) wind turbines to improve the rotor standard deviation of the wind turbines under normal and extreme wind conditions. The pitch H-infinity control algorithm only uses H-infinity control in the blade pitch control loop in the rated power region, and conventional torque gain scheduling algorithms are applied in the partial power region. The performance of this controller was verified using simulations of a 5 MW wind turbine using the commercial aeroelastic simulation code Bladed. The performance of the pitch H-infinity controller was compared with that of the conventional proportional-integral (PI) control algorithm under three different operating conditions: normal operating conditions without sensor noise, normal operating conditions with sensor noise, and extreme operating conditions without sensor noise based on the wind turbine design standard by IEC. Based on the simulation results with two different wind speed regions, namely, the transition region and the rated power region, it was found that the proposed pitch H-infinity controller showed better rotor speed standard deviation performance in the three operating conditions and achieved lower standard deviations of the rotor speed and the electrical power without affecting the mean electrical power. [ABSTRACT FROM AUTHOR]

A novel gain‐scheduling fault estimation is developed on the direction of improving algorithm efficiency based on discrete‐time Takagi–Sugeno fuzzy models. Different from the traditional results in which the zero‐order free matrices are applied, a novel free matrix approach is proposed and the higher‐order free matrices are for the first time introduced in designing conditions of the fuzzy gain‐scheduling fault estimation observer. For each working mode, its proprietary properties about current‐time normalized fuzzy weighting functions are made full use and thus the anti‐interference ability of the developed fuzzy gain‐scheduling fault estimation observer is enhanced without any increase of either the consumed time of solving the required designing criterion or the number of gain scalars needed to be stored in random access memory of the computer in real time. It does provide a good chance to obtain equal or better results of fuzzy gain‐scheduling fault estimation with the usage of much less computing resource than before, which is much meaningful to the applications of theoretical methods. Finally, the effectiveness and superiority of our developed free matrix approach are tested and validated by using performance comparisons on the benchmark example. [ABSTRACT FROM AUTHOR]

Controlling the nonlinear process is a very challenging task in the process plant, whereby it depends on the practitioners' knowledge and skills. This paper aims at developing Gain Scheduling (GS) based controller tunings to obtain the trade-off controller tunings for both servo and regulatory control objectives at the Low, Medium and High operating levels supported by optimization analysis. At first, the research obtains First Order plus Dead Time (FOPDT) models of various operating levels from the Gravity Drained function of LOOP-PRO software. The dynamic characteristics of GA are compared with Particle Swarm Optimization (PSO), which showed GA produced more desirable responses and performance indexes. The analysis also compares process responses and performance indexes of GA with manually calculated controller tunings. The overall result shows that GA optimization analysis produces the most reasonable controller tunings for consistent control performance compared to other methods. Ultimately, GA algorithms were adopted into a Graphical User Interface (GUI) of MATLAB software, allowing the automated generation of the controller tunings for the identified models. [ABSTRACT FROM AUTHOR]

In this paper, relaxed observer-based state estimation of discrete-time Takagi–Sugeno fuzzy systems is proposed via developing a new augmented matrix approach. In contrast with the recent method reported in the literature, a more advanced introduction mode of additional matrices is developed without any redundant constraint under the framework of homogeneous polynomials. Then, the proposed augmented matrices can be built in order to depict the characteristic information of each possible working mode in a superior way, and thus our obtained result is less conservative than the recent one. Moreover, it is worth noting that that the recent method belongs to the special case of ours. Finally, two numerical simulations are given to test and validate the superiority and generality of the developed method. [ABSTRACT FROM AUTHOR]

This paper presents a load frequency control (LFC) design using the variable structure model predictive controller (VSMPC) for gain scheduling (GS) of PI controller in PV connected thermal for multi‐area system. Due to the increasing impact of renewable energy sources into the grid system, the system frequency deteriorates. The proposed PV connected thermal system is evaluated under varying load conditions under increased penetration of the PV source. The combination of renewable energy into the thermal power system aggravates the frequency and it is indispensable to mitigate such a problem by introducing an optimal controller. Perturbation in load greatly affects the system frequency and this problem is addressed by using VSMPC based gain scheduling knowing the effect of proportional and integral in transient and steady‐state conditions. The oscillations in frequency are controlled by a proposed controller and the impact of frequency under penetration of PV is monitored. Further verification of the proposed technique is accomplished through communication delay in the governor and turbine. The Hardware in the loop (HIL) which validates the accuracy and real time performance of the controller. Finally, the validation of the proposed controller is compared with MPC to tune PI controller and renowned evolutionary tuned techniques like particle swarm optimization (PSO), genetic algorithm (GA) and firefly algorithm (FA) to optimize PI controller. The VSMPC with GS of PI controller shows promising results. [ABSTRACT FROM AUTHOR]

A novel multi-instant gain-scheduling scheme is proposed in order to further improve the state estimation performance of discrete-time Takagi–Sugeno fuzzy systems in this article. Different from those existing methods, the enabled working pattern at each sampling instant can be determined in cycle according to utilization of the updated information of adjacent normalized fuzzy weighting functions in depth, and then the featured time-varying free matrices are introduced for each working pattern, which can remove those existing constraints on traditional free matrices. Consequently, the conservatism can be reduced significantly and it does broaden the range of application for observer-based fuzzy state estimation. Despite the fact that the quantity of the free matrices becomes much bigger than before, it needs to be pointed out that the actual computational complexity only increases by a small amount through depressing the number of cycles. Given that the developed designing process belongs to an offline computation, it is an acceptable tradeoff between the increasement of computational burdens and the reduction of conservatism. In the end, the effectiveness of the developed method is illustrated through the detailed analysis of numerical simulations. [ABSTRACT FROM AUTHOR]

This article is devoted to the development of multi-instant gain-scheduling stabilization of discrete-time Takagi–Sugeno fuzzy systems by developing a time-variant balanced matrix approach. Many of the reported advances have provided more and more relaxed results via choosing higher order dependent gain matrices, which will be a trouble in the case of real-time implementation. Therefore, a multi-instant gain-scheduling fuzzy state-feedback controller with different working modes is given to stabilize the closed-loop systems by updating the enabled pair of lower order gain matrices in cycle. For each possible working mode, the real-time situation information of the normalized fuzzy weighting functions can be fully considered by using a set of time-variant balanced matrices in accordance to certain rules and, thus, much freedom is introduced for proposing less conservative results. More importantly, our relaxed result is obtained without requiring higher order dependent gain matrices than the existing ones, i.e., its conservatism is reduced in a more meaningful way and thus it is more conducive to real-time implementation. Finally, both the effectiveness and the superiority of the proposed results are discussed and validated in the numerical section. [ABSTRACT FROM AUTHOR]

The automatic control system plays a crucial role in industries for controlling the process operations. The automatic control system provides a safe and proper controlling mechanism to avoid environmental and quality problems. The control system controls pressure flow, mass flow, speed control, and other process metrics and solves robustness and stability issues. In this manuscript, The Hybrid controller approach like proportional integral (PI) and proportional derivative (PD) based fuzzy logic controller (FLC) using with and without gain scheduling approach is modeled for the compressor to improve the robustness and error response control mechanism. The PI/PD-based FLC system includes step input function, the PI/PD controller, FLC with a closed-loop mechanism, and gain scheduler. The error signals and control response outputs are analyzed in detail for PI/PD-based FLC's and compared with conventional PD/PID controllers. The PD-based FLC with the Gain scheduling approach consumes less overshoot time of 74% than the PD-based FLC without gain scheduling approach. The PD-based FLC with the gain scheduling approach produces less error response in terms of 7.9% in integral time absolute error (ITAE), 7.4% in integral absolute error (IAE), and 16% in integral square error (ISE) than PD based FLC without gain scheduling approach. [ABSTRACT FROM AUTHOR]

This paper presents a novel observer-based gain-scheduling path following control algorithm for autonomous electric vehicles subject to time delay. Firstly, the lateral dynamic model of the autonomous electric vehicle is constructed by a polytope with four vertices, in which the issues of the time-varying longitudinal velocity and nonlinear tyre dynamics are accurately described. Secondly, taking the time delay encountered in the process of signal transmission into consideration, the observer-based path following controller is proposed by using easily measured vehicle states. In the algorithm, the observer and controller gains are gain scheduled according to the actual longitudinal velocity. Thirdly, based on Lyapunov stability theory, an appropriate Lyapunov–Krasovskii functional is constructed to derive sufficient conditions of the controller, which is effective to ensure the asymptotical stability of the closed-loop path following error system with a guaranteed H ∞ performance. Specially, for ease of computation, the sufficient conditions of controller design are developed in terms of a set of linear matrix inequalities. Finally, numerical simulations are implemented to illustrate the efficiency and superiority of the proposed method in comparison with the existing method. [ABSTRACT FROM AUTHOR]

In the practical control of nonlinear valve systems, PID control, as a model-free method, continues to play a crucial role thanks to its simple structure and performance-oriented tuning process. To improve the control performance, advanced gain-scheduling methods are used to schedule the PID control gains based on the operating conditions and/or tracking error. However, determining the scheduled gain is a major challenge, as PID control gains need to be determined at each operating condition. In this paper, a model-assisted online optimization method is proposed based on the modified Non-Dominated Sorting Genetic Algorithms-II (NSGA-II) to obtain the optimal gain-scheduled PID controller. Model-assisted offline optimization through computer-in-the-loop simulation provides the initial scheduled gains for an online algorithm, which then uses the iterative NSGA-II algorithm to automatically schedule and tune PID gains by online searching of the parameter space. As a summary, the proposed approach presents a PID controller optimized through both model-assisted learning based on prior model knowledge and model-free online learning. The proposed approach is demonstrated in the case of a nonlinear valve system able to obtain optimal PID control gains with a given scheduled gain structure. The performance improvement of the optimized gain-scheduled PID control is demonstrated by comparing it with fixed-gain controllers under multiple operating conditions.

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The majority of the past research dealing with lane-changing controller design of autonomous vehicles (A V s) is based on the assumption of full knowledge of the model dynamics of the A V and the surrounding vehicles. However, in the real world, this is not a very realistic assumption as accurate dynamic models are difficult to obtain. Also, the dynamic model parameters might change over time due to various factors. Thus, there is a need for a learning-based lane change controller design methodology that can learn the optimal control policy in real time using sensor data. In this paper, we have addressed this need by introducing an optimal learning-based control methodology that can solve the real-time lane-changing problem of A V s, where the input-state data of the A V is utilized to generate a near-optimal lane-changing controller by approximate/adaptive dynamic programming (ADP) technique. In the case of this type of complex lane-changing maneuver, the lateral dynamics depend on the longitudinal velocity of the vehicle. If the longitudinal velocity is assumed constant, a linear parameter invariant model can be used. However, assuming constant velocity while performing a lane-changing maneuver is not a realistic assumption. This assumption might increase the risk of accidents, especially in the case of lane abortion when the surrounding vehicles are not cooperative. Thus, in this paper, the dynamics of the A V are assumed to be a linear parameter-varying system. Thus we have two challenges for the lane-changing controller design: parameter-varying, and unknown dynamics. With the help of both gain scheduling and ADP techniques combined, a learning-based control algorithm that can generate a near-optimal lane-changing controller without having to know the accurate dynamic model of the A V is proposed. The inclusion of a gain scheduling approach with ADP makes the controller applicable to non-linear and/or parameter-varying A V dynamics. The stability of the learning-based gain scheduling controller has also been rigorously proved. Moreover, a data-driven lane-changing decision-making algorithm is introduced that can make the A V perform a lane abortion if safety conditions are violated during a lane change. Finally, the proposed learning-based gain scheduling controller design algorithm and the lane-changing decision-making methodology are numerically validated using MATLAB, SUMO simulations, and the NGSIM dataset. • A unique method for autonomous vehicle lane-changing using online data is proposed. • The proposed learning-based optimal controller ensures stability under parameter variation. • Convergence of the learning-based optimal control is ensured by a persistent excitation condition. • The proposed controller reduces computational load and improves safety over traditional MPC. [ABSTRACT FROM AUTHOR]

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. • A modified active disturbance rejection control based on gain scheduling is structured. • The convergence of extended state observer for the proposed active disturbance rejection control is educed. • Advantages in tracking performance, disturbance rejection abilities and robustness have been illustrated by comparative simulations. • The proposed active disturbance rejection control is applied to main steam pressure system of an in-service CFB unit. [ABSTRACT FROM AUTHOR]

This paper proposes a new and simple method to combine incremental control with the classical proportional-integral (PI) control in order to increase the robustness properties of the resulting control law. The proposed method avoids hidden coupling terms in a similar manner to the differential PI controller implementation (DPI) and hence allows for fast gain scheduling. The integral action is accomplished by feedback of actuator measurements and therefore avoids integrator windup without complicated anti-windup schemes. The control law obtains an increase in robust performance with a response less sensitive to variations in flight dynamic parameters when compared to the equivalent PI controller. Improvement in disturbance rejection is also obtained with a faster and more robust response. Increased robustness toward input time delay is observed by example. The trade-off between increased robustness and sensitivity to sensor noise and delay can be adjusted with an intuitive tuning parameter. The novel method, denoted incremental DPI (iDPI), is investigated in the context of fixed-wing flight control. The concept is verified as a retrofit for an industry-level PI baseline controller for the longitudinal motion of a fixed-wing aircraft using a high-fidelity simulation model. [ABSTRACT FROM AUTHOR]