Search

This paper presents an improved Model Predictive Control (MPC) for path tracking of a nonholonomic mobile robot with a differential drive. Nonlinear dynamics and nonholonomic constraints make the optimisation problem of MPC for the robot challenging. Nonlinear dynamics of the robots are expressed by a Linear Parameter Varying (LPV), and a Recurrent Neural Network (RNN) solves the constrained optimisation problem, providing optimal velocities. Moreover, an interpolation-based approach has been introduced to augment the region of attraction. The algorithm ensures stability in the presence of bounded disturbances through the inclusion of free control moves in the control law. The controller efficiency has been evaluated in two scenarios in a hospital setting. The simulation results illustrate that the proposed method performs better than nonlinear MPC and standard LPV-based MPC in terms of computational cost, disturbance rejection, and region of attraction. [ABSTRACT FROM AUTHOR]

This paper presents a linear parameter varying (LPV) interpolation modeling method and modal-based pole placement (PP) control strategy for the ball screw drive (BSD) with varying dynamics. The BSD is modeled as a global LPV model with position-load dependence by selecting position and load as scheduling variables. The global LPV model is obtained from local subspace closed-loop identification and LPV interpolation modeling. A modal-based global LPV model is obtained through the similarity transformation. Based on this model, a modal-based LPV PP control strategy is proposed to achieve various modal control. Specifically, a state feedback control structure with an LPV state observer is designed to realize online state estimation and real-time state feedback control of modal state variables which cannot be measured directly. The steady-state error is minimized by introducing an error state space (SS) model with the integral effects. Moreover, the stability of the closed-loop system is analyzed according to the controllable decomposition and principle of separation. It is experimentally demonstrated that the proposed modal-based LPV PP control strategy can effectively achieve precise tracking and outstanding robustness meantime. • A global LPV model of the BSD is built by interpolation modeling. • A modal-based LPV pole placement control is proposed to achieve modal control. • The proposed modeling and control achieve precise tracking and excellent robustness. [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]

Quadcopter unmanned aerial vehicle is a multivariable, coupled, unstable, and underactuated system with inherent nonlinearity. It is gaining popularity in various applications and has been the subject of numerous research studies. However, modelling and controlling a quadcopter to follow a trajectory is a challenging issue for which there is no unique solution. This study proposes an optimal hybrid quadcopter control with MPC-based backstepping control for following a reference trajectory. The outer-loop controller (backstepping controller) regulates the quadcopter’s position, whereas the inner-loop controller (Model Predictive Control) regulates its attitude. The translational and rotational dynamics of the quadcopter are analyzed utilizing the Newton-Euler method. After that, the backstepping controller (BC) is created, which is a recurrent control method according to Lyapunov’s theory that utilizes a genetic algorithm (GA) to choose the controller parameters automatically. In order to apply a linear control technique in the presence of nonlinearities in the quadcopter dynamics, Linear Parameter Varying (LPV) Model Predictive Control (MPC) structure is developed. Simulation validated the dynamic performance of the proposed optimal hybrid MPC-based backstepping controller of the quadcopter in following a given reference trajectory. The simulations demonstrate the fact that using a command control input in trajectory tracking, the proposed control algorithm offers suitable tracking over the assigned position references with maximum appropriate tracking errors of 0.1 m for the �� and �� positions and 0.15 m for the �� position.

Ball screw drive (BSD) is a precision transmission mechanism widely used in high-precision positioning or tracking systems. The dynamic behavior of BSD varies with position and load, which causes tracking errors and poor robustness. Therefore, this paper proposes a polytopic linear parameter varying (LPV) model to express the varying dynamic behavior of BSD. The parameters of the LPV model are identified by the closed-loop frequency domain method and Levenberg–Marquardt iterative algorithm. Based on the polytopic LPV model, an LPV gain scheduling (GS) H ∞ controller is proposed for the BSD with varying dynamics. Specifically, the controller is designed through polytope-based GS representation and mixed sensitivity synthesis. The most significant part is the proposal of a GS H ∞ control algorithm to implement controller parameters that change with changing dynamics. Moreover, the stability of the closed-loop system is achieved by quadratic stabilization with state feedback. Finally, identification experiments and trajectory-tracking comparative experiments are carried out. The experimental results demonstrate that the proposed polytopic LPV modeling and GS H ∞ control synthesis are effective in achieving accurate trajectory tracking and excellent robustness. • A LPV model takes position and load as scheduling variables. • The affine parameters are identified by LM iterative algorithm. • A polytopic LPV gain scheduling H ∞ control strategy achieves good performance. [ABSTRACT FROM AUTHOR]

Quadcopter unmanned aerial vehicle is a multivariable, coupled, unstable, and underactuated system with inherent nonlinearity. It is gaining popularity in various applications and has been the subject of numerous research studies. However, modelling and controlling a quadcopter to follow a trajectory is a challenging issue for which there is no unique solution. This study proposes an optimal hybrid quadcopter control with MPC-based backstepping control for following a reference trajectory. The outer-loop controller (backstepping controller) regulates the quadcopter's position, whereas the inner-loop controller (Model Predictive Control) regulates its attitude. The translational and rotational dynamics of the quadcopter are analyzed utilizing the Newton-Euler method. After that, the backstepping controller (BC) is created, which is a recurrent control method according to Lyapunov's theory that utilizes a genetic algorithm (GA) to choose the controller parameters automatically. In order to apply a linear control technique in the presence of nonlinearities in the quadcopter dynamics, Linear Parameter Varying (LPV) Model Predictive Control (MPC) structure is developed. Simulation validated the dynamic performance of the proposed optimal hybrid MPC-based backstepping controller of the quadcopter in following a given reference trajectory. The simulations demonstrate the fact that using a command control input in trajectory tracking, the proposed control algorithm offers suitable tracking over the assigned position references with maximum appropriate tracking errors of 0.1 m for the X and Y positions and 0.15 m for the Z position. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are rapidly emerging as a crucial sector within the automotive industry. Several companies are investing in the development and enhancement of technologies, which presents challenging problems in the context of robotics and control. In particular, this work primarily focuses on the creation of an autonomous vehicle architecture utilizing the robotic operating system (ROS2) framework, accompanied by advanced control algorithms. In order to facilitate the development and implementation of lateral vehicle dynamics controllers, a reduced-size automated car available in GIPSA-Lab is used as an experimental platform. The objective is to design robust controllers capable of achieving optimal tracking and stability. Accordingly, this problem is tackled under different robust control syntheses, considering the H ∞ approach: using linear time-invariant (LTI) and linear parameter-varying (LPV) model representations. Several simulation and experimental results are included to demonstrate the efficiency of the controllers. [ABSTRACT FROM AUTHOR]

This article proposes a model predictive control (MPC) strategy for a quadrotor drone trajectory tracking based on a compact state-space model based on a quasi-linear parameter varying (qLPV) representation of the nonlinear quadrotor. The use of a qLPV representation allows for faster execution times, which can be suitable for real-time applications and for solving the optimization problem using quadratic programming (QP). The estimation of future values of the scheduling parameters along the prediction horizon is made by using the planned trajectory based on the previous optimal control actions. The performance of the proposed approach is tested by following different trajectories in simulation to show the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, a feedback controller based on the extended state observer is proposed for fully actuated systems. First, a generalized proportional-integral observer is designed to estimate states and disturbances simultaneously. Using the linear parameter varying approach and the convexity principle, a linear matrix inequality condition is given to obtain the observer gains. Second, on the basis of the full-actuation property and the estimated states, a feedback controller, utilizing estimated disturbances to compensate for system disturbances, is designed to make all the states of the closed-loop system uniformly ultimately bounded. In addition, if disturbances are constant or slow time-varying, the observation errors and the states of closed-loop system are all exponentially convergent. Two illustrations are provided to show the validity and practicality of the proposed approach. Simulation results show that the estimated disturbances can follow the true values with relatively small errors, so compensating the system disturbances with estimated values can effectively reduce the ultimate bounds of states of the closed-loop system., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 International Society of Automation. Published by Elsevier Ltd. All rights reserved.)

This article proposes a model predictive control (MPC) strategy for a quadrotor drone trajectory tracking based on a compact state-space model based on a quasi-linear parameter varying (qLPV) representation of the nonlinear quadrotor. The use of a qLPV representation allows for faster execution times, which can be suitable for real-time applications and for solving the optimization problem using quadratic programming (QP). The estimation of future values of the scheduling parameters along the prediction horizon is made by using the planned trajectory based on the previous optimal control actions. The performance of the proposed approach is tested by following different trajectories in simulation to show the effectiveness of the proposed control scheme.

This paper presents a new method for modeling and Attitude Control of Autonomous Helicopters (A.H.) based on a polytopic linear parameter varying approach using parameter set mapping with the Principal Component Analysis (PCA). The polytopic LPV model is extracted based on angular velocities and Euler angles, that is influenced by flopping angles, by generating a set of data over the different trim points. Because of the high volume of trim data, parameter set mapping based on (PCA) is used to reduce the parameter set dimension. State feedback control law is proposed to stabilize the system by introducing a Linear Matrix Inequality (LMI) set over the vertices models. The proposed controller is performed for an Autonomous Helicopter in different scenarios. All the scenarios are investigated with the PCA algorithm as a technique for reducing the computational volume and increasing the speed of solving the LMI set. The simulation results of implementing the planned controller on the nonlinear model of an autonomous helicopter in different scenarios show the effectiveness of the proposed scheme.

Cyber-physical systems (CPSs) integrate controllers, sensors, actuators, and communication networks. Tight integration with communication networks makes CPSs vulnerable to cyberattacks. In this paper, the impact of denial of service (DoS) attacks on the stability of cyber physical systems is investigated, with a focus on the transmission control protocol (TCP). A sufficient stability condition is extracted in linear matrix inequality (LMI) form. The TCP-CPS under DoS attack is modeled as a switching linear parameter-varying (LPV) time-delay system using the Markov jump model. Subsequently, a parameter-dependent stabilizing controller is designed for CPSs under DoS attacks, taking into account network parameters. Finally, the efficiency and feasibility of the findings are demonstrated through a well-known case study in the networked control systems literature. [ABSTRACT FROM AUTHOR]

This research considers a new gain-scheduled output feedback (GSOF) control method in order to control wind turbine embedded with hydrostatic transmission. First of all, the linear parameter varying (LPV) representation of nonlinear system is attained. Then the GSOF control structure based on induced ℒ 2 -gain performance minimization is designed. The control method is particularly suited to cope with wind speed measurement uncertainty. Also another advantage of the presented control method lies in controlling wind turbine in the whole operating region without performance reduction. The control method is validated through simulation on 5 MW wind turbine benchmark and also is compared with gain scheduled quadratic regulator control method. Simulation results indicate the effectiveness of the presented algorithm in improving the overall performance of the wind turbine in whole wind speed region in existence of the wind speed uncertainty. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of designing smooth switching control based on the coprime factorization method. Aiming at the instantaneous chattering phenomenon generated by the linear parameter varying (LPV) controller during the switching moment, the moving region of the gain-scheduling variables is divided into a specified number of local subregions with overlapped region. Riccati inequality is used to solve the central controller for $H_{\infty} $ performance. The Youla free parameters are designed using the coprime factorization for each parameter sub-region by considering the system’s global and local performance requirements. Youla free parameter switching is used to improve the smoothness of switching and suppress transient response disturbance. Finally, the effectiveness of the method is verified by a simulation example.

• A model-based strategy to reduce the effects induced by multiple actuator and sensor faults in non-linear chemical processes is presented. • An observer-based fault detection and diagnosis scheme was used to generates an early and detailed fault information. • A compensation procedure throughout the use of an integrated optimization-based identification and model predictive control technique was developed. • The observers convergence and the controller stability are guaranteed in terms of linear matrix inequalities problems. • A numerical simulation on a highly non-linear continuous stirred tank reactor shows the benefits of the proposed method in fault tolerance. [Display omitted] Background: Process safety is a major concern in the researchers community, both in the past and today. However, the hardware complexity and the involved non-linear dynamics of industrial processes could lead to unsatisfactory behavior of traditional control methods. Methods: To cope with these issues, this paper presents a model-based strategy for fault tolerance in non-linear chemical processes. Specifically, an observer-based fault detection and diagnosis scheme was implemented, which generates early and detailed fault information. Therefore, this valuable data was used to compensate the effects induced by actuator and sensor faults throughout the use of an integrated optimization-based identification and model predictive control technique, which allowed to track a reference even in the presence of faults. Significant Findings: This method reinforces the inherent robustness against faults of linear parameter varying predictive controllers. Moreover, the observers convergence and the controller stability were guaranteed in terms of linear matrix inequalities problems. A simulation based on a typical chemical industrial process, the highly non-linear continuous stirred tank reactor shows that the proposed method can achieve satisfactory performance in fault tolerance. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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 paper presents distributed output feedback control of a class of distributed linear parameter varying systems with switching topology and parameter varying time delay. To formulate the synthesis conditions for the distributed controller in terms of LMIs, the delay dependent bounded-real lemma based on parameter-dependent Lyapunov–Krasovskii functionals is used. The efficacy of the result is illustrated by applying it to two real-world examples pertaining to the consensus problem of multi-agent systems. [ABSTRACT FROM AUTHOR]

In order to keep wind turbines connected and in operation at all times despite the occurrence of some faults, advanced fault detection and accommodation schemes are required. To achieve this goal, this paper proposes to use the Linear Parameter Varying approach to design an Active Fault Tolerant Control for wind turbines. This Active Fault Tolerant Control is integrated with a Fault Detection and Isolation approach. Fault detection is based on a Linear Parameter Varying interval predictor approach while fault isolation is based on analysing the residual fault signatures. To include fault-tolerance in the control system (already available in the considered wind turbine case study based on the well known SAFEPROCESS benchmark), the information of the Fault Detection and Isolation approach block is exploited and it is used in the implementation of a virtual actuator and sensor scheme. The proposed Active Fault Tolerant Control is evaluated using fault scenarios which are proposed in the wind turbine benchmark to assess its performance. Results show the effectiveness of the proposed Active Fault Tolerant Control approach in faulty situation. [ABSTRACT FROM AUTHOR]

A gain scheduled adaptive control scheme based on a polytopic linear parameter varying (LPV) system is proposed in this paper to damp sub-synchronous oscillations (SSOs) for power system integrated with permanent magnet synchronous generators (PMSGs) and operating under high wind speed variability. Firstly, the linearized state space model of the PMSG-integrated power system is established. Secondly, a polytopic LPV system is developed to account for the stochastic drift behavior and wind speed variability. Next, a gap metric-based nonlinearity measurement method is used for configuring the vertices of the polytope. The adaptive polytopic LPV controller is solved using linear matrix inequality and gain scheduling control theory, while mode identification based on a sliding window fast Fourier transform is employed for adaptively adjusting the polytopic LPV controller. Finally, the proposed polytopic LPV adaptive control scheme is compared with three other control techniques to demonstrate its effectiveness in suppressing SSOs. [ABSTRACT FROM AUTHOR]

This paper presents a new method for modeling and Attitude Control of Autonomous Helicopters (A.H.) based on a polytopic linear parameter varying approach using parameter set mapping with the Principal Component Analysis (PCA). The polytopic LPV model is extracted based on angular velocities and Euler angles, that is influenced by flopping angles, by generating a set of data over the different trim points. Because of the high volume of trim data, parameter set mapping based on (PCA) is used to reduce the parameter set dimension. State feedback control law is proposed to stabilize the system by introducing a Linear Matrix Inequality (LMI) set over the vertices models. The proposed controller is performed for an Autonomous Helicopter in different scenarios. All the scenarios are investigated with the PCA algorithm as a technique for reducing the computational volume and increasing the speed of solving the LMI set. The simulation results of implementing the planned controller on the nonlinear model of an autonomous helicopter in different scenarios show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper presents an LPV output feedback control strategy for a 4-DOF control moment gyroscope (CMG). The LPV model constructed by linearizing the nonlinear model around moving operating points contains fifteen scheduling variables causing a massive computational complexity involved in controller synthesis. A practical iterative approach based on kernel principal component analysis (kernel-PCA) is proposed for reduction of scheduling variables and LPV controller synthesis. The LPV controller designed for a fixed rotor speed is applied in feedback to a full nonlinear model of the CMG at different rotor speeds. Simulation results show that the controller obtained through the proposed method ensures improved performance in terms of increased operating range for CMG with reduced control effort while maintaining the transient performance and tracking accuracy.

To research the tendencies of immediate change in the scheduled parameters, a linear parameter–varying control strategy is made in real time for lab-based inverted pendulum system. An adjusted and observing virtual instrument is designed in LabVIEW to construct this mechanism. To obtain the transient response data, a step response is transmitted by virtual instrument to the inverted pendulum by which model of inverted pendulum is identified. System model depicts its characteristics, so this model is employed in designing controller. To improve the performance and robustness, linear parameter–varying controller is created to deal with parameter distinctions. Furthermore, the effectiveness of this designed robust controller is checked by integrating on hardware system. The discovered model is checked by evaluating the presented approaches in real time and simulation outcomes with traditional controller as proportional–integral–derivative controller and linear–quadratic regulator. [ABSTRACT FROM AUTHOR]

The active suspension system of a vehicle manipulated using electro-hydraulic actuators is a challenging nonlinear control problem. In this research work, a novel Linear Parameter Varying (LPV) State-Space (SS) model with a fictional input is proposed to represent a nonlinear half-car active suspension system. Four different scheduling parameters are used to embed the nonlinearities of both the suspension and the electro hydraulic actuators to represent its nonlinear behavior. A recursive least squares (RLS) algorithm is used to predict the future behavior of the scheduling parameters along the prediction horizon. A Model Predictive Control-Linear Quadratic Regulator (MPC-LQR) is implemented as the control strategy and, to ensure stability, Quadratic Stability conditions are imposed as Linear Matrix Inequalities (LMI) constraints. Furthermore, the inclusion of attraction sets to overcome the conservative performance imposed by the Quadratic Stability conditions is included, as well as a terminal set were the switching between the MPC and the LQR controller is made. Simulations results for the half-car active suspension model over a typical road disturbance are tested to show the effectiveness of the proposed MPC-LQR-LPV controller with quadratic stability conditions in terms of comfort and road-holding.

This paper addresses three major issues in dissimilar redundant actuation system when operates in active/active mode. The first one is to reduce the effect of force fighting. The second one is to obtain precise tracking performance of the control surface. The third one is to induce fault tolerance in the presence of faults in hydraulic actuator. The proposed strategy is to design LPV integral sliding mode controller for each actuator in a dissimilar actuation system. The nonlinear dynamics of each actuator is first transformed into LPV form and employed tensor product model transformation to convert into polytopic LPV form. The integral sliding mode FTC is designed based on the LPV model of actuators. To induce the fault tolerance, the nonlinear injection term of proposed integral sliding mode control law is made adaptive subject to the severity of the fault. In order to verify the robustness of the proposed scheme, an external disturbance acting as an airload is applied at the control surface input. Finally, the proposed strategy is applied on the nonlinear model of system to validate the dominant performance as compared to existing methods in the literature. In the simulations, three types of faults in hydraulic actuator are considered and the performance of closed loop system is analyzed.

This paper introduces a novel fast model predictive control (MPC) methodology based on linear parameter-varying (LPV) systems. The proposed approach can deal with large-scale problems better than conventional fast MPC methods. First, the equality constraints given by the model equations are not eliminated to get a condensed quadratic programming (QP) problem, as the model of the LPV system changes and it will be time-consuming to reformulate the QP problem at each sampling time. Instead, the proposed approach constructs a sparse QP problem by keeping the equality constraints. Although the resulting QP problem has a larger dimension than the condensed one, it can be reformulated and solved as a system of piecewise affine equations given by the Karush-Kuhn-Tucker conditions of optimality. Finally, the problem will be solved through a Newton-method and an exact line search in a fast way. The performance is tested and compared with off-the-shelf QP solvers on the conventional buck dc-dc converter control problem both in simulations and the experiments on FPGA. The proposed methodology works well for the controller and is especially faster in comparison with some other conventional algorithms for large prediction horizons.

Summary: In this paper, a new framework for the synthesis of a class of sliding mode observers for affine linear parameter varying (LPV) systems is proposed. The sliding mode observer is synthesized by selecting the design freedom via linear matrix inequalities (LMIs). Posing the problem from a small gain perspective allows existing numerical techniques from the literature to be used for the purpose of synthesizing the observer gains. In particular, the framework allows affine parameter‐dependent Lyapunov functions to be considered for analyzing the stability of the state estimation error dynamics, to help reduce design conservatism. Initially a variable structure observer formulation is proposed, but by imposing further constraints on the LMIs, a stable sliding mode is introduced, which can force and maintain the output estimation error to be zero in finite time. The efficacy of the scheme is demonstrated using an LPV model of the short period dynamics of an aircraft and demonstrates simultaneous asymptotic estimation of the states and disturbances. [ABSTRACT FROM AUTHOR]

In this paper, we present a practical approach to address the vehicle lateral control problem. The proposed method can overcome practical problems associated with vehicle lane changes on highways. The vehicle state with respect to the road, which is called lateral offset, jumps in camera vision sensors when the vehicle changes lanes. Thus, in this study, we solve the state jump problem by translating it into a new domain called the cylinder domain. In addition, we proposed the design of a parameter-varying controller to overcome the nonlinear term of vehicle dynamics by considering it as a varying parameter. The proposed method does not consider the lateral offset jump when changing lanes. Furthermore, its significant advantage in terms of computation time makes it suitable for implementation in low-cost electronic control units (ECUs). The proposed algorithm is validated using MATLAB/Simulink with the vehicle dynamics analysis program CarSim. [ABSTRACT FROM AUTHOR]

This note tackles the problem of variable-speed variable-pitch wind turbine control in the whole wind speed range. Gain-scheduled (GS) control strategy is exploited to provide maximum power point tracking operation and to achieve effective pitch angle regulation. The wind turbine is modeled in the form of a linear parameter varying (LPV) system using wind speed as the scheduling parameter to cope with the varying system dynamics. The wind speed measurement errors are systematically considered in the design procedure. Modeling error is handled through employing an induced L 2 -gain performance criterion. The proposed method results a common gain-scheduled controller for the whole operating region. A benchmark example illustrates the advantages of the presented method. • Gain-scheduled controller synthesis using a linear parameter varying model. • Employing inexact wind speed measurement as the scheduling parameter. • A common controller for both partial- and full-load operating regions. • Stability and performance are analytically guaranteed. [ABSTRACT FROM AUTHOR]

As a significant active safety technology towards fully autonomous driving, driver-automation co-piloting provides a new thinking for enhancing vehicle active safety and relieving driver's workload. However, the preview pattern divergence between driver and steering assistance system (SAS) may give rise to serious steering conflicts, which reduces driver's faith in SAS, cause him/her to turn off it, and even result in fatal traffic accidents. Focusing on mimicking the driver's steering behaviour, this paper proposes a human-like steering assistance scheme by adaptively varying the preview time and assistance gain of the SAS controller. Driver's preview behaviour of the road curvature is numerically identified using simulator tests along a multi-curves road, based on which a nominal preview-scheduled driver model is established. Based on the nominal driver model, a co-piloting system featuring human-like preview behaviour and a time-varying assistance penalty is proposed to formulate the steering torque-overlay dynamics of both agents in path-tracking. Next, a linear parameter varying (LPV)/H∞ controller satisfying pole placement is integrated to ensure the robustness and stability within the entire parameter space. This enables the steering assistance scheme to have a scheduled preview pattern like a human driver. Results of driving simulator experiment indicate that the proposed steering assistance scheme is able to mitigate driver-SAS conflicts and improve driver's path-tracking performance. [ABSTRACT FROM AUTHOR]

The control of an automotive suspension system by means of a hydraulic actuator is a complex nonlinear control problem. In this work, a linear parameter varying (LPV) model is proposed to reduce the complexity of the system while preserving the nonlinear behavior. In terms of control, a dual controller consisting of a model predictive control (MPC) and a Linear Quadratic Regulator (LQR) is implemented. To ensure stability, quadratic stability conditions are imposed in terms of Linear Matrix Inequalities (LMI). Simulation results for quarter-car model over several disturbances are tested in both frequency and time domain to show the effectiveness of the proposed algorithm.

Vehicle rollover is a very serious problem when considering the safety of heavy vehicles, which can result in large financial and environmental consequences. This paper investigates the interest of a Linear Parameter Varying (LPV) controller for an active anti-roll bar system of single unit heavy vehicles, in order to enhance roll stability. We propose a parameter dependent H∞/LPV controller with weighting functions, scheduled by the forward velocity (the varying parameter of the vehicle LPV model) and by the normalized load transfers at the two axles (part of the parameter dependent weighting functions) providing an on-line performance adaptation to the vehicle rollover risk. The effectiveness of the proposed controller is validated by using the TruckSim® simulation software with two different types of heavy vehicle: a fully loaded bus and a truck. The simulation results, in the frequency and time domains, show that the proposed strategy drastically improves the vehicle roll stability when compared with a H∞/LTI controller, a fixed weighting functions H∞/LPV controller and a passive anti-roll bar system.

Summary: This paper studies zonotopic fault detection observer design for a class of linear parameter‐varying descriptor systems. The disturbance and measurement noise are unknown but can be bounded by zonotopes. A zonotopic method is presented to estimate the envelope of residual. To attenuate the effect of disturbance and measurement noise, a zonotopic size optimization criterion is implemented based on P‐radius. To improve the fault detection performance, a finite‐frequency H_ index is introduced based on the generalized Kalman‐Yakubovich‐Popov lemma. Simulation examples of a mass‐spring and a direct‐current motor are utilized to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the linear parameter varying (LPV) reliable non-fragile control for the hypersonic vehicle (HV) is studied in case of disturbance and controller gain variations. Due to the dramatic and complex change of the HV longitudinal dynamics, a polytopic LPV model is constructed for the HV system stability analysis and controller design in a large flight envelope. Then, a disturbance observer (DOB)-based non-fragile controller for HV system with disturbance and unknown controller uncertainty is designed to guarantee the closed-loop stability and control performance under an adequate level of reliability, which is formed with two parts. One part is a DOB to compensate the uncertain dynamics and disturbance. The other is a robust non-fragile controller, which is designed based on a novel robust reliability method to deal with controller uncertainties, and obtained by carrying out reliability-based linear matrix inequality optimization. The presented controller for HV can provide the robustness as well as an excellent performance under the condition that the prescribed reliability degree is satisfied. Finally, numerical simulation for an HV demonstrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Dissimilar redundant actuation systems (DRAS) are in practice in advanced aircraft in order to increase reliability and to resolve the actuator failure issue due to common cause. This paper addresses the problem of force fighting that occurs due to dynamics mismatch of two dissimilar actuators in DRAS and provides a method to achieve precise tracking of aircraft control surface. The nonlinear system dynamics are first transformed into linear parameter varying (LPV) form using recursive least square (RLS) method. The LPV observer based controller is then designed to synchronize the positions of dissimilar actuators in DRAS and to drive the control surface smoothly. By applying linear matrix inequalities (LMIs), parameter dependent Lyapunov function (PDLF) is obtained to achieve global stability and to compute the controller and observer gains. To test controller according to real flight condition, an external disturbance signal that acts as air load is applied at the control surface input. Several simulations on the nonlinear system validate the dominant performance of proposed controller as compared to the existing methods in literature. [ABSTRACT FROM AUTHOR]

Effective flight boundary control will be helpful to prevent loss-of-control in flight. A model reference adaptive control method based on linear parameter varying (LPV) model is studied for flight boundary protection. An affine parameter dependent LPV flight dynamics model (FDM) is built up by function substitution method. The mismatch between LPV and full-quality nonlinear FDM can be decreased with the variation of scheduling parameters. The problem of real-time flight boundary protection can be solved by constrained generalized predictive control. Furthermore, a numerical algorithm is designed to protect the boundary of control surface deflection and its increments, flight states, etc. The simulation results show that the instantaneous dynamics response of LPV model can approximate that of nonlinear FDM. Flight boundary protection can be implemented effectively by constrained predictive control numerical algorithm. [ABSTRACT FROM AUTHOR]

Altogether with extent of automotive role in today's life, governments are approving stringent laws to lower the permitted level of automotive emissions. An important specie of automotive engines emissions is NOx. Different methods are developed to decrease the level of NOx generation; one of which is exhaust gas recirculation (EGR). Since the performance of EGR is strictly depended on burned gas fraction (BGF) in inlet manifold, the precise control of BGF is of importance. Unfortunately, due to inlet manifold temperature, no economical sensor is available to measure the BGF; therefore, estimators are employed instead. In this paper, a stable observer is designed for estimation of air fraction in inlet manifold. The governing equations are in the form of linear parameter varying (LPV). Since the LPV parameters are not directly measured, a hierarchical estimator structure is developed. Lyapunov theory is employed to design the higher level estimator, while high gain estimators and open-loop estimators are developed for estimation of the lower level parameters. Experimental test results show that the higher level estimator is able to estimate the BGF with high accuracy in both transient and steady states. Furthermore, it is shown that BGF estimator is more sensitive to aspirated gas flow estimation rather than exhaust temperature and pressure. [ABSTRACT FROM AUTHOR]

• Ensuring operation conditions on robot controllers is paramount in anthropic domains. • For LfD-based VICs, only stability has been addressed by limiting term modulations. • Presented method ensures more conditions for LfD-based VICs by tuning its parameters. • Conditions regard safety and performance, in terms of LMI constraints and heuristics. • The most suitable set of parameters is found off-line through an automated process. • The method is validated and applied to a real case study using a KINOVA manipulator. This paper presents an approach to ensure conditions on Variable Impedance Controllers through the off-line tuning of the parameters involved in its description. In particular, we prove its application to term modulations defined by a Learning from Demonstration technique. This is performed through the assessment of conditions regarding safety and performance, which encompass heuristics and constraints in the form of Linear Matrix Inequalities. Latter ones allow to define a convex optimisation problem to analyse their fulfilment, and require a polytopic description of the VIC, in this case, obtained from its formulation as a discrete-time Linear Parameter Varying system. With respect to the current state-of-art, this approach only limits the term definition obtained by the Learning from Demonstration technique to be continuous and function of exogenous signals, i.e. external variables to the robot. Therefore, using a solution-search method, the most suitable set of parameters according to assessment criteria can be obtained. Using a 7-DoF Kinova Gen3 manipulator, validation and comparison against solutions with relaxed conditions are performed. The method is applied to generate Variable Impedance Controllers for a pulley belt looping task, inspired by the Assembly Challenge for Industrial Robotics in World Robot Summit 2018, to reduce the exerted force with respect to a standard (constant) Impedance Controller. These controllers fulfil a set of safety constraints, namely stability, bounds on task variables and maximum response overshooting; and their performance is determined by the User Preference heuristic, which allows to intuitively define the desired compliant behaviour along the task. In the context of the task, this is used to generate new controllers for one-off modifications of the nominal belt looping task setup without new demonstrations. [ABSTRACT FROM AUTHOR]

In this paper, we present a practical approach to address the vehicle lateral control problem. The proposed method can overcome practical problems associated with vehicle lane changes on highways. The vehicle state with respect to the road, which is called lateral offset, jumps in camera vision sensors when the vehicle changes lanes. Thus, in this study, we solve the state jump problem by translating it into a new domain called the cylinder domain. In addition, we proposed the design of a parameter-varying controller to overcome the nonlinear term of vehicle dynamics by considering it as a varying parameter. The proposed method does not consider the lateral offset jump when changing lanes. Furthermore, its significant advantage in terms of computation time makes it suitable for implementation in low-cost electronic control units (ECUs). The proposed algorithm is validated using MATLAB/Simulink with the vehicle dynamics analysis program CarSim.

By adopting the linear parameter varying (LPV) control technique, this paper presents an active fault-tolerant control (FTC) strategy with application to unmanned quadrotor helicopter (UQH). Adverse effects from payload grasping and dropping caused variations of system dynamics as well as battery drainage induced loss of actuator effectiveness are expected to be counteracted in this study. First, the UQH is manipulated by a well designed baseline controller. In the presence of either payload grasping/dropping or battery drainage, their magnitudes are then obtained from a LPV-based fault detection and diagnosis (FDD) scheme. Next, based on the estimated values, a fault-tolerant tracking controller, which is linear parameter dependent, is devised in a convex polytopic LPV representation schedules to a new status in corresponding to the system variations, so that the negative impacts can be compensated. The parameters that change with system variations are specified as scheduling scalars for the LPV controller, while the ultimate control rule is obtainable by employing a set of well-established linear matrix inequality (LMI) conditions. Finally, both numerical simulations on a nonlinear model of UQH and experiments on a real UQH are conducted so as to testify the effectiveness of proposed methodology. [ABSTRACT FROM AUTHOR]

Dynamic joint stiffness is a dynamic, nonlinear relationship between the position of a joint and the torque acting about it, which can be used to describe the biomechanics of the joint and associated limb(s). This paper models and quantifies changes in ankle dynamic stiffness and its individual elements, intrinsic and reflex stiffness, in healthy human subjects during isometric, time-varying (TV) contractions of the ankle plantarflexor muscles. A subspace, linear parameter varying, parallel-cascade (LPV-PC) algorithm was used to identify the model from measured input position perturbations and output torque data using voluntary torque as the LPV scheduling variable (SV). Monte-Carlo simulations demonstrated that the algorithm is accurate, precise, and robust to colored measurement noise. The algorithm was then used to examine stiffness changes associated with TV isometric contractions. The SV was estimated from the Soleus EMG using a Hammerstein model of EMG-torque dynamics identified from unperturbed trials. The LPV-PC algorithm identified (i) a non-parametric LPV impulse response function (LPV IRF) for intrinsic stiffness and (ii) a LPV-Hammerstein model for reflex stiffness consisting of a LPV static nonlinearity followed by a time-invariant state-space model of reflex dynamics. The results demonstrated that: (a) intrinsic stiffness, in particular ankle elasticity, increased significantly and monotonically with activation level; (b) the gain of the reflex pathway increased from rest to around 10–20% of subject's MVC and then declined; and (c) the reflex dynamics were second order. These findings suggest that in healthy human ankle, reflex stiffness contributes most at low muscle contraction levels, whereas, intrinsic contributions monotonically increase with activation level.

This paper proposes a health-aware control approach for drinking water transport networks. This approach is based on an economic model predictive control (MPC) that considers an additional goal with the aim of extending the components and system reliability. The components and system reliability are incorporated into the MPC model using a Linear Parameter Varying (LPV) modeling approach. The MPC controller uses additionally an economic objective function that determines the optimal filling/emptying sequence of the tanks considering that electricity price varies between day and night and that the demand also follows a 24-h repetitive pattern. The proposed LPV-MPC control approach allows considering the model nonlinearities by embedding them in the parameters. The values of these varying parameters are updated at each iteration taking into account the new values of the scheduling variables. In this way, the optimization problem associated with the MPC problem is solved by means of Quadratic Programming (QP) to avoid the use of nonlinear programming. This iterative approach reduces the computational load compared to the solution of a nonlinear optimization problem. A case study based on the Barcelona water transport network is used for assessing the proposed approach performance.