Preview Control Research Articles

Preview control for linear parameter‐varying discrete‐time systems using a linear fractional representation

AbstractIn order to create a mixed tracking controller for discrete‐time linear parameter‐varying (LPV) systems, this work introduces a unique control strategy employing a linear fractional representation (LFR). Under the premise that the reference signal can be previewable, a design strategy for a controller with preview compensations is suggested. First, a preview tracking control issue is changed into a regulator problem by adding two additional linked auxiliary parameters into the initial system state/input, which further creates more enhanced error systems incorporating future information of reference signals. The suggested conditions then depend on utilizing slack variables with decision matrices related to the LFR approach in order to give innovative preview controller designs. We also construct reduced conservative synthesis situations by taking into account parameter‐dependent Lyapunov functions and full‐block multipliers. In order to create state‐feedback and output‐feedback preview tracking controllers, design requirements are characterized as linear matrix inequalities. Two simulation studies are completed as the last step to highlight how successful the suggested control strategy is.

Vehicle posture wheelbase preview control of in-wheel motors drive intelligent vehicle on potholed road

The in-wheel motors drive intelligent vehicle (IMDIV) has a strong ability of dynamic control, which makes it be a major development path of intelligent vehicles in the future. However, its stability is easily deteriorated by the impact of the road when driving on potholed road. To tackle this issue, a vehicle posture control method based on road feedback and elevation recognition is proposed. Firstly, an elevation recognition algorithm of adaptive Kalman filter (AKF) based on vehicle dynamic response is proposed after considering the flooded road and the system noise uncertainty. Secondly, a vehicle posture controller of fuzzy active disturbance rejection control (FADRC) is designed, with the function of dynamically adjusting the active disturbance rejection control (ADRC) parameters. It is designed to implement front suspension feedback control and rear suspension wheelbase preview control. At last, the road elevation recognition algorithm and vehicle posture control method are proved by Simulink simulation and off-line hardware in the loop test. The results revealed that the AKF can effectively recognize both white noise pavement and potholed road, and the recognition accuracy is greater than 90%. The FADRC controller realizes accurate front suspension feedback control and rear suspension wheelbase preview control by adjusting the real-time online parameters of ADRC. The proposed vehicle posture control method can effectively control the pitch and roll motion, which significantly improves the stability of IMDIV on potholed road. This study can serve as a reference for the posture stability control of IMDIV on potholed road.

A simulation study on visual preview control of vehicle magnetorheological suspension

Abstract At present, the boom in intelligent vehicles, especially automotives, has brought magnetorheological (MR hereinafter) suspensions into further focus. Generally, traditional MR suspensions work with acceleration sensors to respond with a non-negligible time delay issue due to the inductive property of MR damper (MRD hereinafter), which hinders their full performance especially at high-speed driving with a powerless “blind zone”. To address this issue, this study proposes a simulation study on visual preview control for vehicle MR suspension based on MPC (Model Predictive Control), since the road preview is able to compensate the time delay due to the future road information perceived in advance. First, a camera with a computer vision algorithm is employed to preview road information of target type, target size and distance to wheels. Besides, the error correction of radial and tangential distortions of camera lens are investigated, and the trajectory of wheels are predicted by a cycloid model because MR suspension only needs to respond to the road targets that the wheels will pass over. Further, the area in front of the vehicle is divided into a recognition zone and action zone, and the road targets within action zone (<1.5 m) are calculated more precisely for a prepared immediate response from the MR suspension. Then, the performance of a simulating vehicle MR suspension is investigated through a joint simulation by the automotive simulation software CarSim and the model-solving Simulink. The evident results show that the MR suspension with visual preview control performs the best in ride comfort (mean square values of sprung mass acceleration is 82% and 52% lower compared to a passive suspension and a MR suspension with skyhook control, respectively). Therefore, the visual preview control strategy of MR suspension is able to enhance the performance on the discrete road conditions.

Nash Equilibrium-Based $$ H_\infty $$ Optimal PI Preview Control for a Class of Continuous-Time Linear Systems

Nash Equilibrium-Based $$ H_\infty $$ Optimal PI Preview Control for a Class of Continuous-Time Linear Systems

Linear Modeling of Doppler Wind Lidar Systems for Gust Load Alleviation Design

Preview control using wind estimates derived from Doppler wind lidar measurements is a promising technique for designing active gust load alleviation functions. Due to the relatively high noise levels in the lidar measurements, the associated estimator must use some type of smoothing to obtain a usable estimate for gust load alleviation. In the resulting wind estimate, this results in the loss of some of the information as well as a filtered and reduced component of the measurement noise. Taking the losses and noise into account during control synthesis should help ease the tuning procedure and improve the performance and robustness of the resulting controller. This paper proposes a method based on a maximum a posteriori interpretation of the wind estimation problem to consistently produce linear filters that closely approximate the behavior of the complete measurement chain (sensor and wind estimator) and that can be integrated into a linear control synthesis framework. Its characteristics are shown to closely match those of the real estimator over a range of types of turbulence using a standard set of system parameters.

Preview Control for Cyber–Physical Systems under Periodic Denial-of-Service Attacks

In this paper, the preview control problem for cyber–physical systems (CPSs) under denial-of-service (DOS) attacks is studied. First, we employ an attack-tolerant strategy to design an augmented error system (AES) for scenarios where both state and reference signal channels are subject to periodic attacks. We then discuss the stochastic stability conditions for the AES and derive the corresponding controller. Subsequently, the preview controller for the original system is developed. Finally, the effectiveness of the obtained results is demonstrated through a numerical simulation using an unmanned ground vehicle (UGV) model, indicating the practical applicability of the proposed control strategy.

H∞ preview control for a class of continuous-time system with actuator saturation

H∞ preview control for a class of continuous-time system with actuator saturation

Robust fault-tolerant preview control for automatic landing of carrier-based aircraft

PurposeThis paper aims to propose the automatic carrier landing system with the fault-tolerant ability for carrier-based aircraft in the presence of deck motion, external airwake disturbance and actuator fault, which consists of the reference trajectory generation module and flight control module.Design/methodology/approachThe longitudinal and lateral basic controllers are designed based on the optimal preview control (OPC), which can ensure favorable tracking performance and anti-disturbance ability of system. Furthermore, based on the OPC, the robust fault-tolerant preview control scheme is proposed to attenuate the impact of actuator fault on system, which ensures the safe landing of carrier-based aircraft in case of actuator failure.FindingsBoth the Lyapunov method and simulations prove that the tracking errors can converge to zero and system states can be asymptotically stable both in normal and fault operations.Originality/valueThe fault-tolerant control strategy is introduced into preview control to deal with actuator fault, which combines feedforward control based on future previewable information and feedback control based on current information to improve the system performance.

Dissipative sliding mode tracking control for discrete-time singular T-S fuzzy systems via a preview target mechanism

This study focuses on the dissipative sliding mode preview tracking control (SMPTC) of discrete-time singular T-S fuzzy systems (STSFSs) via a preview target mechanism. Due to the presence of singular matrix and input matrices with fuzzy rules in the system, the existing preview control approaches cannot be directly adopted. As a result, a novel singular augmented error system (AES) is derived based on a general method. Furthermore, the tracking problem has been translated into the stabilisation of singular AES. A linear sliding mode surface (SMS) is proposed for singular AES, and a suitably SMPTC law is presented. Sufficiency conditions for relaxation are derived to guarantee that the sliding mode dynamics is admissible with the dissipative criterion, and the sliding mode region (SMR) can be reached. The difference singular matrix is fully considered in the whole design process such that the derived conditions can be checked easily. Finally, two examples have shown the effective tracking of the original output signal of system by utilising the proposed SMPTC law.

Preview-based terrain adaptive active suspension control strategy for heavy-duty trucks

This paper proposes a preview-based active suspension control method for heavy-duty trucks, where the vehicle front wheel terrain preview information is employed to improve the performance of the rear, focusing on enhancing the handling stability and vehicle smoothness. To begin with, the vehicle front wheel preview information is introduced to the half-vehicle model, and a state quantity is employed to calculate the time lag between front and rear for predicting the control input of the rear wheel. Secondly, based on the developed model, an [Formula: see text] controller is designed with the half-vehicle model based on the current stochastic linear optimal control combined with the preview controller, which provides more stable effect than the prior controllers by merging perceived ground information. Furthermore, an established seven-degree-of-freedom vehicle suspension model is utilized for the preview-based controller to govern the kinetic behavior of the heavy-duty truck, allowing for a more thorough analysis of operation smoothness and vehicle stability. At last, the vehicle comparison simulations are carried out, which indicates that the preview-based [Formula: see text] controller designed by inspecting the terrain preview information can straighten out the smoothness and safety of the heavy-duty truck more effectively in contrast with the LQG controller.

Disturbance rejection based on discrete-time adaptive preview control for unmanned aerial vehicle shipboard landing under structure damage

This article studies the shipboard landing control problem for unmanned aerial vehicle with external disturbance and structure damage. A novel discrete-time adaptive preview architecture is presented, where the model reference adaptive control is improved by introducing the preview control method, so that both the uncertainties (caused by the airwake and structure damage) and the control lag could be well compensated simultaneously. The proposed scheme guarantees a more rapid, accurate, and robust performance. For application, the reference input signal is generated by fusing the deck motion prediction information and glide path command. The effectiveness of the algorithm is validated by a longitudinal landing simulation.

Observer-based preview control for T-S fuzzy systems

PurposeConsidering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator and preview controller, is considered to address the tracking control problem.Design/methodology/approachThe authors employ an augmentation technique to construct an augmented error system for uncertain T-S fuzzy discrete-time systems with time-varying uncertainties. Additionally, the authors obtain the corresponding linear matrix inequality (LMI) conditions for designing the preview controller.FindingsThis paper discusses the preview tracking problem for nonlinear systems. First, considering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator, and preview controller, is considered to address the tracking control problem. Then, using the fuzzy Lyapunov functional with the linear matrix inequality (LMI) technique, new sufficient conditions for the asymptotic stability of the augmented system are derived by applying the LMI technique. The preview controller and fuzzy observer can be designed in one step. Finally, a numerical example is used to illustrate the effectiveness of the results.Originality/valueAn augmented error system is successfully constructed by the state augmentation approach. A novel preview controller is designed to address the tracking control problem. The preview controller and fuzzy observer can be designed in one step.

The preview control of a corticothalamic model with disturbance

<abstract><p>Based on a neural field network model with impulsive and random disturbances, a preview control method that makes full use of known future information is proposed to reduce the static error of the target signal and the transient oscillatory behavior of the controlled system when it receives random disturbance inputs. The preview controller for epileptic seizures is constructed, and the feasibility and effectiveness of clinical single-target and multi-target stimulation in epilepsy regulation are explored from a computational perspective. In addition, a performance index function is proposed to evaluate the energy consumption of controller with and without preview under different input (target) strategies. Suggestions for different strategies are given in terms of the individualized disease environment of patients. From the perspective of seizure control effectiveness and performance consumption, the results show that the preview controller has a greater advantage. The theory of preview control is applied to the control of epileptic seizures for the first time, and the conclusions of the multifaceted study provide some references for clinical trials and controller applications.</p></abstract>

Robust Preview Control for Half‐Vehicle Active Suspension System of Hub‐Driven Electric Vehicles with Coupling Effects

The mechanical‐electrical‐magnetic coupling effects caused by the air gap deformation in in‐wheel motors exacerbate motor magnetic force oscillation, deteriorating the in‐wheel motor operation and ride comfort. In order to enhance vehicle performances, it is imperative to mitigate these coupling effects during the design of hub‐driven electric vehicle suspensions. In this paper, the analytical investigation is conducted on the coupling effects between electromagnetic excitation and transient dynamics in EVs based on the half‐vehicle model. The dynamic responses of hub‐driven EVs considering coupling effects are explored under both open‐loop and closed‐loop systems at various speeds and road conditions. Numerical simulations demonstrate that the closed‐loop system with wheelbase preview robust control exhibits superior comprehensive performance in suppressing coupling effects of the rear wheel hub motor and enhancing ride comfort compared to the system without preview control. Furthermore, as vehicle speed increases, robust preview control effectively reduces fluctuations in pitch angle acceleration, pitch angle, and rear wheel rotor eccentricity. In addition, robust preview control yields better performance in reducing vehicle body centroid acceleration and rear wheel rotor eccentricity and enhancing motor and vehicle performance with increasing road roughness. This work provides a practical vibration model and control reference for studying the dynamic characteristics of hub‐driven EVs.

Preview control of the random response of a full car vehicle model traversing a rough road

Preview control of the random response of a full car vehicle model traversing a rough road

Increased power gains from wake steering control using preview wind direction information

Abstract. Yaw controllers typically rely on measurements taken at the wind turbine, resulting in a slow reaction to wind direction changes and subsequent power losses due to misalignments. Delayed yaw action is especially problematic in wake steering operation because it can result in power losses when the yaw misalignment angle deviates from the intended one due to a changing wind direction. This study explores the use of preview wind direction information for wake steering control in a two-turbine setup with a wind speed in the partial load range. For these conditions and a simple yaw controller, results from an engineering model identify an optimum preview time of 90 s. These results are validated by forcing wind direction changes in a large-eddy simulation model. For a set of six simulations with large wind direction changes, the average power gain from wake steering increases from only 0.44 % to 1.32 %. For a second set of six simulations with smaller wind direction changes, the average power gain from wake steering increases from 1.24 % to 1.85 %. Low-frequency fluctuations are shown to have a larger impact on the performance of wake steering and the effectiveness of preview control, in particular, than high-frequency fluctuations. From these results, it is concluded that the benefit of preview wind direction control for wake steering is substantial, making it a topic worth pursuing in future work.

Feasibility and stability of preview control for 2-D discrete-time systems described by the Roesser model

Feasibility and stability of preview control for 2-D discrete-time systems described by the Roesser model

Longitudinal and Lateral Stability Control Strategies for ACC Systems of Differential Steering Electric Vehicles

To ensure lateral stability during the cruising of a differential steering vehicle (DSV), this paper presents a curving adaptive cruise control (ACC) system coordinated with a differential steering control (DSC) system, which considers both longitudinal cruising capability and lateral stability on curved roads. Firstly, a DSV dynamics model is developed and a control strategy architecture for a curving ACC system is designed. Then, the car-following control strategy for the curving ACC system is designed based on the fuzzy model predictive control (FMPC) algorithm. The strategy aims to improve the economy and balances car following, safety, comfort and economy. Moreover, fuzzy logic rules are designed to update the weight coefficients of the performance indicators in real time. Finally, the lateral stability controller is designed based on the preview algorithm and the sliding mode control (SMC) algorithm. The simulation results show that the lateral stability of the DSV during the curving cruise is realized via the control of the differential drive torque of the two front wheels. The proposed FMPC controller and SMC controller based on the preview control algorithm satisfy the performance in terms of vehicle following and lateral stable driving in the process of cruising.

Damping Magnetorheological Systems Based on Optimal Neural Networks Preview Control Integrated with New Hybrid Fuzzy Controller to Improve Ride Comfort

<div>Adaptive neural networks (ANNs) have become famous for modeling and controlling dynamic systems. However, because of their failure to precisely reflect the intricate dynamics of the system, these have limited use in practical applications and perform poorly during training and testing. This research explores novel approaches to this issue, including modifying the simple neuron unit and developing a generalized neuron (GN). The revised version of the neuron unit helps to develop the system controller, which is responsible for providing the desired control signal based on the inputs received from the dynamic responses of the vehicle suspension system. The controller is then tested and evaluated based on the performance of the magnetorheological (MR) damper for the main suspension system. These results of the tests show that the optimal preview controller designed using the GN both ∑-Π-ANN and Π-∑-ANN can accurately capture the complex dynamics of the MR damper and improve their damping characteristics compared with other methods. The seat and main suspension systems work together to provide more support and comfort for the driver and passengers. The short stroke of the MR damper is used in seat suspension as it allows for more precise control over the suspension and can provide a smoother ride. The new hybrid fuzzy type-2 (T-2) control is designed to accurately estimate the desired damping force for the seat MR damper. This system also allows for the damping force to be adjusted to meet the desired requirements of the seat MR damper. This integration of damping systems allows better control and stability of the vehicle and provides a smoother ride for drivers and passengers. Furthermore, integrating the damping systems increases the overall performance of the vehicle, making it better able to handle various road conditions.</div>

Active Suspension Robust Preview Control by Considering Actuator Delay

Active suspension systems (ASSs) are crucial for realizing the improvement demand of ride comfort in autonomous vehicles (AVs). This paper presents a novel integrated robust preview control (PC) algorithm to further improve the ride comfort by effectively handling actuator delay. An augmented ASS model is first established to consider actuator delay. Then, to obtain the time-domain feedforward disturbance, updating the local spatial road dataset from sensing systems by low-pass filtering and introducing speed for conversion. A predictive control framework that utilizes preview road disturbances is developed to achieve better ride comfort. And assuming parameterized disturbances, it is transformed into a consistent quadratic programming (QP) problem for real-time application in ASS. Furthermore, an innovative design of combined observer provides accurate and robust observations under multiple uncertainties, with which the algorithm can effectively compensate for actuator delay under different road conditions. Finally, the bounded-input bounded-output (BIBO) stability of the algorithm is well demonstrated. Simulation and bench test results indicate that the proposed algorithm can not only significantly improve road holding and ride comfort, but also realize a robust observation of the augmented ASS state. In addition, the solving time of the bench test satisfies the real-time requirements of the algorithm.