Road Disturbance Research Articles

Neuroadaptive deferred full-state constraints control without feasibility conditions for uncertain nonlinear EASSs

This article studies the neuroadaptive full-state constraints control problem for a class of electromagnetic active suspension systems (EASSs). First, the original constraint system with arbitrary initial values is transformed into a new constraint system with zero initial values by using the shift function method. Then, a new kind of cotangent-type nonlinear state-dependent transition function is constructed to solve the asymmetric time-varying full-state constraints control problem, which eliminates the limitation that the virtual controller needs to satisfy the feasibility conditions in the previous full-state constraints control based on Barrier Lyapunov Function (BLF) and Integral BLF. Furthermore, the neural networks (NNs) are used as nonlinear function approximators to deal with the unknown nonlinear dynamics of EASSs, a neuroadaptive full-state constraints control design method is proposed under the Backstepping recursive design framework. Finally, the effectiveness of the proposed method is verified by a simulation of EASSs with road disturbances.

An MPC-LQR-LPV Controller with Quadratic Stability Conditions for a Nonlinear Half-Car Active Suspension System with Electro-Hydraulic Actuators

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.

Applying a systematic conservation planning tool and ecological risk index for spatial prioritization and optimization of protected area networks in Iran

One of the key purposes of conservation selection strategies is to design a network of sites to support relevant biodiversity components and, therefore, decrease the risk of populations becoming isolated. To this end, it is important to be aware of the habitat locations of the target species and the threats of human activities, in order to identify areas of a high conservation priority. This paper takes the Chaharmahal and Bakhtiari province (Iran) as a case study, to highlight a network optimization for six target species of conservation concern, including the Persian leopard, Panthera pardus Pocock, wild sheep, Ovis orientalis Gmelin and wild goat, Capra aegagrus Erxleben. To run the optimization, we first generated the following input data: we modelled suitable habitats, using the InVEST model (Integrated Valuation of Environmental Services and Tradeoffs) and simulated the ecological impact of road networks (Spatial Road Disturbance Index (SPROADI), Kernel Density Estimation (KDE) and the Landscape Ecological Risk Index (ERI)). A visual inspection of the input data revealed that a large percentage of the study area constitutes a suitable habitat for the target species, however, the disturbances caused by the road demonstrate that the central and north-eastern regions of the study area are significantly affected. Indeed, approximately 10% and 25% of the study area are in the high and medium risk categories, respectively. Optimization using Marxan, shows that the north-western and southern regions of the study area should be given high conservation priority, necessary for an efficient conservation network. Habitats located in the north-central region should act as stepping-stone areas or corridors between the isolated regions in the north-east and the well-connected areas in the north-west and south. Overall, the findings of the present study show that the current network of protected areas is not contradictory to that suggested by Marxan, but has deficiencies in terms of size and stepping-stones.

Symmetry Control of Comfortable Vehicle Suspension Based on H∞

Suspension is an important part of intelligent and safe transportation; it is the balance point between the comfort and handling stability of a vehicle under intelligent traffic conditions. In this study, a control method of left-right symmetry of air suspension based on H∞ theory was proposed, which was verified under intelligent traffic conditions. First, the control stability caused by the active suspension control system running on uneven roads needs to be ensured. To address this issue, a 1/4 vehicle active suspension model was established, and the vertical acceleration of the vehicle body was applied as the main index of ride comfort. H∞ performance constraint output indicators of the controller contained the tire dynamic load, suspension dynamic stroke, and actuator control force limit. Based on the Lyapunov stability theory, an output feedback control law with H∞-guaranteed performance was proposed to constrain multiple targets. This way, the control problem was transformed into a solution to the Riccati equation. The simulation results showed that when dealing with general road disturbances, the proposed control strategy can reduce the vehicle body acceleration by about 20% and meet the requirements of an ultimate suspension dynamic deflection of 0.08 m and a dynamic tire load of 1500 N. Using this symmetrical control method can significantly improve the ride comfort and driving stability of a vehicle under intelligent traffic conditions.

Design and Performance Assessment of Adaptive Harmonic Control for a Half‐Car Active Suspension System

The vehicle suspension system is represented by a complex group of components which connect the wheels to the frame or body. Its primary function is to reduce or absorb various vehicle vibrations generated by road disturbances, providing comfort and safety for passengers. Most modern vehicles have independent, active, or semiactive front and rear suspensions which allow the use of electronic actuation. For this reason, automotive engineers conduct research on the active suspension model to determine the most suitable control algorithm. Three active suspension models are intensely used within simulations: the quarter‐car, the half‐car, and the full‐car models. This paper proposes an adaptive harmonic control for a half‐car active suspension system. The mathematical model of the suspension system is implemented in the MATLAB/Simulink environment. The control approach is tested via simulation, and several comparisons with the classical proportional‐integral controller are provided. The simulation results show that the controller behaves quite similarly on the half‐car model as it did on a quarter‐car model. Additionally, an improvement to the harmonic control algorithm has been accomplished.

Non Linear Parameter Varying Observer based on Descriptor Modeling for Damper Fault Estimation

This paper proposes an H∞ Non Linear Parameter Varying (NLPV) observer for fault estimation in semi-active Electro-Rheological (ER) suspensions. The damper fault (a loss-of-efficiency factor) is modeled as a lost force of unknown/free dynamics to be estimated. Thanks to the parameter-dependent descriptor-form system modeling, there is no assumption made on the fault dynamics, thus making this method applicable to all considered types of damper faults. The nonlinearity in the damper model is bounded by its Lipschitz property, while the road disturbance and the measurement noise are handled using the H∞ condition. The observer is parameterized and then designed by solving Linear Matrix Inequalities (LMIs) and is implemented in a polytopic gain scheduling approach. Synthesis results including Bode plots and simulations illustrate the method in both the frequency and the time domains.

Quarter Car Active Suspension System Control Using Fuzzy Controller

The performance of active suspension systems is directly related to the mechanical design and control of the system. The stable operation of the controller improves driving comfort and handling. The quarter car model is frequently used in the analysis of suspension systems due to its simple structure. In this study, Matlab Simulink software was used in the modeling, control and simulation of the quarter car active suspension model. System performance was investigated for four different road profiles with PID and fuzzy logic control methods. Two of the road profiles used are in the form of impact signals consisting of pits and bumps. The other two road profiles are random road disturbances with high frequency. The effects of active and passive suspension systems on driving comfort are compared by taking into account the control methods used. As a result of the study, it has been determined that the fuzzy logic controller gives better results in pulse signals consisting of bumps and pits, and the PID controller gives better results in high-frequency random road disturbances. In addition, with the use of fuzzy logic control method in the active suspension system, a significant decrease in the actuator force has occurred. This result is very interesting in terms of minimizing energy, reducing actuator sizes and reducing costs.

An empirical approach for developing functions for the vulnerability of roads to tropical cyclones

Tropical cyclones (TCs) pose great risks to road infrastructures. However, there are very few studies available that address the vulnerability of roads to TCs; although, such studies are prerequisites for risk assessment. In this study, we used records of road damage from TC events in Hainan Province, China, to construct vulnerability models that quantify the relationship between the road damage level and different TC intensity measures. These measures include cumulative precipitation and the maximum wind speed, as well as their joint effect. We found that the derived vulnerability model of the joint effect of precipitation and wind speed outperforms models constructed with single TC intensity measures. The derived functions show a good fit to the observed data and can provide an accurate estimate of road damage from TCs, as validated by historical damage records. Based on the derived vulnerability curves, we further assessed the risk of road assets in Hainan Province to TCs. The results can provide guidance to road administrations for prioritizing the maintenance and reinforcement of roads, preparing for the possible adverse impacts of TCs on roads, and facilitating decision-making to reduce road disturbances.

PI Observer-Based Fault-Tolerant Tracking Controller for Automobile Active Suspensions

The objective of this paper is to retain the desirable dynamics performances and improve ride quality for active suspensions with the actuator faults and unknown road disturbances. To that end, a novel proportional-integral observer (PIO)-based fault-tolerant tracking controller (FTTC) design is proposed for automobile active suspensions (ASSs) encountered with actuator faults and parameter uncertainties. First, the Takagi-Sugeno (T-S) fuzzy model approach is adopted to establish T-S representation of the faulty ASSs by describing vehicle dynamics system as the weighed summation of a common linear system. Afterwards, a nominal robust H∞ output feedback controller is developed to enhance the suspension performances under fault-free mode, whose output response indicators are taken as the prescribed reference trajectories. Then, a PIO-based fault estimator is designed to predict both the system states and the unmeasurable actuator faults, synchronously. On basis of this designed observer, the expected PIO-FTTC is synthesized to track the prescribed reference trajectories, and further to make up for the system performance deteriorations aroused by the actuator faults. Finally, a simulative investigation demonstrates the effectiveness and feasibility of the proposed PIO-FTTC compared to existing control approach.

Optimisation of robust and LQR control parameters for discrete car model using genetic algorithm

Active suspension systems are the main feature in modern cars and will be the main stream in the future. The optimisation of their performances requires many studies about the different types of controllers. Robust H-infinity and linear quadratic regulator (LQR) controllers are used to control the suspension system and to reduce the vibrations in the car and to improve handling. A half car discrete model is considered in this research to study the effects on passengers owing to different road profiles. The weights of robust H-infinity and LQR controllers are obtained using genetic algorithm on a half car model with two different types of common road disturbance. The design parameters of both the active controllers vary with various road profiles. This proves that particular design parameters in robust and LQR controller do not have the ability to adapt to the variations in road surface. Furthermore, active controllers significantly improve the performance of the system in all aspects when compared to passive systems.

Embedded Real-Time Speed Forecasting for Electric Vehicles: A Case Study on RSK Urban Roads

During the past ten years, worldwide efforts have been pursuing an ambitious policy of sustainable development, particularly in the energy sector. This ambition was revealed by noticeable progress in the deployment and development of infrastructures for the production of renewable electrical energy. These infrastructures combined with the deployment of wired and wireless communications could support research actions in the field of connected electro-mobility. Also, this progress was manifested by the development of electric vehicles (EV), penetrating our transportation roads more and more. They are considered among the potential solutions, which are envisaged to further reduce road transport’s greenhouse gas emissions, relying on low-carbon energy production. However, the uncertainty caused by both external road disturbances and drivers’ behavior could influence the prediction of upcoming power demands. These latter are mainly affected by the unpredictability of the electric vehicles’ speed on transportation roads. In this work, we introduce an energy management platform, which interfaces with in-vehicle components, using a developed embedded system, and external services, using IoT and big data technologies, for efficient battery power use. The platform was deployed in real-setting scenarios and tested for EV speed prediction. In fact, we have used driving data, which have been collected on Rabat-Salé-Kénitra (RSK) urban roads by our Twizy EV. A multivariate Long Short Term Memory (LSTM) algorithm was developed and deployed for speed forecasting. The effectiveness of LSTM was evaluated against well-known algorithms: Auto Regressive Integrated Moving Average (ARIMA), Convolutional Neural Network (CNN) and Convolutional LSTM (ConvLSTM). Experiments have been conducted using two approaches; the whole trajectory dataset and segmented trajectory datasets to train the models. The experimentation results show that LSTM outperforms the other used algorithms in terms of forecasting the speed, especially when using the trajectory segmentation approach.

Active Fault-Tolerant Control for Discrete Vehicle Active Suspension Via Reduced-Order Observer

In this article, the fault-tolerant control (FTC) problem of vehicle active suspension is concerned in the discrete-time domain, in which the road disturbances and faults in actuator and measurement are considered. The main contribution consists of proposing an active physically realizable fault-tolerant controller based on a reduced-order observer, which makes up an optimal vibration control component and an event-triggered FTC component. More specifically, by discussing a discrete vehicle active suspension subject to road disturbances generated from the output of a designed exosystem, the optimal vibration control component is derived from maximum principle to offset the inevitable vibrations. Meanwhile, based on the real-time system output of vehicle suspension rather than residual error, a reduced-order observer is proposed to cover the physically unrealizable problem for the designed optimal vibration control component. After that, an event-triggered FTC component and an event-triggered restructured system output are designed to compensate the faults in actuator and measurement, respectively. Finally, extensive experiments are conduced to the control performance of vehicle active suspension under the proposed controller, and confirm its effectiveness and superiority over other control schemes.

Simulation of a Self-Balancing Platform on the Mobile Car


 In the last years, the self-balancing platform has become one of the most common candidates to use in many applications such as flight, biomedical fields, industry. This paper introduced the simulated model of a proposed self-balancing platform that described the self–balancing attitude in (X-axis, Y-axis, or both axis) under the influence of road disturbance. To simulate the self-balanced platform's performance during the tilt, an integration between Solidworks, Simscape, and Simulink toolboxes in MATLAB was used. The platform's dynamic model was drawn in SolidWorks and exported as a STEP file used in the Simscape Multibody environment. The system is controlled using the proportional-integral-derivative (PID) controller to maintain the platform leveled and compensate for any road disturbances. Several road disturbances scenarios were designed in the x-axis, y-axis, or both axis (the pitch and roll angles) to examine the controller effectiveness. The simulation results indicate that that the platform completed self-balancing under the effect of disturbance (10° and -10°) on the X-axis, Y-axis, and both axes in less than two milliseconds. Therefore, a proposed self-balancing platform's simulated model has a high self-balancing accuracy and meets operational requirements despite its simple design.

Combined Input–Output Finite-time Stability with H∞ Static Output-feedback Control Approach for Active Suspension

Combined input–output finite-time stability condition with static output-feedback H ∞ control is proposed in this work to effectively attenuate the short-duration road disturbance. The control problem is formulated based on the pre-defined condition of input–output finite-time stability with H ∞ bound along with pre-established quarter-car active suspension model. Then, a feasible static output-feedback controller is designed via variable substitution approach and linear matrix inequalities. Output-feedback control’s initial infeasibility issue is solved by state-feedback technique. The controller is specifically designed for the system which exposed with short-duration exogenous disturbances hence, this work prefers finite-time approach instead Lyapunov asymptotic stability condition. To enumerate the significance of the proposed controller numerical examples are presented and its response is effectively analyzed in distinct domains (time and frequency). The theoretical results denote that the combined input–output finite-time stability condition with static output-feedback H ∞ control can enhance the vehicle performance better when compared with passive and conventional static output-feedback control scheme. Additionally, the controller robustness is verified.

Semi active damping force estimation using [formula omitted] estimators with different sensing configurations

Semi-active suspension systems have become a widespread tool to improve the handling and comfort of vehicles. These systems require adjustable dampers as well as supplementary sensing elements. In addition to displacements and accelerations, some of the best performing approaches require knowledge of the semi active damper force. Since this variable can be difficult and expensive to measure, several estimation methods have been proposed. In this article, two Linear-Parameter-VaryingH∞ (LPV-H∞) filters are developed to estimate the Semi-Active (SA) damper force, considering two different combinations of sensing elements: the first configuration is more expensive, but potentially more accurate and reliable; whereas the second configuration is cheaper and arguably less reliable. Thanks to the use of LPV-H∞ theory, both filters are designed to account for the main nonlinear phenomena of SA dampers (i.e. saturation, hysteresis, etc.), as well as being quadratically stable, robust to the road disturbances and optimized to reduce the estimation error in a specified frequency band. Simulations and experimental data are used to assess the proposed estimators as well as a typical inverse-dynamics estimation approach. The results show that while both of the proposed estimators yield a good degree of accuracy, there are indeed fundamental differences depending on the available sensing elements; a conclusion which could be crucial to appropriately define the instrumentation of semi-active suspension systems.

Long-term impacts of road disturbance on old-growth coast redwood forests

In forest ecosystems, road expansions and other landcover changes create abrupt artificial boundaries that alter the microclimate along the forest edge, potentially impacting growth and physiology of bordering trees. To understand how previous landcover changes and road installations have affected coast redwoods (Sequoia sempervirens), we used dendroecological methods to contrast tree-ring growth patterns and stable isotopes (Δ13C) of redwoods before and after a known disturbance event, the expansion of Highway 101 in the 1950s, that bisected several old-growth stands of Humboldt Redwoods State Park. Increment cores were extracted from redwoods in old-growth stands that bordered the highway and other forest edges (secondary forests and agricultural fields) as well as two control areas. Disturbance detection methods and dendroclimatic modeling were then employed to determine whether the expansion led to growth suppression and elevated water stress. Tree-ring growth data indicated that the construction of Highway 101 disproportionately impacted the growth of trees that were within 30 m of the highway and that these effects were particularly elevated in trees that currently exhibit crown dieback. Similarly, climatic modeling of tree-ring Δ13C data indicated that highway adjacent trees also experienced elevated water stress for several decades following the construction of the highway. By analyzing tree-ring data of redwoods within Humboldt Redwoods State Park, here we provide critical insight to the temporal and spatial implications of the habitat fragmentation and road installation that has been nearly ubiquitous in the redwood distribution since Euro-American settlement.

Unscented Kalman-filter to manage the handling-comfort trade-off of quarter-of-vehicle

This paper investigates managing the comfort-handling trade-off of a quarter car suspension system using a Kalman filter. Using the unscented Kalman filter, the adapted feedback input signal is extracted based on the vertical acceleration signals of the chassis and wheel. Considering the chassis acceleration signal as the primary feedback to maintain a required comfort level, it is continuously adapted to keep an acceptable level of road handling. Compared with the traditional methods, which rely on the combination of the two modes of comfort and handling through an intermediate variable to manage the contradiction, this method focuses on comfort and improves the process of the road handling automatically. The proposed strategy is evaluated using simulation in MATLAB and the results show the feasibility of this method in managing the handling-comfort trade-off. In addition, mathematical relationships that allow this control strategy to be derived were shown. Moreover, the effects of road disturbances amplitudes and road quality on the performance of the proposed control strategy were investigated. Furthermore, the performance of the proposed method is compared with that of the hybrid-hook and the results show the superiority of the proposed algorithm.

Static output feedback stabilization of T-S fuzzy active suspension systems

This paper investigates, a new methodology for non-linear active suspension systems (ASS) with static output feedback (SOF) control. The nonlinear ASS is represented by a Takagi–Sugeno fuzzy control ASS in continuous time. A SOF control presented by a family of linear matrix inequalities (LMIs) guarantees the asymptotic stability and ensures certain an H∞ performance level. This methodology motivated this research, where in the context of active suspension (AS) systems, the guaranteed performance correspond to ride comfort in the presence of road disturbances. Thus, a controller is developed for a quarter-car model with active suspension. The Simulated results with root-mean-square (RMS) values illustrate the effectiveness of the proposed approach.

Decentralized Dynamic Event-Triggered Communication and Active Suspension Control of In-Wheel Motor Driven Electric Vehicles with Dynamic Damping

This paper addresses the co-design problem of decentralized dynamic event-triggered communication and active suspension control for an in-wheel motor driven electric vehicle equipped with a dynamic damper. The main objective is to simultaneously improve the desired suspension performance caused by various road disturbances and alleviate the network resource utilization for the concerned in-vehicle networked suspension system. First, a T-S fuzzy active suspension model of an electric vehicle under dynamic damping is established. Second, a novel decentralized dynamic event-triggered communication mechanism is developed to regulate each sensor's data transmissions such that sampled data packets on each sensor are scheduled in an independent manner. In contrast to the traditional static triggering mechanisms, a key feature of the proposed mechanism is that the threshold parameter in the event trigger is adjusted adaptively over time to reduce the network resources occupancy. Third, co-design criteria for the desired event-triggered fuzzy controller and dynamic triggering mechanisms are derived. Finally, comprehensive comparative simulation studies of a 3-degrees-of-freedom quarter suspension model are provided under both bump road disturbance and ISO-2631 classified random road disturbance to validate the effectiveness of the proposed co-design approach. It is shown that ride comfort can be greatly improved in either road disturbance case and the suspension deflection, dynamic tyre load and actuator control input are all kept below the prescribed maximum allowable limits, while simultaneously maintaining desirable communication efficiency.

Linear Quadratic Regulator Based Control Device for Active Suspension System with Enhanced Vehicle Ride Comfort


 
 
 The suspension system is required in an automobile in order to absorb shock that comes from various type of disturbances such as irregular road profile, engine vibrations and wheel. Besides, the suspension plays an important part in enhancing the passenger ride comfort. The suspension will make sure that the tire is always contacted with the road for a better grip and braking. Conventionally, passive suspension has been used in car manufacturing that leads to huge vibrations that affect the ride quality. This is because ride comfort of passengers gets affected by overshoot and settling time of vehicle under vibration. Therefore, a good controller design is required to minimize the vibrations. In this research, an active suspension of quarter car model that considers only vertical movement is utilized in the suspension system. This paper presents a Linear Quadratic Regulator (LQR) method to enhance the vehicle ride comfort towards the vibration of the suspension system. The control design approach is then compared with the classical control which is the Proportional Integral Derivative (PID) that is set as a benchmark control. The results for both controllers are evaluated through simulations in MATLAB and Simulink Software. Other than using the passenger vehicle parameters, the parameters of bus are also tested into the system to investigate the vehicle performance by taking the bumps and road pavements as road disturbances. The results obtained from the simulations show that the responses of the quadratic based approach give the significant improvement in minimizing the vibration and fast settling time compared to passive and PID control.