Design an intelligent controller for full vehicle nonlinear active suspension systems

The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function

Active neuro-fuzzy integrated vehicle dynamics controller to improve the vehicle handling adn stability at complicated maneuvers

With the recent advancements in vehicle’s industry, driving safety in passenger vehicles is considered one of the key issues in designing any vehicle. According to other studies Electronic Stability Control (ESC) is considered to be the greatest road safety innovation since the seatbelt. Yet ESC has its drawbacks, that encouraged the development of other stability systems to correct or compensate these draw backs. But to efficiently make up for the ESC problems the integration of various control systems is needed, which is a pretty complicated task on its own. Lately, solving this stability problem became a hot research topic accompanied by the market demands for improving the available stability systems. Therefore, this thesis aims to add an innovative approach to help improve the vehicle stability. This approach consists of an intelligent algorithm that collects data about the vehicle characteristics and behavior. Then it uses an Artificial Neural Network to construct a fuzzy logic control system through learning from the optimum control values that was generated beforehand by the intelligent algorithm. This way, the proposed controller didn’t depend only on experts’ knowledge like the other controllers presented in the literature. This makes the controller more generic and reliable which is a very important aspect in designing a safety critical controller, like the presented one, where any fault in it can lead to a fatal accident. Also using the technique of using an Artificial Neural Network to construct a fuzzy logic control allows benefiting from the learning and autoautoadaption capability of neural networks and the smooth controlling performance that fuzzy logic controllers offers. Simulations results show the effectiveness of the proposed controller for improving the vehicle stability in different driving maneuvers. Where the controller’s results were compared to an uncontrolled vehicle and another vehicle controlled by a controller from the literature. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Cuando un vehículo entra en una curva a alta velocidad, la aceleración lateral producida hace que el vehículo tienda a ser más inestable y menos controlable desde el punto de vista del conductor. Esta inestabilidad, podría conllevar un comportamiento no deseado del vehículo, como el sub-viraje o el sobre-viraje, que pueden llevar al vehículo a salirse de su curso previsto o que vuelque. Además, las estadísticas concluyen que la inestabilidad lateral del vehículo es causa de accidentes de fatales consecuencias. Para hacer frente a este problema, se han propuesto varios sistemas de control, con el objetivo de generar una acción contraria que lleve de nuevo al vehículo a su curso deseado. Estos sistemas pretenden alterar de una manera u otra las fuerzas centrífugas del neumático con el fin de producir fuerzas de compensación que ayuden a mantener el control lateral del vehículo. Estos controladores presentan estrategias de control diferentes: algunos intentan afectar directamente a los ángulos de dirección de los neumáticos, otros inciden en las fuerzas longitudinales de los neumáticos para crear un momento de guiñaada alrededor del eje vertical del vehículo, y por último, otros intentan afectar a la distribución de la carga vertical entre los neumáticos. Por ello, debido a la diferencia de las características de cada uno de estos sistemas, sus capacidades de controlar también difieren. Sin desmerecer a ninguno de ellos, algunos demuestran mayor eficacia en situaciones de inestabilidad suaves; otros lo son cuando el vehículo llega a sus límites de adhesión, y los hay cuando la aceleración lateral supera un cierto valor. Por esta razón, se recomienda el uso de más de un sistema de control para beneficiarse de las ventajas de sus diferentes conceptos de control. Sin embargo, la combinación de más de un controlador de estabilidad de un vehículo, no es tarea fácil, dado que podrían producirse conflictos entre los diferentes controladores, así como la superposición de los diferentes objetivos de control. Adicionalmente, una simple combinación podría llevar a una mayor complejidad del hardware y el software usados, debido a la posible repetición de sensores y actuadores, y en consecuencia a una complejidad de cables de conexión. Por ello, se han propuesto sistemas de Dinámica de Vehículos de Control Integral (IVDC), para proporcionar una integración cuidadosamente diseñada con el objetivo de coordinar los diferentes sistemas de control del chasis. De esta manera, los conflictos de control podrían ser eliminados, y los resultados podrían reforzarse aún más mediante tal combinación. Igualmente el coste y la complejidad del sistema podrían reducirse debido al posible uso compartido de sensores, actuadores, unidades de control y cables. Recientemente, los sistemas de IVDC han sido un tema de investigación recurrente, existiendo distintos sistemas en la literatura que han intentado controlar varias combinaciones de los citados controladores utilizando una variedad de técnicas de control, muchos de los cuales han mostrado resultados prometedores en la mejora del manejo del vehículo a través de los resultados de simulaciones. No obstante, estos sistemas eran manualmente diseñados y probados en un número limitado de maniobras y condiciones. Además, han sido testados en las mismas maniobras utilizadas para su dise˜no y, por tanto, su fiabilidad y previsibilidad son cuestionables. Por otra parte, los sistemas de control de estabilidad del vehículo son considerados como sistemas de seguridad crítica, donde cualquier error podría causar un accidente fatal. De este modo, como consecuencia de la imprecisión humana, un controlador diseñado manualmente que ha sido desarrollado a través de pruebas de situación limitada, es propenso a errores que generan deficiencias en ciertas zonas de control o a inexactitudes en las decisiones de los valores de control. Por otra parte, la selección manual del margen de control dedicado a cada sub-sistema integrado no asegura la optimización de las capacidades de los controladores. Además, dado que estos controladores son diseñados por el hombre, cualquier variación de las características del modelo del vehículo, como por ejemplo algo tan sencillo como el cambio en la rigidez de la suspensión, necesitaría de intervención humana para volver a calibrar o volver a ajustar manualmente el sistema con el objetivo de adaptarse a la variación realizada. Por lo tanto, en esta tesis se intentará reemplazar el conocimiento humano y los sistemas diseñados manualmente, por un sistema automatizado e inteligente, que autoconstruye el sistema de control sin intervención humana. Este método utilizará una red neuronal inteligente que aprende los valores óptimos de control a través de un algoritmo extenso de minería de datos. En consecuencia, se autoconstruye un controlador de lógica difusa que corrige la estabilidad del vehículo a través de un sistema activo de corrección de la entrada al volante y un sistema de control de ángulo de guiñada mediante los frenos. Las entradas de control de estos sistemas serán la velocidad del ángulo de guiñada y el ángulo de deslizamiento lateral, siendo los controladores más eficaces presentados en la literatura

Grey Signal Predictor and Fuzzy Controls for Active Vehicle Suspension Systems via Lyapunov Theory

In order to investigate and decide that the vehicle asymptotic vibration stability and improved comfort, the present paper deals with a fuzzy neural network (NN) evolved bat algorithm (EBA) backstepping adaptive controller based on grey signal predictors. The Lyapunov theory and backstepping method is utilized to appraise the math nonlinearity in the active vehicle suspension as well as acquire the final simulation control law in order to track the suitable signal. The Discrete Grey Model DGM (2,1) have been thus used to acquire prospect movement of the suspension system, so that the command controller can prove the convergence and the stability of the entire formula through the Lyapunov-like lemma. The controller overspreads the application range of mechanical elastic vehicle wheel (MEVW) as well as lays a favorable theoretic foundation in adapting to new wheels

Vehicle Dynamics, Lateral Forces, Roll Angle, Tire Wear and Road Profile States Estimation - A Review

Estimation of vehicle dynamics, tire wear, and road profile are indispensable prefaces in the development of automobile manufacturing due to the growing demands for vehicle safety, stability, and intelligent control, economic and environmental protection. Thus, vehicle state estimation approaches have captured the great interest of researchers because of the intricacy of vehicle dynamics and stability control systems. Over the last few decades, great enhancement has been accomplished in the theory and experiments for the development of these estimation states. This article provides a comprehensive review of recent advances in vehicle dynamics, tire wear, and road profile estimations. Most relevant and significant models have been reviewed in relation to the vehicle dynamics, roll angle, tire wear, and road profile states. Finally, some suggestions have been pointed out for enhancing the performance of the vehicle dynamics models

State of the art of control schemes for smart systems featuring magneto-rheological materials

This review presents various control strategies for application systems utilizing smart magneto-rheological fluid (MRF) and magneto-rheological elastomers (MRE). It is well known that both MRF and MRE are actively studied and applied to many practical systems such as vehicle dampers. The mandatory requirements for successful applications of MRF and MRE include several factors: advanced material properties, optimal mechanisms, suitable modeling, and appropriate control schemes. Among these requirements, the use of an appropriate control scheme is a crucial factor since it is the final action stage of the application systems to achieve the desired output responses. There are numerous different control strategies which have been applied to many different application systems of MRF and MRE, summarized in this review. In the literature review, advantages and disadvantages of each control scheme are discussed so that potential researchers can develop more effective strategies to achieve higher control performance of many application systems utilizing magneto-rheological materials

A STUDY OF TORQUE VECTORING AND TRACTION CONTROL FOR AN ALL-WHEEL DRIVE ELECTRIC VEHICLE

Common vehicle always experience energy loss during cornering manoeuver. Thus, to ensure it did not happened especially at high speed, a study of torque vectoring and traction control need to be made since it can increase the traction control of tyres during cornering at high speed. The study of torque vectoring and traction control for an all-wheel drive electric vehicle was conducted by modelling an all-wheel drive EV in ADAMS/Car software. In addition, an optimal control algorithm will be developed for best performance to minimize energy losses using MATLAB/Simulink software. Furthermore, to prove the effectiveness of the all-wheel drive electric, the torque and traction control simulation of the all-wheel drive electric vehicle will be compared with uncontrolled electric vehicle model. According to the result, torque vectoring and traction control of in-wheel motor in all wheel drive EV can help to increase the performance of the electric vehicle during cornering manoeuver. In conclusion, this study of torque vectoring and traction control for an all-wheel drive electric vehicle will help researchers to improvise the design of the future electric vehicle in term of the vehicle performance during cornering manoeuvre

Virtual Model Of A Vehicle Adaptive Damper System

Several FCA vehicles are fitted with semi-active damper systems which modulate the level of damping implemented in the vehicle suspension system to improve both the handling and ride quality felt by vehicle’s occupants. Durability simulations are necessary to analyze a vehicle’s or a component’s structural integrity over an expected lifespan. Performing durability simulations in a virtual environment has streamlined the traditional development cycle by reducing the need to construct physical prototypes and conduct physical road or bench tests. It is essential that the vehicle is modeled as accurately as possible in the virtual environment to ensure the results are representative of real-world performance. Presently, the incorporation of a semi-active damper system in a virtual durability simulation involves the expensive and resource intensive use of empirically obtained data. The goal of this project is to improve the fidelity and efficiency of durability simulations by including the loading effects of a semi-active suspension system. To accomplish this, several semi active suspension control algorithms and practical considerations are studied. Using a car model developed in Simulink©, a neural network, clipped optimal control, and sliding mode control algorithms are developed to approximate operating characteristics of the supplier controller. The development of each controller, along with appropriate tuning and validation procedures in Simulink©, are presented. A process known as co-simulation is then used to integrate each of the chosen semi-active damper control systems into durability simulations used in vehicle development processes at FCA. Co-simulation is a process wherein the controller is executed in parallel with MSC Adams© CAE durability simulation software using Matlab©/Simulink©. The accuracy of the neural network, sliding mode controller, and clipped optimal controller are validated by correlating results to a Co-simulation carried out with a supplier controller. It is found that the performance of the neural network controller resulted in output chattering throughout the simulation. While performance is acceptable in ranges where the output data is expected to be low frequency and low amplitude, instances where this was not the case induced chattering events. These events are most likely due to the neural network receiving inputs outside of the range of data which it was trained on