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

The application of neural networks in active suspension

This thesis considers the application of neural networks to automotive suspension systems. In particular their ability to learn non-linear feedback control relationships. The speed of processing, once trained, means that neural networks open up new opportunities and allow increased complexity in the control strategies employed. The suitability of neural networks for this task is demonstrated here using multilayer perceptron, (MLP) feed forward neural networks applied to a quarter vehicle simulation model. Initially neural networks are trained from a training data set created using a non-linear optimal control strategy, the complexity of which prohibits its direct use. They are shown to be successful in learning the relationship between the current system states and the optimal control. [Continues.

Neurofuzzy controller based full vehicle nonlinear active suspension systems

To design a robust controller for active suspension systems is very important for guaranteeing the riding comfort for passengers and road handling quality for a vehicle. In this thesis, the mathematical model of full vehicle nonlinear active suspension systems with hydraulic actuators is derived to take into account all the motions of the vehicle and the nonlinearity behaviours of the active suspension system and hydraulic actuators. Four robust control types are designed and the comparisons among the robustness of those controllers against different disturbance types are investigated to select the best controller among them. The MATLAB SIMULINK toolboxes are used to simulate the proposed controllers with the controlled model and to display the responses of the controlled model under different types of disturbance. The results show that the neurofuzzy controller is more effective and robust than the other controller types. The implementation of the neurofuzzy controller using FPGA boards has been investigated in this work. The Xilinx ISE program is employed to synthesis the VHDL codes that describe the operation of the neurofuzzy controller and to generate the configuration file used to program the FPGA. The ModelSim program is used to simulate the operation of the VHDL codes and to obtain the expected output data of the FPGA boards. To confirm that FPGA the board used as the neurofuzzy controller system operated as expected, a MATLAB script file is used to compare the set of data obtained from the ModelSim program and the set of data obtained from the MATLAB SIMULINK model. The results show that the FPGA board is effective to be used as a neurofuzzy controller for full vehicle nonlinear active suspension systems. The active suspension system has a great performance for vibration isolation. However the main drawback of the active suspension is that it is high energy consumptive. Therefore, to use this suspension system in the proposed model, this drawback should be solved. Electromagnetic actuators are used to convert the vibration energy that arises from the rough road to useful electrical energy to reduce the energy consumption by the active suspension systems. The results show that the electromagnetic devices act as a power generator, i.e. the vibration energy excited by the rough road surface has been converted to a useful electrical energy supply for the actuators. Furthermore, when the nonlinear damper models are replaced by the electromagnetic actuators, riding comfort and the road handling quality are improved. As a result, two targets have been achieved by using hydraulic actuators with electromagnetic suspension systems: increasing fuel economy and improving the vehicle performance

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

Fuzzy sliding mode controller design for semi-active seat suspension with neuro-inverse dynamics approximation for MR damper

To improve the ride comfort of car, this paper proposed a semi-active seat suspension with magneto-rheological (MR) damper and designed a new fuzzy sliding mode controller with expansion factor (FSMCEF) based on the neuro-inverse dynamics approximation of the MR damper. This FSMCEF combines the advantages of both sliding mode controller (SMC) and fuzzy controller (FC) with expansion factor (EF), and it takes an ideal skyhook model as the reference, and creates a sliding mode control law based on the errors dynamics between the seat suspension and its reference model. Further fuzzy rules are used to suppress the chattering occurred in the above sliding mode control by fuzzifying the sliding mode surface and its derivative. Moreover, in order to compute the required control current for MR damper after solving the desired control force using FSMCEF, this paper presented a BP algorithm based neural network inverse model, located between the FSMCEF and the MR damper, taking the displacement, velocity of the MR damper and the desired control force output by FSMCEF as its input, and predicting the control current required to input MR damper. The predicting error and stability of the neural network inverse model for MR is investigated by sample testing. In addition, the stability analysis of FSMCEF is also completed by under nominal system and non-nominal system with parameter uncertainty and external disturbance. The results of numerical simulations show that the vibration reduction effect of the semi-active seat is obviously improved using FSMCEF compared with using PID controller and SMC

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

Control strategies of series active variable geometry suspension for cars

This thesis develops control strategies of a new type of active suspension for high performance cars, through vehicle modelling, controller design and application, and simulation validation. The basic disciplines related to automotive suspensions are first reviewed and are followed by a brief explanation of the new Series Active Variable Geometry Suspension (SAVGS) concept which has been proposed prior to the work in this thesis. As part of the control synthesis, recent studies in suspension control approaches are intensively reviewed to identify the most suitable control approach for the single-link variant of the SAVGS. The modelling process of the high-fidelity multi-body quarter- and full- vehicle models, and the modelling of the linearised models used throughout this project are given in detail. The design of the controllers uses the linearised models, while the performance of the closed loop system is investigated by implementing the controllers to the nonlinear models. The main body of this thesis elaborates on the process of synthesising H∞ control schemes for quarter-car to full-car control. Starting by using the quarter-car single-link variant of the SAVGS, an H∞ -controlled scheme is successfully constructed, which provides optimal road disturbance and external force rejection to improve comfort and road holding in the context of high frequency dynamics. This control technique is then extended to the more complex full-car SAVGS and its control by considering the pitching and rolling motions in the context of high frequency dynamics as additional objectives. To improve the level of robustness to single-link rotations and remove the geometry nonlinearity away from the equilibrium position, an updated approach of the full-car SAVGS H∞ -controlled scheme is then developed based on a new linear equivalent hand-derived full-car model. Finally, an overall SAVGS control framework is developed, which operates by blending together the updated H∞ controller and an attitude controller, to tackle the comfort and road holding in the high frequency vehicle dynamics and chassis attitude motions in the low frequency vehicle dynamics simultaneously. In all cases, cascade inner position controllers developed prior to the work in this thesis are employed at each corner of the vehicle and combined with the control systems developed in this thesis, to ensure that none of the physical or design limitations of the actuator are violated under any circumstances.Open Acces

State Estimation and Control of Active Systems for High Performance Vehicles

In recent days, mechatronic systems are getting integrated in vehicles ever more. While stability and safety systems such as ABS, ESP have pioneered the introduction of such systems in the modern day car, the lowered cost and increased computational power of electronics along with electrification of the various components has fuelled an increase in this trend. The availability of chassis control systems onboard vehicles has been widely studied and exploited for augmenting vehicle stability. At the same time, for the context of high performance and luxury vehicles, chassis control systems offer a vast and untapped potential to improve vehicle handling and the driveability experience. As performance objectives have not been studied very well in the literature, this thesis deals with the problem of control system design for various active chassis control systems with performance as the main objective. A precursor to the control system design is having complete knowledge of the vehicle states, including those such as the vehicle sideslip angle and the vehicle mass, that cannot be measured directly. The first half of the thesis is dedicated to the development of algorithms for the estimation of these variables in a robust manner. While several estimation methods do exist in the literature, there is still some scope of research in terms of the development of estimation algorithms that have been validated on a test track with extensive experimental testing without using research grade sensors. The advantage of the presented algorithms is that they work only with CAN-BUS data coming from the standard vehicle ESP sensor cluster. The algorithms are tested rigorously under all possible conditions to guarantee robustness. The second half of the thesis deals with the design of the control objectives and controllers for the control of an active rear wheel steering system for a high performance supercar and a torque vectoring algorithm for an electric racing vehicle. With the use of an active rear wheel steering, the driver’s confidence in the vehicle improves due a reduction in the lag between the lateral acceleration and the yaw rate, which allows drivers to push the vehicle harder on a racetrack without losing confidence in it. The torque vectoring algorithm controls the motor torques to improve the tire utilisation and increases the net lateral force, which allows professional drivers to set faster lap times