Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey

Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Performance investigation of integrated model of quarter car semi-active seat suspension with human model

In this paper, an integrated model of a semi-active seat suspension with a human model over a quarter is presented. The proposed eight-degrees of freedom (8-DOF) integrated model consists of 2-DOF for the quarter car model, 2-DOF for the semi-active seat suspension and 4-DOF for the human model. A magneto-rheological (MR) damper is implemented for the seat suspension. The fuzzy logic-based self-tuning (FLST) proportional–integral–derivative (PID) controller allows to regulate the controlled force on the basis of sprung mass velocity error and its derivative as input. The controlled force is tracked by the Heaviside step function which determines the supply voltage for the MR damper. The performance of the proposed integrated model is analysed, in-terms of human head accelerations, for several road profiles and at different speeds. The performance of the semi-active seat suspension is compared with the traditional passive seat suspension to validate the effectiveness of the proposed integrated model with a semi-active seat suspension. The simulation results show that the semi-active seat suspension improves the ride comfort significantly by reducing the head acceleration effectively compared to the passive seat suspension

Control of Quarter-Car Active Suspension System Based on Optimized Fuzzy Linear Quadratic Regulator Control Method

Vehicle suspension systems, which affect driving performance and passenger comfort, are actively researched with the development of technology and the insufficient quality of passive suspension systems. This paper establishes the suspension model of a quarter of the car and active control is realized. The suspension model was created using the Lagrange–Euler method. LQR, fuzzy logic control (FLC), and fuzzy-LQR control algorithms were developed and applied to the suspension system for active control. The purpose of these controllers is to improve car handling and passenger comfort. Undesirable vibrations occur in passive suspension systems. These vibrations should be reduced using the proposed control methods and a robust system should be developed. To enhance the performance of the fuzzy logic control (FLC) and fuzzy-LQR control methods, the optimal values of the coefficients of the points where the feet of the member functions touch are calculated using the particle swarm optimization (PSO) algorithm. Then, the designed controllers were simulated in the computer environment. The success of the control performance of the applied methods concerning the passive suspension system was compared in percentages. The results are presented and evaluated graphically and numerically. Using the integral time-weighted absolute error (ITAE) criterion, the methods were compared with each other and with the studies in the literature. As a result, it was found that the proposed control method (fuzzy-LQR) is about 84.2% more successful in body motion, 90% in car acceleration, 84.5% in suspension deflection, and 86.7% in tire deflection compared to the studies in the literature. All these results show that the car’s ride comfort has been significantly improved

Robust Control of Automotive Active Seat-Suspension System Subject to Actuator Saturation

This paper deals with the problem of robust sampled-data control for an automotive seatsuspension system subject to control input saturation. By using the nature of the sector nonlinearity, a sampled-data based control input saturation in the control design is studied. A passenger dynamic behavior is considered in the modeling of seat-suspension system, which makes the model more precisely and brings about uncertainties as well in the developed model. Robust output feedback control strategy is adopted since some state variables, such as, body acceleration and body deflection, are unavailable. The desired controller can be achieved by solving the corresponding linear matrix inequalities (LMIs). Finally, a design example has been given to demonstrate the effectiveness and advantages of the proposed controller design approach

Investigation of a non-linear suspension in a quarter car model

This thesis presents the study of a quarter car model which consists of a two-degree-of-freedom (2 DOF) with a linear spring and a nonlinear spring configuration. In this thesis, the use of non-linear vibration attachments is briefly explained, and a survey of the research done in this area is also discussed. The survey will show what have been done by the researches in this new field of nonlinear attachments. Also, it will be shown that this topic was not extensively researched and is a new type of research where no sufficient experimental work has been applied. As an application, a quarter car model was chosen to be investigated. The aim of the Thesis is to validate theoretically and experimentally the use of nonlinear springs in a quarter car model. Design the new type of suspension and insert it in the experimental set up, built from the ground up in the laboratory. A novel criterion for optimal ride comfort is the root mean square of the absolute acceleration specified by British standards ISO 2631-1997. A new way to reduce vibrations is to take advantage of nonlinear components. The mathematical model of the quarter-car is derived, and the dynamics are evaluated in terms of the main mass displacement and acceleration. The simulation of the car dynamics is performed using Matlab® and Simulink®. The realization of vibration reduction through one-way irreversible nonlinear energy localization which requires no pre-tuning in a quarter car model is studied for the first time. Results show that the addition of the nonlinear stiffness decreases the vibration of the sprung mass to meet optimal ride comfort standards. As the passenger is situated above the sprung mass, any reduction in the sprung mass dynamics will directly have the same effect on the passenger of the vehicle. The future is in the use of a nonlinear suspension that could provide improvement in performance over that realized by the passive, semi active and active suspension. The use of a quarter car model is simple compared to a half car model or a full car model, furthermore in the more complex models you can study the heave and the pitch of the vehicle. For the initial study of the nonlinear spring the quarter car model was sufficient enough to study the dynamics of the vehicle. Obtaining an optimum suspension system is of great importance for automotive and vibration engineer involved in the vehicle design process. The suspension affects an automobile’s comfort, performance, and safety. In this thesis, the optimization of suspension parameters which include the spring stiffness and damper coefficient is designed to compromise between the comfort and the road handling. Using Genetic algorithm an automated optimization of suspension parameters was executed to meet performance requirements specified. Results show that by optimizing the parameters the vibration in the system decreases immensely

Modelling and control of magnetorheological dampers for vehicle suspension systems

Magnetorheological (MR) dampers are adaptive devices whose properties can be adjusted through the application of a controlled voltage signal. A semi-active suspension system incorporating MR dampers combines the advantages of both active and passive suspensions. For this reason, there has been a continuous effort to develop control algorithms for MR-damped vehicle suspension systems to meet the requirements of the automotive industry. The overall aims of this thesis are twofold: (i) The investigation of non-parametric techniques for the identification of the nonlinear dynamics of an MR damper. (ii) The implementation of these techniques in the investigation of MR damper control of a vehicle suspension system that makes minimal use of sensors, thereby reducing the implementation cost and increasing system reliability. The novel contributions of this thesis can be listed as follows: 1- Nonparametric identification modelling of an MR damper using Chebyshev polynomials to identify the damping force from both simulated and experimental data. 2- The neural network identification of both the direct and inverse dynamics of an MR damper through an experimental procedure. 3- The experimental evaluation of a neural network MR damper controller relative to previously proposed controllers. 4- The application of the neural-based damper controller trained through experimental data to a semi-active vehicle suspension system. 5- The development and evaluation of an improved control strategy for a semi-active car seat suspension system using an MR damper. Simulated and experimental validation data tests show that Chebyshev polynomials can be used to identify the damper force as an approximate function of the displacement, velocity and input voltage. Feed-forward and recurrent neural networks are used to model both the direct and inverse dynamics of MR dampers. It is shown that these neural networks are superior to Chebyshev polynomials and can reliably represent both the direct and inverse dynamic behaviours of MR dampers. The neural network models are shown to be reasonably robust against significant temperature variation. Experimental tests show that an MR damper controller based a recurrent neural network (RNN) model of its inverse dynamics is superior to conventional controllers in achieving a desired damping force, apart from being more cost-effective. This is confirmed by introducing such a controller into a semi-active suspension, in conjunction with an overall system controller based on the sliding mode control algorithm. Control performance criteria are evaluated in the time and frequency domains in order to quantify the suspension effectiveness under bump and random road excitations. A study using the modified Bouc-Wen model for the MR damper, and another study using an actual damper fitted in a hardware-in-the-loop- simulation (HILS), both show that the inverse RNN damper controller potentially gives significantly superior ride comfort and vehicle stability. It is also shown that a similar control strategy is highly effective when used for a semi-active car seat suspension system incorporating an MR damper.EThOS - Electronic Theses Online ServiceEgyptian GovenmentGBUnited Kingdo

Active suspension control of electric vehicle with in-wheel motors

In-wheel motor (IWM) technology has attracted increasing research interests in recent years due to the numerous advantages it offers. However, the direct attachment of IWMs to the wheels can result in an increase in the vehicle unsprung mass and a significant drop in the suspension ride comfort performance and road holding stability. Other issues such as motor bearing wear motor vibration, air-gap eccentricity and residual unbalanced radial force can adversely influence the motor vibration, passenger comfort and vehicle rollover stability. Active suspension and optimized passive suspension are possible methods deployed to improve the ride comfort and safety of electric vehicles equipped with inwheel motor. The trade-off between ride comfort and handling stability is a major challenge in active suspension design. This thesis investigates the development of novel active suspension systems for successful implementation of IWM technology in electric cars. Towards such aim, several active suspension methods based on robust H∞ control methods are developed to achieve enhanced suspension performance by overcoming the conflicting requirement between ride comfort, suspension deflection and road holding. A novel fault-tolerant H∞ controller based on friction compensation is in the presence of system parameter uncertainties, actuator faults, as well as actuator time delay and system friction is proposed. A friction observer-based Takagi-Sugeno (T-S) fuzzy H∞ controller is developed for active suspension with sprung mass variation and system friction. This method is validated experimentally on a quarter car test rig. The experimental results demonstrate the effectiveness of proposed control methods in improving vehicle ride performance and road holding capability under different road profiles. Quarter car suspension model with suspended shaft-less direct-drive motors has the potential to improve the road holding capability and ride performance. Based on the quarter car suspension with dynamic vibration absorber (DVA) model, a multi-objective parameter optimization for active suspension of IWM mounted electric vehicle based on genetic algorithm (GA) is proposed to suppress the sprung mass vibration, motor vibration, motor bearing wear as well as improving ride comfort, suspension deflection and road holding stability. Then a fault-tolerant fuzzy H∞ control design approach for active suspension of IWM driven electric vehicles in the presence of sprung mass variation, actuator faults and control input constraints is proposed. The T-S fuzzy suspension model is used to cope with the possible sprung mass variation. The output feedback control problem for active suspension system of IWM driven electric vehicles with actuator faults and time delay is further investigated. The suspended motor parameters and vehicle suspension parameters are optimized based on the particle swarm optimization. A robust output feedback H∞ controller is designed to guarantee the system’s asymptotic stability and simultaneously satisfying the performance constraints. The proposed output feedback controller reveals much better performance than previous work when different actuator thrust losses and time delay occurs. The road surface roughness is coupled with in-wheel switched reluctance motor air-gap eccentricity and the unbalanced residual vertical force. Coupling effects between road excitation and in wheel switched reluctance motor (SRM) on electric vehicle ride comfort are also analysed in this thesis. A hybrid control method including output feedback controller and SRM controller are designed to suppress SRM vibration and to prolong the SRM lifespan, while at the same time improving vehicle ride comfort. Then a state feedback H∞ controller combined with SRM controller is designed for in-wheel SRM driven electric vehicle with DVA structure to enhance vehicle and SRM performance. Simulation results demonstrate the effectiveness of DVA structure based active suspension system with proposed control method its ability to significantly improve the road holding capability and ride performance, as well as motor performance