Backstepping based fuzzy logic control of active vehicle suspension systems

In this paper, Particle Swarm Optimization (PSO) technique is applied to tune the Adaptive Neuro Fuzzy Controller (ANFIS) for vehicle suspension system. LQR controller is used to obtain the training data set for the vehicle suspension system. Subtractive clustering technique is used to formulate ANFIS which approximates the actuator output force as a function of system states. PSO algorithm search for optimal radii for subtractive clustering based ANFIS. Training is done off line and the cost function is based on the minimization of the error between actual and approximated output. Simulation results show that the PSO-ANFIS based vehicle suspension system exhibits an improved ride comfort and good road holding ability.

Semi-active [formula omitted] control of vehicle suspension with magneto-rheological dampers

Due to the inherent nonlinear nature of electro-rheological (ER) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the nonlinear damping force model is made to identify the properties of the ER damper using higher order spectrum. The higher order spectral analysis is used to investigate the nonlinear frequency coupling phenomena with the damping force signal according to the sinusoidal excitation of the damper. Also, this paper presents an inverse model of the ER damper, i.e., the model can predict the required voltage so that the ER damper can produce the desired force for the requirement of vibration control of vehicle suspension systems. The inverse model has been constructed by using piecewise linear damping force model. In this paper, the fuzzy logic control based on heuristic knowledge is combined with the skyhook control. And it is simulated for a quarter car model. The acceleration of the sprung mass is included in the premise part of the fuzzy rules to reduce the vertical acceleration RMS value of the sprung mass. Then scaling factors and membership functions are tuned using genetic algorithm to obtain optimal performance.

Scientific research and technological advances in recent years on car suspensions have led to an increase in their dynamic performance, improved passenger comfort and safety. The importance of the field is also given by the need for continuous increase of traffic safety factors in the context of infrastructure development and implicitly the increase of driving speed. The paper presents the classification of suspensions and methods used for modelling, simulation, and control of active vehicle suspensions. The Matlab environment was used for simulation and control design. Several control methods and comparisons between them are presented and implemented by simulation. A consistent part of the paper presents an MPC type control method applied for the control of the active suspension. Possible future developments are highlighted.

In order to effectively realize the damping control and regenerative energy recovery of vehicle suspension, a new kind of hybrid active suspension structure with the ball screw actuator and magnetorheological (MR) damper is put forward. Firstly, for the analysis of the suspension performance, a quarter dynamic model of vehicle hybrid suspension is established, and at the same time, the mathematical models of MR damper and ball screw actuator are founded. Secondly, the active mode with damping switching control of the hybrid suspension and the semiactive mode with feedback adjustment of the electromagnetic damping force of the hybrid suspension are analyzed. Then, the multimode coordinated control system of the hybrid suspension is designed. Under the cyclic driving condition, the damping performance and energy consumption characteristics of the hybrid suspension are simulated by MATLAB/Simulink software. Finally, the bench tests of the hybrid suspension system are done. The simulation and experimental results show that compared with passive suspension, the root mean square of the sprung mass acceleration of the hybrid suspension with the active mode and semiactive mode is, respectively, reduced by 39% and 16% under the random road. The damping effect of the hybrid suspension system is obvious.

To provide a dynamic semi-active vehicle suspension control with magnetorheological damper able to operate under conditions of current uncertainty, algorithms such as “Sky Hook” and their modifications, algorithms based on fuzzy logic are widely used. They are mainly designed to attenuate kinematic disturbances—disturbances of the road surface. The article considers a new adaptive suspension control algorithm able to reduce the impacts on the vehicle body from the road and inertial forces under the current uncertainty of parameters of vehicle, suspension, and disturbances. The algorithm is based on the parametric identification of the mathematical model of the controlled element, carried out during current operation of the control system, as well as use of an implicit reference model and “simplified” conditions of adaptability. The model study of the proposed algorithm effectiveness was carried out using two-mass model of “quarter-vehicle” suspension in comparison with passive suspension.KeywordsSemi-active suspension control systemMagnetorheological fluidControl algorithmsCurrent uncertaintyParametric identification algorithmAdaptive control system

In this paper a fuzzy Sugeno rule based controller is used to manage a semi-active suspension of 1/4 vehicle model. Our semi-active suspensions have the characteristic to change the damping constant in order to reduce vertical oscillation of the vehicle, to restrain the shell vehicle acceleration and consequently to be more comfortable for passengers. On the other hand, if no control is present, the suspension works in passive mode, i.e. without damping parameters changing. The inputs of our fuzzy controller, three in total, are the dynamic wheel load described by five membership functions, its derivative and the vertical acceleration both described by three membership functions. By comparing the performances of the suspension with and without control, the Sugeno based model controller results in a more comfortable and safe system.

Multi-objective control for active vehicle suspension with wheelbase preview

During blueberry picking operations, changes in the blueberry harvester’s body posture (BHBP) caused by undulating farmland surfaces significantly affect the operational stability of the harvesting device. To mitigate these effects, an active suspension (AS) control system was developed. First, considering the coherence and time lag of the four-wheel tracks, the filtered white noise method was chosen to generate road excitations. Subsequently, the virtual prototype model of the blueberry harvester (BH) was built in ADAMS with the nonlinear properties of the passive suspension. A fuzzy proportion integral differential (PID) control algorithm and decoupled control strategy were employed to design the AS control system. Finally, ADAMS-MATLAB co-simulations and field tests were conducted. The results indicate that the measured farmland road profile conforms to standard D-level road excitations according to ISO8608, and the co-simulation model accurately predicts the BH’s dynamic response. Under AS control, the vertical acceleration, pitch acceleration, and roll acceleration of the BH decreased by 38.52%, 37.39%, and 34.29%, respectively, during simulations compared to the passive suspension. In field tests, these reductions were 36.51%, 33.84%, and 30.21%, respectively. The AS control system proposed in this study significantly improves the stability of the BH under farmland road excitation, effectively mitigating equipment impacts and wear caused by machine jolts while enhancing harvesting performance. This research addresses the gap in applying AS technology within the field of harvesting machinery, offering a novel technical approach for the development of vehicle control systems for harvesting machinery targeting blueberries and other shrub crops. It holds considerable theoretical value and practical significance.

In this paper, we study the suspension performances with an adaptive inerter under the assumption that the inertance may be adjusted in real-time. A quarter-car model with an inerter installed in parallel with a spring and a damper is considered. First, for a given suspension system, a study of the effects of using a fixed inerter is provided. It shows the difficulty in choosing a fixed inerter to satisfy the vehicle performances at the sprung mass natural frequency without deteriorating significantly at the unsprung mass natural frequency. Then, a state-feedback H2 controller for an active suspension system is designed. The active force is approximated by an inerter with adjustable inertance. Ride quality, suspension deflection and tyre deflection performances are considered independently as the prime objective. Simulation results show that comparing with the passive suspension with fixed inerter, for ride quality and tyre deflection, the suspension with adaptive inerter can achieve improvement at the sprung mass natural frequency at the expense of a relatively small deterioration at the unsprung mass natural frequency. For suspension deflection, a better performance can be achieved at high frequencies with almost no loss of performance at other frequencies. This method is easier to implement in practice than the optimized passive suspension with a fixed inerter, which calls for complicated layout.

This paper investigates the issue of accurate tracking control for vehicle suspension systems subject to finite time convergence in presence of external disturbance. For this purpose, a continuous nonsingular terminal sliding mode (TSM) control approach is proposed for disturbance attenuation. Through a rigorous stability analysis, it is proved that the time taken to reach the desired trajectory under disturbance is guaranteed to be finite time due to the proposed control scheme. The numerical simulation results carried out for a quarter suspension to illustrate the effectiveness of the proposed control law, where a typical period road input is considered as the road disturbance.

This paper addresses the issue of a road-type-adaptive control strategy aimed at enhancing suspension performance. H2 synthesis is employed for modeling road irregularities as impulses or white noise, minimizing the root mean square (RMS) of performance outputs for these specific road types. It should be noted, however, that this approach may lead to suboptimal performance when applied to other road profiles. In contrast, the H∞ controller is employed to minimize the RMS of performance outputs under worst-case road irregularities, taking a conservative stance that ensures robustness across all road profiles. To leverage the advantages of both controllers and achieve overall improved suspension performance, automatic switching between these controllers is recommended based on the identified road type. To implement this adaptive switching mechanism, manual switching is performed, gathering input–output data from the controllers. These data are subsequently employed for training an Adaptive Neuro-Fuzzy Inference System (ANFIS) network. This elegant approach contributes significantly to the optimization of suspension performance. The simulation results employing this novel ANFIS-based controller demonstrate substantial performance enhancements compared to both the H2 and H∞ controllers. Notably, the ANFIS-based controller exhibits a remarkable 62% improvement in vehicle body comfort and a significant 57% enhancement in ride safety compared to passive suspension, highlighting its potential for superior suspension performance across diverse road conditions.

This paper analyzes, theoretically and by computer simulation, the capabilities and limitations of neural networks to be used for the identification and optimal control of nonlinear dynamical systems. It is shown how neural networks can be efficiently trained to identify the forward and inverse dynamics of systems and also to work as optimal controllers which minimize some peformance measures of the system to be controlled. The performance of neural networks when applied to the optimal control of nonlinear vehicle suspensions is analyzed and compared with the performance of passive suspensions and that of active suspensions with linear (LQ) controllers designed by linearizing the nonlinear characteristics of the suspensions around the equilibrium point. It is found that nonlinear vehicle suspensions with neuro-control show better performance than suspensions controlled with conventional LQ regulators. Some issues about the implementation of neural networks to improve its convergence and generalization properties are analyzed.

Effective control of ride quality and handling performance are challenges for active vehicle suspension systems, particularly for off-road applications. Off-road vehicles experience large suspension displacements, where the nonlinear kinematics and damping characteristics of suspension elements are significant. These nonlinearities tend to degrade the performance of active suspension systems, introducing harshness to the ride quality and reducing off-road mobility. Typical control strategies rely on linear time-invariant models of the suspension dynamics. While these models are convenient, nominally accurate, and tractable due to the abundance of linear control techniques, they neglect the nonlinearities and time-varying dynamics present in real suspension systems. One approach to improving the effectiveness of active vehicle suspension systems, while preserving the benefits of linear control techniques, is to estimate and cancel the nonlinearities using feedback linearization. In this paper, the authors demonstrate an intelligent parameter estimation approach, using structured artificial neural networks, that continually "learns" the nonlinear parameter variations of a quarter-car suspension model. This estimation algorithm becomes the foundation for an intelligent feedback linearization (IFL) controller for active vehicle suspensions. Results are presented for real-time experimental tests and field evaluations on a military HMMWV. These results clearly demonstrate the viability and effectiveness of this approach as a tool for rapid, online development of vehicle suspension models and controllers.

An active suspension of an intelligent electric vehicle driven by four in-wheel motors (IEV-DFIM) is a strong nonlinear system because of time-varying parameters in practice, which causes difficult controllability. For addressing this issue, the paper proposes a novel homogeneous output feedback control method. Firstly, an active suspension dynamic model which considers the time-varying sprung mass, stiffness coefficients and damping coefficients is built. Secondly, an active suspension control system is constructed based on the dynamic model whose uncertain and nonlinear terms do not meet the linear or high-order growing. Thirdly, the homogeneous output feedback method is developed to relax the growth condition imposed on the uncertain and nonlinear terms for the active suspension. Finally, the simulation and test are carried out to verify the effectiveness of the designed controller compared with the sliding mode control method and passive suspension.