Improving the ride comfort of full car model with a decoupling intelligent model free controller

Abstract

A new technique of active control is presented for enhancing the dynamic responses of full car systems with bias to ride comfort performance. A seven degrees of freedom full car model is used, and its performance is evaluated on a bump road and random road. The proposed scheme is a combination between traditional controller design methods with an algebraic estimator of perturbations that will be eliminated online. From the inputs and the outputs signals, the unmodeled phenomenon can be estimated and be subtracted for ensuring the proper functioning of the system. The precise mathematical model of the system is not required for the implementation of the control scheme and is replaced by ultra-local models. Furthermore, the effectiveness of the algebraic estimator of road profile is introduced for applying the excitation sources of the reference generator. The road profiles in front of the vehicle are estimated separately from the rear road profiles. The ride comfort is improved progressively with the novel controller configuration by 83% from the passive case. Further, pitching and rolling fluctuations are reduced by 80% and 85% respectively. The numerical simulations illustrate the effectiveness and the robustness against vehicle body mass deviation and nonlinear actuator saturation of the proposed control method compared to passive and Active Disturbance Rejection Controller based on Input Decoupling Transformation.

Active suspension for all-terrain vehicle with intelligent control using artificial neural networks

“…The virtual controller demonstrated promising results in mitigating the acceleration of the suspended mass across diverse scenarios. Its performance was validated through a comparison with a conventional PID controller [24].…”

Optimal α-Variable Model-Free Adaptive Barrier Function Fractional Order Nonlinear Sliding Mode Control for Four Area Interconnected Hybrid Power System With Nonlinearities

“…Fortunately, the ultra-local model (ULM) algorithm-based model-free control (MFC) has attracted tremendous attention from academics and is widely utilized in many control applications [8], [30], which decreases the reliance on model knowledge and depends merely on input and output information if compared to the model-based control strategies. The MFC's robustness mainly depends on the proposed estimator's ability to observe unknown, uncertain system dynamics, such as time-delay estimation (TDE) [31] and algebraic observer (AO) [32]. However, due to time delays and time windows, respectively, both the TDE and AO inevitably have approximation errors.…”

“…2) To avoid precise modeling information and decrease the difficulty of controller design, the ULM algorithmbased MPC is employed to re-formulate the complex nonlinear four-area interconnected hybrid power system. Unlike the existing ULM algorithm-based MFCs, the TDE [31] and AO [32] have unavoidable estimation errors due to the time delays and time windows, respectively. Furthermore, when the time derivative of the uncertain dynamics can not converge to zero, the ESO [8], [34] can only achieve bounded observation asymptotically.…”

“…Thus, an adaptive parameter based on the barrier function is proposed to approximate it. 4) In the existing ULM algorithm-based MFCs [8], [31]- [34], the value of α is fixed and pre-defined. In [35], an adaptive method based on a least-squares algorithm is presented to tune α automatically.…”

Effective Energy Management Strategy with Model-Free DC-Bus Voltage Control for Fuel Cell/Battery/Supercapacitor Hybrid Electric Vehicle System

“…Fortunately, the model-free control (MPC) based on an ULM algorithm has received great attention from researchers and widely applied in different fields [39,40], which reduces the dependence on model information and relies only on input/output data if compared to the model-based control approach. The robustness of the MFC method relies on the designed observer/estimator to approximate the unmodeled system dynamics or lumped uncertainties like algebraic observer (AO) [41] and time-delay estimation (TDE) [42]. However, the TDE and AO both have inevitable estimating errors because of the time delays and time windows, respectively.…”