Uncertainty modelling and high performance robust controller for active front steering control

Aripin, Muhamad Khairi; Ghazali, Rozaimi; Sam, Yahaya Md; Danapalasingam, Kumeresan A.; Ismail, Muhamad Fahezal

doi:10.1109/ascc.2015.7244673

Cited by 5 publications

(6 citation statements)

References 23 publications

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“…The design of the I-EHB actuator adopted in this paper is shown in Fig.3. There are mainly electric brake master cylinder and eight high-speed on-off valves in the IEHB system, including a permanent magnetic synchronous motor 2 and a transmission 3, four pressure increasing valves (8,12,15,20) and four pressure reducing valves (16,19,22,23). And there are two plunger pumps (5,7).…”

Section: Structure and Model Of The I-ehb Actuatormentioning

confidence: 99%

“…Equation (19) (the sliding mode surface function) is used as the stability indicator, which is shown in Fig.10(f). It can be seen that, the peak values of the sliding mode surface function by ARBFN-SMC controller, by SMC controller, and without the stabilizing controller are about 6.0, 9.2, and 12.6, respectively.…”

Section: Test On a Low Adhesion-coefficient Roadmentioning

confidence: 99%

“…tire pressure, wheel alignment parameters, road condition, etc. ), which is a disadvantage for vehicle motion control performance [14,[17][18][19][20]. When the vehicle undergoes high accelerations at the limits of handling, tire's dynamics nature presents inherent nonlinearity that means a changed tire cornering stiffness.…”

Section: Introductionmentioning

confidence: 99%

“…In [17,18], tire nonlinearity is handled using the uncertain tire cornering stiffness, which are represented via a norm-bounded uncertainty. In [19], the reduced nonlinear two track model and linear uncertain single track model with uncertainty of cornering stiffness have been established. In [20], an adaptive robust global chassis nonlinear controller is developed in order to deal with the parameter uncertainties in the vehicle system and the unknown external disturbances.…”

Section: Introductionmentioning

confidence: 99%

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A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

et al. 2017

Vehicle System Dynamics

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Modeling uncertainties, parameter variations and unknown external disturbances are the main obstacles in the development of advanced controller for vehicle stability control at the limits of handling. For the parameter variations and unknown external disturbances, sliding mode control (SMC) method could be employed to achieve satisfactory tracking performance. But modeling uncertainties, such as the errors are caused in the process of model simplification, always exist in the model-based controller design and degrade the control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large uncertainty of the system by learning ability to approximate arbitrary nonlinear functions and guarantee the closed-loop system is robust global asymptotic stabilization. In this paper, a novel vehicle dynamics stability control strategy is proposed using adaptive radial basis function network sliding mode control (ARBFN-SMC) theory to learn the system uncertainties and eliminate the effects of system uncertainties. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. The co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with integrated-electro-hydraulic brake system (I-EHB) actuator under a Sine With Dwell manoeuvre. The simulation results show that the ARBFN-SMC scheme has superior stability and tracking performances at different running conditions compared with the SMC scheme.

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Section: Structure and Model Of The I-ehb Actuatormentioning

confidence: 99%

Section: Test On a Low Adhesion-coefficient Roadmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

et al. 2017

Vehicle System Dynamics

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“…During wheel slip tracking control, a radial basis function neural network (RBFNN) was used as an uncertainty observer to estimate the uncertainty during modeling [24]. A linear uncertainty vehicle model with steering rigidity uncertainty was built [25]. A function recursive fuzzy neural network uncertainty estimator [26] and a self-organizing function-link fuzzy cerebellar model articulation controller [27] were used to approximate the unknown nonlinear terms of vehicle dynamics during ABS control.…”

Section: Introductionmentioning

confidence: 99%

Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation

et al. 2021

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This study is targeted at the key state parameters of vehicle stability controllers, the controlled vehicle model, and the nonlinearity and uncertainty of external disturbance. An adaptive double-layer unscented Kalman filter (ADUKF) is used to compute the sideslip angle, and a vehicle stability control algorithm adaptive fuzzy radial basis function neural network sliding mode control (AFRBF-SMC) is proposed. Since the sideslip angle cannot be directly determined, a 7-degrees-of-freedom (DOF) nonlinear vehicle dynamic model is established and combined with ADUKF to estimate the sideslip angle. After that, a vehicle stability sliding mode controller is designed and used to trace the ideal values of the vehicle stability parameters. To handle the severe system vibration due to the large robustness coefficient in the sliding mode controller, we use a fuzzy radial basis function neural network (FRBFNN) algorithm to approximate the uncertain disturbance of the system. Then the adaptive rate of the system is solved using the Lyapunov algorithm, and the systemic stability and convergence of this algorithm are validated. Finally, the controlling algorithm is verified through joint simulation on MATLAB/Simulink-Carsim. ADUKF can estimate the sideslip angle with high precision. The AFRBF-SMC vehicle stability controller performs well with high precision and low vibration and can ensure the driving stability of vehicles.

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Continuous interval type‐2 fuzzy Q‐learning algorithm for trajectory tracking tasks for vehicles

Xuan

Lam

Shi

et al. 2022

Intl J Robust & Nonlinear

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Trajectory tracking is a fundamental but challenging task for vehicle automation. In addition to the system nonlinearity, the main difficulties in the trajectory tracking task are due to the environmental noise and the model uncertainties under different driving scenarios. Considering the uncertainties in the environment, the reinforcement learning method with continuous action and noise-resistance capability could be a promising way to overcome these issues. In this article, a novel continuous interval type-2 fuzzy Q-learning (CIT2FQL) algorithm is proposed to deal with the trajectory tracking task. By introducing the n-dimensional interval type-2 fuzzy inference system (n-D IT2FIS) in fuzzy Q-learning, our proposed method achieves the continuous Q-learning by combining the action interpolation with IT2FIS for the first time. We also proposed a simplified type-reduction method for n-D IT2FIS to improve the computing efficiency of the proposed method. Moreover, a radial basis function (RBF) layer is chosen as the basis function to achieve the q-value interpolation. Finally, a trajectory tracking task in a simulation environment is conducted to verify the effectiveness and robustness of the proposed method under different scenarios. The results demonstrate that the proposed method has better robustness and noise-resistance capability while maintaining good tracking performance compared with the state-of-the-art baseline algorithms including double deep Q network (DDQN), proximal policy optimization (PPO), and interval type-2 dynamic fuzzy Q-learning (IT2DFQL).

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Uncertainty modelling and high performance robust controller for active front steering control

Cited by 5 publications

References 23 publications

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation

Continuous interval type‐2 fuzzy Q‐learning algorithm for trajectory tracking tasks for vehicles

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