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Collaborative control strategy based on different driving conditions is a challenge for chassis systems with various electronic control units. This paper proposes a chassis cooperative control strategy based on vehicle inertial sensor data. Its novelty lies in the fact that it greatly simplifies the judgment logic while classifying various driving behaviours, and further reduces the possibility of bad control operation caused by misjudgement of driving state through heuristic decision logic. The clustering algorithm based on triaxial acceleration and angular velocity data was used to identify the driving behaviour of the vehicle, and the complex driving conditions were simplified into a single driving condition. The multi‐axis weighted fusion method is used to extend the data and improve the generalization performance of the data. In order to improve the stability of steering control, S‐type function is used to allocate the weights of AFS and DYC. The proposed control strategy is tested in the CarSim/Simulink co‐simulation environment, and the simulation results of two key driving scenarios (double lane change and emergency braking) show that the proposed control strategy can effectively improve the vehicle handling and safety.

An adaptive second-order sliding-mode observer based on Super-Twisting Algorithm (STA-SMO) to estimate rotor position and speed of permanent magnet synchronous motor (PMSM) is proposed in this paper. The advantages of the proposed observer algorithm are reflected in small chattering, high tracking accuracy, good robustness to parameters change and external disturbance in wide positive and reversal speed range. The Lyapunov stability of the system is proved. A new perturbation term form is employed and according to the stable condition of STA-SMO, the adaptive sliding-mode coefficients related to absolute value of estimated speed are deduced to guarantee the performance mentioned above in wide positive and negative speed range. In order to extract rotor position and speed in both speed range from the adaptive STA-SMO, an improved conventional phase-locked loop (IPLL), which is more accurate, is studied and its non-linear dynamics are analyzed in detail to prove the effectiveness of the IPLL theoretically. In the end, the effectiveness of the adopted adaptive second-order sliding-mode observer with IPLL structure is verified through simulations. Simulation results show that the position error and chattering of the proposed adaptive observer are decreased more than 25% and 50% compared with conventional SMO and STA-SMO in wide positive and negative speed range. Meanwhile, the proposed system still has good estimation performance and strong robustness with torque and parameters variation.

A nonlinear state equation model of Active Hydro-Pneumatic suspension (AHP) system is established basing on power bond graph theory. The nonlinear characteristics of stiffness and friction of the hydro-pneumatic spring actuator and the oil compressibility are considered in modeling. Meanwhile, a theoretical analysis is conducted for dynamic structural characteristics of hydro-pneumatic spring actuator. A sliding mode control (SMC) strategy is presented which has two closed-loops, where the outer loop considers the sprung velocity of skyhook reference model output as tracking target and the inner loop regards the desired force of the sliding mode solver as tracking target. Simultaneously, the sliding mode control laws of inner and outer loops are deduced. Especially, a divergence problem of outer loop sliding model solver caused by time delay is analyzed and a stabilization control algorithm is put forward to solve it. The accurate tracking of desired force of actuator and the improvement of ride quality are realized, while the effectiveness of the proposed sliding mode control law and stabilization control algorithm are verified through simulation studies of relevant contrast test.

A new road estimation based suspension hybrid control strategy is proposed. Its aim is to adaptively change control gains to improve both ride comfort and road handling with the constraint of rattle space. To achieve this, analytical expressions for ride comfort, road handling, and rattle space with respect to road input are derived based on the hybrid control, and the problem is transformed into a MOOP (Multiobjective Optimization Problem) and has been solved by NSGA-II (Nondominated Sorting Genetic Algorithm-II). A new road estimation and classification method, which is based on ANFIS (Adaptive Neurofuzzy Inference System) and wavelet transforms, is then presented as a means of detecting the road profile level, and a Kalman filter is designed for observing unknown states. The results of simulations conducted with random road excitation show that the efficiency of the proposed control strategy compares favourably to that of a passive system.

The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN) is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

The camera always suffers from image instability on the moving vehicle due to unintentional vibrations caused by road roughness. This article presents an adaptive neural network approach mixed with linear quadratic regulator control for a quarter-car active suspension system to stabilize the image captured area of the camera. An active suspension system provides extra force through the actuator which allows it to suppress vertical vibration of sprung mass. First, to deal with the road disturbance and the system uncertainties, radial basis function neural network is proposed to construct the map between the state error and the compensation component, which can correct the optimal state-feedback control law. The weights matrix of radial basis function neural network is adaptively tuned online. Then, the closed-loop stability and asymptotic convergence performance is guaranteed by Lyapunov analysis. Finally, the simulation results demonstrate that the proposed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.