Active Suspension Control Research Articles

Constrained H∞ optimal control for nonlinear active suspensions via data-driven reinforcement learning algorithm

Abstract This paper addresses the constrained H∞ optimal control problem for nonlinear active vehicle suspension systems, with a focus on deriving an approximate solution through data-driven reinforcement learning in the context of differential games. A dynamic model of the half-car active suspension system with constraints is first established, where the constrained control forces and external road disturbances are formulated as a zero-sum game between two players. This leads to the Hamilton-Jacobi-Isaacs (HJI) equation, with a Nash equilibrium as the desired solution. To efficiently solve the HJI equation and mitigate the impact of model parameter uncertainties, an actor-critic neural network (NN) framework is employed to approximate both the control policy and the value function of the system. A reinforcement learning algorithm based on the input-output data of the suspension system is subsequently derived. Numerical examples are provided to demonstrate the effectiveness of the proposed approach. Under varying control force constraints, the active suspension system consistently exhibits excellent vibration reduction performance.

Road Roughness Recognition: Feature Extraction and Speed-Adaptive Classification Based on Simulation and Real-Vehicle Tests

Road roughness exerts a direct influence on the vertical dynamic performance of vehicles, and the accurate characterization of road roughness is essential for optimizing vehicle suspension systems. This paper addresses two key challenges in roughness recognition: feature extraction and adaptive classification under different speeds. In detail, based on simulation tests of the quarter-vehicle vertical vibration model and real-vehicle test, this paper reveals the strong correlation between the unsprung mass vertical vibration response of vehicles and road roughness. The feasibility of using unsprung mass vertical vibration response as a feature for recognizing and classifying road roughness is verified. And an adaptive road roughness classifier is proposed based on vehicle-speed-related features. Both simulation and real-vehicle results confirm that (i) the unsprung vertical vibration displacement is strongly correlated with road roughness (R2 = 0.997); (ii) road roughness can be classified with high accuracy with the unsprung mass vertical vibration response taken as the only feature (simulation tests: 98.88% to 100%; real-vehicle tests: 100%); and (iii) the accuracy of the proposed speed-adaptive classifier is 20% more accurate than the conventional classifier that does not consider vehicle speed features. This research can provide accurate road excitation for the adaptive real-time control of semi-active or active vehicle suspensions.

Robust active suspension preview control method based on unknown road detection information

Although robust active suspension control problem has been well studied, robust controller design considering unknown road surface recognition (such as speed bump detection) has not been fully solved. This paper developed a H∞(H-infinity) preview control method to complete the robust active control design for suspension systems, which uses the ideas of visual perception proposed for preview control. First, a novel detection approach of typical working conditions of urban road (speed bump pavement) based on YOLOV5 is developed using camera combined with image fitting technology. Then, an augmented system is constructed using the recognized speed bump information, so as to reformulate the active suspension control into a modified robust regulation problem. On this basis, the linear matrix inequalities (LMI) technique is introduced to develop the H∞ preview control of the suspension system. Finally, the efficiency of the proposed speed bump detection and H∞ preview control approach is verified by real vehicle testing and simulation, respectively.

Control of Active Suspension Systems Based on Mechanical Wave Concepts

Wave-based control (WBC) offers a relatively novel approach to the challenge of controlling flexible mechanisms by treating the interaction between actuator and system as the launch and absorption of mechanical waves. WBC is a robust approach but has been unexplored in active suspension systems to date. This study adapts WBC to a quarter-car suspension model. Having embedded an actuator as the active element of a car suspension, a novel but simple ‘force impedance’ adaptation of WBC is introduced and implemented for effective vibration control. Testing with various input signals (pulse, sinusoidal, and random profile) highlights the active system’s significant ride comfort and rapid vibration suppression with zero steady-state error. Compared to two other models—one employing an ideal skyhook strategy and the other a passive suspension—the active system utilizing WBC outperforms across many criteria. The active controller achieves over 38% superior ride comfort compared to the skyhook model for a pulse road input. This is accomplished while adhering to WBC principles: relying solely on actuator-interface measurements, simplicity, cost-effectiveness, with no need for detailed system models, extensive sensors, or deep system knowledge.

Co-Design of Enhanced Fuzzy Observer-Based Estimation and Gain-Scheduling Control for Active Suspension Systems Under Malicious Attacks

Co-Design of Enhanced Fuzzy Observer-Based Estimation and Gain-Scheduling Control for Active Suspension Systems Under Malicious Attacks

A Position–Force Feedback Optimal Control Strategy for Improving the Passability and Wheel Grounding Performance of Active Suspension Vehicles in a Coordinated Manner

This paper aims to solve the problems of poor mobility, passability, and stability in heavy-duty vehicles, and proposes an active suspension system control strategy based on position–force feedback optimal control to coordinately enhance vehicle passability and wheel grounding performance. Firstly, a two-degrees-of-freedom one-sixth vehicle active suspension model and a valve-controlled hydraulic actuator system model are constructed, and the advantages of impedance control in robot compliance control are integrated to analyze their applicability in hydraulic active suspension. Next, a position feedback controller and force feedback LQG optimal controller for fuzzy PID control are designed, the fuzzy PID-LQG (FPL) integrated method is applied to the hydraulic active suspension system, and the dynamic load of the wheel is tracked by impedance control to obtain the spring mass displacement correction. Then, a suspension system model under the excitation of a C-class road surface and a 0.11 m raised road surface is constructed, and the dynamic simulation and comparison of active/passive suspension systems are carried out. The results show that, compared with PS and LQR control, the body vertical acceleration, suspension dynamic deflection, and wheel dynamic load root-mean-square value of the proposed FPL integrated control active suspension are reduced, which can effectively reduce the body vibration and wheel dynamic load and meet the design objectives proposed in this paper, effectively improving vehicle ride comfort, handling stability, passability, and wheel grounding performance.

Robust decoupled sliding mode control for active suspension systems with prescribed tracking performance

Robust decoupled sliding mode control for active suspension systems with prescribed tracking performance

Affordable Road Obstacle Detection and Active Suspension Control Using Inertial and Motion Sensors

The electrification trend characterizing the current automotive industry creates opportunities for the implementation of innovative functionalities, enhancing aspects of energy efficiency and vehicle dynamics. Active vehicle suspensions are an important subsystem in this process. To enable proper suspension control, vehicle sensors can be used to measure the system’s response and, in some cases, preview the road conditions and the presence of possible obstacles. When assessing the performance of a suspension system, the speed bump crossing represents a challenging maneuver. A suitable trade-off between comfort and road holding must be found through different phases of the profile. The proposed work uses a fixed-gain observer obtained from Kalman filtering to identify road unevenness and adapt the control strategy when the vehicle travels through a bump. To this end, the obstacle is identified through the use of affordable sensors available in high-end vehicles: accelerometers, inertial measurement units, and stroke sensors. The proposed technique is also affordable from the computational point of view, thus enabling its use in common microprocessors tailored for the automotive field. The bump identification technique is validated through experimental data captured in a vehicle demonstrator. Subsequently, numerical results show that the proposed technique is able to enhance comfort while keeping road holding and attenuating the transient after taking the bump.

Resilient control design of networked vehicle suspension system under False Data Injection Attacks

Under covert false data injection (FDI) attacks, the robust security control problem of nonlinear active vehicle suspension systems (AVSSs) is examined in this paper. First, the network fuzzy control active suspension model is developed, and the Takagi-Sugeno (T-S) fuzzy technology is applied to address the uncertainty of AVSSs. Secondly, considering the influence of the FDI attack model, a resilient controller is designed for AVSSs. The exponential stability condition of the system under the network assault is then derived concurrently with the design of the Lyapunov function associated with the membership degree. Lastly, a simulation test confirms the developed controller's efficacy.

Active control method for vehicle suspension based on the mechanical characteristics of large-scalezero-stiffness vibration isolator

People have high requirements for the comfort of vehicle riding, so active suspension control has always been a research hotspot. However, due to the difficulty in obtaining speed signals, the implementation of active suspension control is extremely difficult. In this paper, a novel active control method for vehicle suspension is proposed, which is based on the mechanical characteristics of a large-scale zero-stiffness isolator. The active control force obtained by the new method is only proportional to the relative displacement between the tire and the vehicle body, which is easily achievable in practical applications. Firstly, a physical model of a novel large-scale zero-stiffness vibration isolator is developed, and its segmented force characteristics are derived. Subsequently, the force characteristics between the tire and the vehicle body are integrated with the segmented force characteristics of the isolator to generate the active control force for the 2-degree-of-freedom (2-DOF) quarter vehicle active suspension model. Then, an analysis was conducted to demonstrate that the proposed active control method can maintain stability of the vehicle suspension system. Then, there representative road surfaces are selected for numerical simulation testing, and the results demonstrate the efficacy of the active control method in significantly enhancing suspension performance. In comparison to passive suspension, the vertical acceleration RMS values of the vehicle body are reduced by 76.2%, 77.8%, and 43.2% under the bump road, sine undulating road, and C-level road, respectively. Finally, to be closer to the actual situation, a new testing verification method is proposed. A multi-body dynamics model considering the existence of random nonlinear disturbances in the vehicle body is built and it is used to test the effectiveness of the control algorithm.

Comparative Study on Active Suspension Controllers with Parameter Adaptive and Static Output Feedback Control

This paper presents a comparative study on active suspension controllers for ride comfort. Two types of active suspension controllers are designed and compared in terms of ride comfort: static output feedback (SOF) and parameter adaptive ones, which have identical controller structure. A quarter-car model is selected as a vehicle model. To date, LQR has been used as an active suspension controller. LQR is hard to implement in real vehicles due to the full-state measurement requirement. To avoid the full-state measurement of LQR, SOF control is selected as a controller structure in this paper. Suspension stroke and its rate are selected as sensor outputs for SOF and parameter active controllers. Two types of SOF controllers are designed. The first is the LQ SOF controller, designed with the state-space model and LQ cost function. The second is SOF controllers, designed by simulation-based optimization (SBOM) for the quarter-car model with nonlinear spring and damper. A parameter adaptive controller is designed with the recursive lease square (RLS) algorithm and its equivalent extended Kalman filter (EKF). For comparison, LQR is designed and used as a baseline. From simulation results, it is shown that the static output feedback and parameter adaptive controllers are equivalent to each other in terms of controller structure and ride comfort and which conditions are needed for better control performance on those controllers.

Study on Impedance Control of Active Suspension

Study on Impedance Control of Active Suspension

Energy-saving tracking control and experiment of nonlinear active suspension for multi-axle vehicles considering road slope

Energy-saving tracking control and experiment of nonlinear active suspension for multi-axle vehicles considering road slope

Finite time energy-saving control of active suspension based on force random walk PSO

To reduce energy consumption in the active suspension, this paper proposes a new method for vehicle active suspension control. The energy consumption characteristics of the coupling system is analyzed. On this basis, a finite time controller based on unknown state estimation is proposed, and its global finite time convergence is proved, the upper limit of time convergence is given. Secondly, based on Analytic Hierarchy Process (AHP) and the force random walk PSO algorithm (FRWPSO), a multi-objective optimization function is constructed to determine the control parameters under different driving modes. Finally, simulation experiments show that under random road surfaces and triangular obstacles, the proposed control method can effectively improve suspension comfort and operational stability compared to nonsingular terminal sliding mode control (TSMC), The RMS values of energy consumption decreased by 55.34% and 20.55% respectively, verifying the effectiveness of the proposed control method.

Active suspension control strategy with the high adaptability of multi-axle vehicles based on the body attitude stabilization

A control strategy with the strong adaptability is crucial to maintain the stable body attitude, when the vehicle is encountering various complex road inputs. This article is based on the theory that the centroid of the cheetah's body always moves in a straight or curved line when running in the wild, and studies the control strategy of the vehicle’s active suspension. Firstly, the relationship matrix between control forces of actuators and virtual control forces that maintains the stabilization of the body attitude is derived, which is the Moore Penrose inverse matrix. Secondly, due to numerous nonlinearities, uncertain factors in the entire vehicle system and complex road conditions, a variable universe fuzzy controller for the stabilization of the vehicle body attitude is proposed. Finally, experiments are conducted with a three-axle heavy emergency rescue vehicle. Experimental results show that compared with the RMS of the vehicle with the passive suspension, the RMS of the pitch angle and the roll angle of the body with the active suspension are reduced by 27.04% and 25.84%, respectively; the RMS of the pitch angular acceleration, the roll angular acceleration and the vertical displacement acceleration of the body with the active suspension are reduced by 20.05%, 27.12% and 20.03%, respectively. The proposed controller has the high adaptability to the road input excitation, thereby improving the adaptability of vehicles to various road conditions effectively, maintaining the stable body attitude while driving, improving the “comfort” and the “safety” of passengers and on-board rescue equipment, and greatly improving the rescue efficiency of the vehicle.

Preview-based MPC for active suspension control of tank vehicle with lateral liquid sloshing suppression

This paper proposes an active suspension control method for tank vehicle, which can effectively suppress the lateral sloshing of liquid cargo, so as to improve the tank vehicle’s roll stability under the coupling excitation of non-structural road environment and additional moments of liquid cargo. Firstly, to describe the additional moments generated by the liquid cargo when the vehicle is laterally excited, a liquid sloshing equivalent mechanical pendulum model is put forward. Further, realizing the significant influence of road parameters on the roll stability of the vehicle, the liquid-vehicle-road coupling model is derived under the consideration of road curvature and cross-slope. Subsequently, based on the road information obtained in advance, a preview-based model predictive control (MPC) controller is designed to improve the roll stability of the tank vehicle and suppress the lateral sloshing of the liquid. Finally, the simulation is verified based on Matlab/Simulink under two complex scenarios, which indicates that the proposed control method can achieve better control effect compared with the traditional linear quadratic regulator (LQR) controller.

Road preview method for active suspension based on reinforcement learning

Abstract The pre-identification of impulse road surfaces, such as speed bumps, potholes, and manhole covers, can significantly enhance the performance of active suspension systems. However, existing current identification methods often fail to balance between low cost, high reliability, and precise speed. This study proposes an impulse road surface identification method based on a reinforcement learning network. First, a real dataset of impulse road surfaces was collected, with suspension dynamic responses serving as inputs and random road levels along with impulse road surface features as outputs. This data was used to develop a Random Forest Extreme Gradient Boosting (RF-XGBoost) network to recognize road surface information from vehicle dynamics. Next, a semantic segmentation network was employed to segment road surfaces during intelligent vehicle travel, using road surface information identified by the random forest network as the reward function for reinforcement learning. The reinforcement learning policy was pre-trained using the actual collected data. To validate the effectiveness of the proposed control strategy, a simulation environment was constructed in Prescan, where speed bumps, potholes, and manhole covers of varying heights and sizes were randomly arranged. The reinforcement learning-based road surface identification algorithm was implemented in Simulink, and a co-simulation was conducted with the CarSim vehicle model. The enhanced RF-XGBoost network effectively distinguishes between random and impulse road surfaces, achieving average recognition accuracies of 95.7% for random surfaces and 98.2% for impulse surfaces. During initial training iterations, the RL network exhibited lower accuracy; however, as training progressed, the accuracy for unfamiliar road surface features reached an average of 96.5%, and overall recognition accuracy improved by an average of 9%. The simulation results demonstrate that the proposed reinforcement learning identification method effectively acquires information about the road ahead, shows robustness and generalization, and provides crucial disturbance data for subsequent active suspension control.

Enhanced Fuzzy Logic Control for Active Suspension Systems via Hybrid Water Wave and Particle Swarm Optimization

Enhanced Fuzzy Logic Control for Active Suspension Systems via Hybrid Water Wave and Particle Swarm Optimization

Adaptive Event-Triggered Fuzzy Security Control for Active Suspensions of In-Wheel-Motor-Driven Electric Vehicles with Probabilistic Actuator Attacks

Adaptive Event-Triggered Fuzzy Security Control for Active Suspensions of In-Wheel-Motor-Driven Electric Vehicles with Probabilistic Actuator Attacks

Recent Advancements in Intelligent Control Techniques of Active Suspension System

Active suspension systems are designed to improve vehicle ride comfort and handling stability by adapting to changing road conditions, surpassing the capabilities of traditional passive and semi-active systems. The article explores intelligent control approaches in active suspension systems, focusing on three main strategies: FLC (Fuzzy Logic Control), Fuzzy PID Control, and MPC (Model Predictive Control). FLC is highlighted for its capacity to manage uncertainties and nonlinear vehicle dynamics using rule-based decision-making. Fuzzy PID Control builds upon traditional PID control by incorporating fuzzy logic, enabling real-time adjustments and improved adaptability to road conditions. This approach improves performance in complex systems by optimizing control gains, enhancing stability and comfort. MPC is noted for its predictive capability, which optimizes control actions based on future states, making it highly effective in multi-variable and constraint-heavy applications, despite its computational demands. While FLC and Fuzzy PID are simpler and responsive, MPC provides the advantage of precision in complex, high-dimensional scenarios. Reducing MPC’s computational complexity for real-time use, enhancing adaptability through hybrid controls along with optimizing energy use alongside ride comfort and stability is the future in intelligent control of active suspension.