Multi-Objective Sliding Mode Control on Vehicle Cornering Stability with Variable Gear Ratio Actuator-Based Active Front Steering Systems

Ma, Xinbo; Wong, Pak Kin; Zhao, Jing; Xie, Zhengchao

doi:10.3390/s17010049

Cited by 18 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All these steps have led to the conclusion that this approach leads to smaller errors because there is no need to rely on the results of predictive models [ 31 , 32 , 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Considerations for Determining the Coefficient of Inertia Masses for a Tracked Vehicle

Alexa

Coropeţchi

Vasile

et al. 2020

Sensors

View full text Add to dashboard Cite

The purpose of the article is to present a point of view on determining the mass moment of inertia coefficient of a tracked vehicle. This coefficient is very useful to be able to estimate the performance of a tracked vehicle, including slips in the converter. Determining vehicle acceleration plays an important role in assessing vehicle mobility. Additionally, during the transition from the Hydroconverter to the hydro-clutch regime, these estimations become quite difficult due to the complexity of the propulsion aggregate (engine and hydrodynamic transmission) and rolling equipment. The algorithm for determining performance is focused on estimating acceleration performance. To validate the proposed model, tests were performed to determine the equivalent reduced moments of inertia at the drive wheel (gravitational method) and the main components (three-wire pendulum method). The dynamic performances determined during the starting process are necessary for the validation of the general model for simulating the longitudinal dynamics of the vehicle. Finally, the differential and algebraic equations of the virtual model approximate more accurately the actual process of the operation of the vehicle. The virtual model, through the data obtained from the simulation process, allows for the determination, indirectly, of the variation of the mass moment of inertia coefficient and its expression of approximation.

show abstract

“…All these steps have led to the conclusion that this approach leads to smaller errors because there is no need to rely on the results of predictive models [ 31 , 32 , 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, these vehicles can be deployed to perform tasks in unstructured environments, being forced to move and change position at speeds characterized by a strong start. On the other hand, different obstacles can appear, which slows the dynamics of the movement [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Considerations for Determining the Coefficient of Inertia Masses for a Tracked Vehicle

Alexa

Coropeţchi

Vasile

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The former does not need detailed knowledge of vehicle dynamics, for example, proportional–integral–derivative (PID), 6,7 fuzzy control, 8–10 and neural network. 11,12 The latter relies on the accurate dynamic model of the vehicle to build controller, such as sliding mode control, 13–15 H-infinity, 16,17 feedback linearization, 18,19 and model predictive control (MPC). 20–23 In the steering control systems, control inputs commonly consist of the steering angle and the steering torque.…”

Section: Introductionmentioning

confidence: 99%

Steering control based on model predictive control for obstacle avoidance of unmanned ground vehicle

Zhao

Cao

et al. 2020

Measurement and Control

View full text Add to dashboard Cite

In this paper, a strategy based on model predictive control consisting of path planning and path tracking is designed for obstacle avoidance steering control problem of the unmanned ground vehicle. The path planning controller can reconfigure a new obstacle avoidance reference path, where the constraint of the front-wheel-steering angle is transformed to formulate lateral acceleration constraint. The path tracking controller is designed to realize the accurate and fast following of the reconfigured path, and the control variable of tracking controller is steering angle. In this work, obstacles are divided into two categories: static and dynamic. When the decision-making system of the unmanned ground vehicle determines the existence of static obstacles, the obstacle avoidance path will be generated online by an optimal path reconfiguration based on direct collocation method. In the case of dynamic obstacles, receding horizon control is used for real-time path optimization. To decrease online computation burden and realize fast path tracking, the tracking controller is developed using the continuous-time model predictive control algorithm, where the extended state observer is combined to estimate the lumped disturbances for strengthening the robustness of the controller. Finally, simulations show the effectiveness of the proposed approach in comparison with nonlinear model predictive control, and the CarSim simulation is presented to further prove the feasibility of the proposed method.

show abstract

“…These nonlinear factors introduce several problems, such as tracking errors, limit cycles and poor stick-slip motion. In order to address these issues, various control schemes are proposed to control the position of the EMA system, including PID control algorithms [4,5,6,7],fuzzy control [8,9], intelligent algorithms [10], sliding mode control [11,12,13], the ADRC algorithm [14], robust control [15], active control [16], model-based prognostic algorithms [17], and compensation control [18,19,20]. Among these algorithms, PID is the most widely used because of its simplicity and reliability, though it suffers from poor robustness and weak anti-interference ability.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Control with PID–Improved Sliding Mode for Flat-Top of Missile Electromechanical Actuator Systems

Zhou

Mao

Zhang

et al. 2018

Sensors

View full text Add to dashboard Cite

Electromechanical actuator (EMA) systems are widely employed in missiles. Due to the influence of the nonlinearities, there is a flat-top of about 64 ms when tracking the small-angle sinusoidal signals, which significantly reduces the performance of the EMA system and even causes the missile trajectory to oscillate. Aiming to solve these problems, this paper presents a hybrid control for flat-top situations. In contrast to the traditional PID or sliding mode controllers that missiles usually use, this paper utilizes improved sliding mode control based on a novel reaching law to eliminate the flat-top during the steering of the input signal, and utilizes the PID control to replace discontinuous control and improve the performance of EMA system. In addition, boundary layer and switching function are employed to solve the high-frequency chattering problem caused by traditional sliding mode control. Experiments indicate that the hybrid control can evidently reduce the flat-top time from 64 ms to 12 ms and eliminate the trajectory limit cycle oscillation. Compared with PID controllers, the proposed controller provides better performance—less chattering, less flat-top, higher precision, and no oscillation.

show abstract

Multi-Objective Sliding Mode Control on Vehicle Cornering Stability with Variable Gear Ratio Actuator-Based Active Front Steering Systems

Cited by 18 publications

References 14 publications

Considerations for Determining the Coefficient of Inertia Masses for a Tracked Vehicle

Considerations for Determining the Coefficient of Inertia Masses for a Tracked Vehicle

Steering control based on model predictive control for obstacle avoidance of unmanned ground vehicle

A Hybrid Control with PID–Improved Sliding Mode for Flat-Top of Missile Electromechanical Actuator Systems

Contact Info

Product

Resources

About