Tracking differentiator based back-stepping control for valve-controlled hydraulic actuator system

Wang

Proceedings of the Institution of Mechanical Engineers, Part I:

et al. 2023

Self Cite

In this article, a linear quadratic regulator (LQR)-based sliding model control strategy was proposed for the commercial vehicle seat suspension system. First, the multiple degrees of freedom mechanical dynamics model of a quarter suspension system was built. Then, the LQR force command planner was designed based on mechanical dynamics to ensure driver comfort. Second, considering the mechanical dynamics augmented with hydraulic actuator dynamics, a proportional-integral sliding mode control strategy was developed to track the reference force, which was calculated by the LQR force command planner in real time. Taking the problems of noise disturbances and unavailable full states feedback into consideration, the Kalman filter and tracking differentiator were designed and integrated into the control algorithm. Finally, the quarter suspension AMESim model was built, and the proposed control strategy was implemented and verified in the AMESim-Matlab/Simulink co-simulation environment. Comprehensive simulations were carried out. The simulation results, both from time domain and frequency domain perspective, indicated that the seat ride comfort can be effectively improved with the proposed method.

“…According to the lemma, 19 with choosing s 1 ) s 2 ) 1 and s 3 ) s 4 ) 1, the substitutes v 1 , v 2 , v 3 , and v 4 produced by TD can approximate to x2 , _…”

Section: Kf and Td Designmentioning

confidence: 99%

Section: Controller Designmentioning

confidence: 99%

LQR force command planning–based sliding mode control for active suspension system

Wang

Proceedings of the Institution of Mechanical Engineers, Part I:

et al. 2023

Self Cite

“…In recent years, the research on nonlinear systems with unmodeled dynamics has gained widespread attention. This is because such systems are prevalent and of practical importance in various fields [1][2][3]. Similarly, backstepping method is also widely used in the study of nonlinear problems because of its many advantages [4] and [5].…”

Section: Introductionmentioning

confidence: 99%

Output feedback command filtered fuzzy controller design for uncertain strict-feedback nonlinear systems with unmodeled dynamics and event-triggered strategy

Sun,

Chen,

Gao

et al. 2024

Eng. Res. Express

An adaptive control problem is considered for a class of nth-order uncertain nonlinear systems with unmodeled dynamics. Compared with the traditional backstepping method, this article improves the systems performance by introducing command filtered and event-triggered. The former primarily enhances the flexibility of controller design, while the latter employs a variable threshold triggeringmechanism to achieve savings in communication resources. Simultaneously, the estimation error is compensated by the introduced hyperbolic tangent function. In addition, fuzzy logic system (FLS) is primarily used for observer design and approximation of unknown functions. The conclusion that the closed-loop signals are bounded is obtained through stability analysis. Finally, the numerical simulation results provide a comparative validation of the effectiveness of the proposed control scheme.

“…The tracking differentiator contains a filter factor parameter and a speed factor parameter, which are difficult to adjust. If the two parameters are not selected properly, the output signal of the tracking differentiator will diverge [23]. In contrast, the Kalman filter is a recursive filter, which has the advantages of small computation, low storage and high real-time performance and is suitable for long-term online estimation [24].…”

Section: Introductionmentioning

confidence: 99%

Two-Degrees-of-Freedom PID Control with Kalman Filter for Engraving Machine System

Dong,

Hao,

Shao

et al. 2023

Actuators

For an engraving machine system with input dynamic disturbance and output random measurement noise, a two-degrees-of-freedom proportional integral derivative (2-DOF PID) control method based on the Kalman filter is firstly proposed in this paper, which can effectively reject the input disturbance and ensure the set point tracking performance of the controller. The 2-DOF controller consists of a disturbance rejection controller and a set point tracking controller. The disturbance rejection controller is composed of a PID controller based on a disturbance observer and expectation model. The parameters of the set point tracking controller are tuned using a differential evolution algorithm (DE), and the cumulative absolute error value (IAE) is used as the fitness function of the DE algorithm, which can improve the rationality of intelligent parameter tuning. In addition, the Kalman filter is also applied to deal with the output noise to suppress the influence of the output measurement uncertainty. Finally, compared with existing algorithms, the feasibility and superiority of the proposed algorithm are verified using numerical simulation and an experimental test.