Active control of a nonlinear suspension with output constraints and variable-adaptive-law control

Abstract

A variable-adaptive-law control algorithm for application to common problems like multi-objective control, actuator output constraints, and suboptimal adaptive laws is proposed in this paper. The multi-objective control problem of a nonlinear suspension is converted to the constrained stability problem of a sprung mass using a quarter nonlinear-suspension model. A variable-adaptive-law controller is then used, along with feedback from the output error, and considering the constraints of the actuator output. The controller modifies the adaptive law to reduce the active control force and restores it to the unsaturated zone. This ensures that the suspension system is always in a controlled state when the output saturation occurs. The controller was simulated for the following two cases: (i) a bump road and (ii) a C-grade road. The analysis is verified by experiments in the end.

Dynamic Responses of 8-DoF Vehicle with Active Suspension: Fuzzy-PID Control

“…Classic PID control is a control algorithm that combines proportion, integration, and differentiation in a closed-loop system, which can automatically correct the control system accurately and quickly. Modern control algorithms include optimal control [17], adaptive control [18], robust control [19], and sliding mode variable structure control [20]. Optimal control is to find out the allowable control rule, make the dynamic system transfer from the initial state to a required terminal state, and ensure that a required performance index reaches the minimum (or large).…”

Explicit model predictive control of semi-active suspension systems with magneto-rheological dampers subject to input constraints

“…In addition, although various nonlinear control methods such as sliding model control (SMC; Dixit and Buckner, 2005) and the adaptive control algorithm (Nugroho et al, 2014; Song et al, 2005) have shown that these controllers can improve the control performance and ensure stability against external disturbances and parameter uncertainties (Choi et al, 2016), the constraints of the control input and output responses have not been considered. Recently, a clipped linear-quadratic (LQ) optimal control (Brezas et al, 2015) was developed for a semi-active suspension, along with an adaptive law control method (Yao et al, 2018) for an active suspension system, where the control input constraint is treated using a saturation function. However, some state responses became unstable because the saturation function associated with the control input was no longer rigorous to appropriately handle the constraints of the damping force.…”