Global optimization control for nonlinear full‐car active suspension system with multi‐performances

Abstract

In this article, based on feedback linearized approach and linear quadratic regulator optimization control approach using particle swarm optimization, the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization, controllable convergence rate, improved suspension and globally exponential stability multi-performances of highly nonlinear multi-input multi-output full-car uncertain system are simultaneously achieved without applying any nonlinear function approximator including neural network approach and fuzzy logic approach. The article proposes a new method to achieve the optimal control matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally determined via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This study has first proposed the convergence rate formula of the nonlinear system response as the fitness function of LQR approach by using PSO optimization algorithm to take the place of the conventional trial and error method and locally Jacobian linearized approach under the guarantee of globally exponential stability. The simulation results show how the full-car system can be controlled optimally and satisfactorily and confirm the superiority of the proposed method.

Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm

“…The values of individuals in the population are assigned to the weighting coefficients q 1 , q 2 , and q 3 of the LQR controller in turn. The optimal gain feedback matrix K is solved by Riccati Equation (10), and then the optimal control force U a (t) is solved by Formula (12). Then the control force is acted on the active suspension model, and the RMS values of the three performance indexes are calculated; 3.…”

“…Many experts have conducted many studies on the algorithms, including adaptive PID control [1], fuzzy control [2], sliding mode control [3], neural networks [4], reinforcement learning [5], etc. Besides, the LQR theory, as one of the earliest and most mature control algorithms in modern control theory, has been studied deeply [6][7][8][9][10].…”

Extending the LQR to the design of PID type‐ii and type‐iii control loops

“…Modern optimal control theory has a rich set of analytical tools to design control strategies satisfying desirable characteristics of the excursion of the system states according to the designer's specifications in an optimal manner [19]. The LQR is one such design methodology whereby quadratic performance indices involving the control signal and the state variables are minimized in an optimal fashion [20][21][22][23]. It has a very nice stability property, that is, if the process is of single-input and single-output, then the control system has at least the phase margin of 60 • and the gain margin of infinity [24].…”