LQG controller design applied to a pneumatic stewart-gough platform

Grewal, Karmjit Singh; Dixon, Roger; Pearson, J.

doi:10.1007/s11633-012-0615-7

Cited by 38 publications

(14 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is just here to assume that the maximum allowable suspension displacement is max = ±0.05 m, and that the maximum value of controllable force is max = ±500 N. Analytic hierarchy process (AHP) is a multicriteria decision aiding method based on a solid axiomatic foundation in which the general weight coefficient of each performance evaluation indicator related to ride comfort and handing stability can be obtained based on the objective and subjective weight proportion coefficients [17]. According to the computation progress based on AHP [22], the weight coefficients of each performance evaluation indicator are initially determined as 1 = 1, 2 = 5.3, 3 = 146032.8, 4 = 102510.9, 5 = 1065.8, and 6 = 1207.8. Therefore, to accelerate the convergence of GA, the initial search scope for each coefficient is defined as follows which is based on the results of AHP method; that is, the search scope of 1 is set as In order to obtain the optimal weight coefficients of each performance evaluation indicator for the LQG controller, the corresponding weight coefficients are calculated as 1 = 1,…”

Section: Numerical Examples and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Design of LQG Controller for Active Suspension without Considering Road Input Signals

Pang

Chen

et al. 2017

Shock and Vibration

View full text Add to dashboard Cite

As the road conditions are completely unknown in the design of a suspension controller, an improved linear quadratic and Gaussian distributed (LQG) controller is proposed for active suspension system without considering road input signals. The main purpose is to optimize the vehicle body acceleration, pitching angular acceleration, displacement of suspension system, and tire dynamic deflection comprehensively. Meanwhile, it will extend the applicability of the LQG controller. Firstly, the half-vehicle and road input mathematical models of an active suspension system are established, with the weight coefficients of each evaluating indicator optimized by using genetic algorithm (GA). Then, a simulation model is built in Matlab/Simulink environment. Finally, a comparison of simulation is conducted to illustrate that the proposed LQG controller can obtain the better comprehensive performance of vehicle suspension system and improve riding comfort and handling safety compared to the conventional one.

show abstract

Section: Numerical Examples and Discussionmentioning

confidence: 99%

“…When designing the LQG controller, it is necessary to consider all the performance evaluation indicators in order to achieve better comprehensive vehicle dynamics performance. So in this paper, the comprehensive performance evaluation indicator denoted by is defined as follows [21,22]:…”

Section: The Conventional Lqg Controllermentioning

confidence: 99%

Design of LQG Controller for Active Suspension without Considering Road Input Signals

Pang

Chen

et al. 2017

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…Quad-rotor, another example of underactuated strongly coupled nonlinear system is presented in [24] in which the adaptive backstepping sliding mode approach is used for the trajectory tracking control. The optimal control design method of linear quadratic Gaussian (LQG), which is a combination of a linear quadratic estimator (LQE) (i.e., Kalman filter) and a linear quadratic regulator (LQR), has been used for optimal control of pneumatic Stewart-Gough platform in [25]. In recent trends even the various advance control approaches [8][9][10][11][12][13][14][15][16][17][18][19][20]24] are developing and being tried for many dynamical systems control, the simplicity of control algorithms along with the fulfillment of control objectives is further desired.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of Nonlinear Inverted Pendulum System Using PID Controller and LQR: Performance Analysis Without and With Disturbance Input

Prasad

Tyagi

Gupta

2014

Int. J. Autom. Comput.

188

101

View full text Add to dashboard Cite

Abstract:Linear quadratic regulator (LQR) and proportional-integral-derivative (PID) control methods, which are generally used for control of linear dynamical systems, are used in this paper to control the nonlinear dynamical system. LQR is one of the optimal control techniques, which takes into account the states of the dynamical system and control input to make the optimal control decisions. The nonlinear system states are fed to LQR which is designed using a linear state-space model. This is simple as well as robust. The inverted pendulum, a highly nonlinear unstable system, is used as a benchmark for implementing the control methods. Here the control objective is to control the system such that the cart reaches a desired position and the inverted pendulum stabilizes in the upright position. In this paper, the modeling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using PID controller and LQR have been presented for both cases of without and with disturbance input. The Matlab-Simulink models have been developed for simulation and performance analysis of the control schemes. The simulation results justify the comparative advantage of LQR control method.

show abstract

“…The authors of [4], [5] consider the practical application of control to a pneumatically actuated Stewart-Gough platform with 6 Degrees of Freedom (DOF). In their experimental set-up, the flow of the air supply is governed by the servo valve that gives a flow into each of the cylinder chambers, which is proportional to the voltage applied.…”

Section: Introductionmentioning

confidence: 99%