Optimal State Space Control of DC Motor

Ruderman, Michael; Krettek, Johannes; Hoffmann, Frank; Bertram, Torsten

doi:10.3182/20080706-5-kr-1001.00977

Cited by 79 publications

(49 citation statements)

References 6 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The AXEM DC motor F9M2 [6], which has the characteristics shown in table 1, is considered for the simulation in this article. In reality, is not zero, and its value is considered as a disturbance to the system.…”

Section: Transfer Function and Block Diagrammentioning

confidence: 99%

Robust control system design for a brushed direct current motor using LabVIEW simulation loop

Khoury

Szemes

2018

MATEC Web Conf.

View full text Add to dashboard Cite

Abstract. This paper presents the design of a robust control system for a PMDC motor using LabVIEW software. At first, the mathematical equations of the motor were modelled and simulated. Then, a proportional integral controller (PI) was used as a speed controller, and to guarantee the robustness of the system against the disturbances, the PI parameters were tuned by applying a sensitivity analysis. Finally, a proportional gain (P) was used as a position controller to guide the system towards the desired angle. The simulation was performed using LabVIEW Simulation Loop, and the sensitivity analysis was carried using LabVIEW MathScript tool.

show abstract

Section: Transfer Function and Block Diagrammentioning

confidence: 99%

Robust control system design for a brushed direct current motor using LabVIEW simulation loop

Khoury

Szemes

2018

MATEC Web Conf.

View full text Add to dashboard Cite

show abstract

“…The active forces generated by the actuators which make the robot move can be expressed in the The active torque around the COM is calculated as defined in equation (13).…”

Section: Model Of Dynamics Subsystemmentioning

confidence: 99%

“…The closed loop reference tracking control system for the SSMR is described by equations (52) and (53). The control law for reference tracking LQR controller can be defined as equation(58) [13]:…”

Section: Linear Quadratic Regulator (Lqr)mentioning

confidence: 99%

Dynamics Based Control of a Skid Steering Mobile Robot

Elshazly

Abbas

Zyada

2016

Jurnal Ilmu Komputer dan Informasi

View full text Add to dashboard Cite

In this paper, development of a reduced order, augmented dynamics-drive model that combines both the dynamics and drive subsystems of the skid steering mobile robot (SSMR) is presented. A Linear Quadratic Regulator (LQR) control algorithm with feed-forward compensation of the disturbances part included in the reduced order augmented dynamics-drive model is designed. The proposed controller has many advantages such as its simplicity in terms of design and implementation in comparison with complex nonlinear control schemes that are usually designed for this system. Moreover, the good performance is also provided by the controller for the SSMR comparable with a nonlinear controller based on the inverse dynamics which depends on the availability of an accurate model describing the system. Simulation results illustrate the effectiveness and enhancement provided by the proposed controller. Keywords: Skid Steering Mobile Robots, Reduced order model, Linear Quadratic Regulator, FeedForward Compensation, Inverse Dynamics. AbstrakDalam paper ini, pengembangan reduced order, augmented model dynamics-drive yang menggabungkan kedua dinamika dan subsistem drive dari skid steering mobile robot (SSMR) ditampilkan. Sebuah algoritma kontrol Linear Quadratic Regulator (LQR) dengan kompensasi feed-forward dari disturbances part termasuk dalam reduced order augmented model dynamics-drive dirancang. Pengendali yang diusulkan memiliki banyak keuntungan seperti kesederhanaan dalam hal desain dan implementasi dibandingkan dengan skema kontrol nonlinear kompleks yang biasanya dirancang untuk sistem ini. Selain itu, kinerja yang baik juga disediakan oleh pengendali untuk SSMR sebanding dengan pengendali nonlinear berdasarkan dinamika inverse yang tergantung pada ketersediaan dari model yang akurat yang menggambarkan sistem. Hasil simulasi menggambarkan efektivitas dan peningkatan oleh pengendali yang diusulkan.

show abstract

“…General system identification methods [1][2][3][4] can be applied to DC motor model identification. In particular, various methods have been applied to DC motor parameter identification; that is, [5,6] used the algebraic identification method, [7] used the recursive least square method, [8] applied the inverse theory, [9] used the least square method, and [10] applied the moments method. Identified DC motor models are often subsequently used for controller design and/or optimization, for example, [6,9,11].…”

Section: Introductionmentioning

confidence: 99%

DC Motor Parameter Identification Using Speed Step Responses

2012

Modelling and Simulation in Engineering

View full text Add to dashboard Cite

Based on the DC motor speed response measurement under a step voltage input, important motor parameters such as the electrical time constant, the mechanical time constant, and the friction can be estimated. A power series expansion of the motor speed response is presented, whose coefficients are related to the motor parameters. These coefficients can be easily computed using existing curve fitting methods. Experimental results are presented to demonstrate the application of this approach. In these experiments, the approach was readily implemented and gave more accurate estimates than conventional methods.

show abstract

Optimal State Space Control of DC Motor

Cited by 79 publications

References 6 publications

Robust control system design for a brushed direct current motor using LabVIEW simulation loop

Robust control system design for a brushed direct current motor using LabVIEW simulation loop

Dynamics Based Control of a Skid Steering Mobile Robot

DC Motor Parameter Identification Using Speed Step Responses

Contact Info

Product

Resources

About