Design and implementation of adaptive neuro-fuzzy inference system for the control of an uncertain ball and beam apparatus

Saleem, Muhammad Mubasher; Shahid, Muhammad Laiq Ur Rahman; Nouman, Ahmed; Zaki, Hammad; Tariq, Muhammad Atiq Ur Rehman

doi:10.22581/muet1982.2202.17

Cited by 7 publications

(6 citation statements)

References 22 publications

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“…The equation which describes the dynamics of the system is obtained by using Euler Lagrange equation based on the energy balance of the system as follows [22,23]:…”

Section: Mathematical Model Of the Ball And Beam Systemmentioning

confidence: 99%

Performance Comparison of the Ball and Beam System using Linear Quadratic Regulator Controller

Umar¹,

Mu’azu²,

Haruna³

et al. 2023

PID Control for Linear and Nonlinear Industrial Processes

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This paper proposes the performance comparison of a linear quadratic regulator (LQR) controller for the ball and beam system (BBS). The BBS is a standard benchmark control system, which has two degree-of-freedom (2 DOF). It is an open loop and a highly nonlinear unstable system. This makes its parameter difficult to be estimated accurately, hence designing a controller for it is a challenging task. MatheThe BBS was modelled using Euler–Lagrange modeling technique, while the LQR controller was used for the stabilization of the ball on the beam. Simulation was done in MATLAB/Simulink 2022b environment, and the results simulated showed that for the two weighting matrices (Q and R), the state weighting matrix had a higher penalty on the ball displacement, ball velocity, beam angle, and beam angular velocity at lower values of Q. For the state weighting matrix had a better effect of penalty performance on the BBS with lower values. Also, as the diagonal element of the state weighting matrix Q increases from 0.1 to 20, the values of the optimal controller K increase, the reduced Ricatti matrix P increases, and the estimated eigenvalues E reduce. This implies that the ball displacement, ball velocity, beam angle, and beam angular velocity are better at lower values of Q.

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“…The equation which describes the dynamics of the system is obtained by using Euler Lagrange equation based on the energy balance of the system as follows [22,23]:…”

Section: Mathematical Model Of the Ball And Beam Systemmentioning

confidence: 99%

Performance Comparison of the Ball and Beam System using Linear Quadratic Regulator Controller

Umar¹,

Mu’azu²,

Haruna³

et al. 2023

PID Control for Linear and Nonlinear Industrial Processes

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“…Therefore, it is important to ensure that the controller designed using G om to be robust enough to control G o . As per [22], the rule to achieve closed-loop robust stability is…”

Section: System Robustnessmentioning

confidence: 99%

“…Singapore: Springer; 2023. pp. 37-50 [22] Morari M, Zafiriou E. Robust Process Control. Upper Saddle River: PTR Prentice Hall; 2002 [23] Siddiqui MA, Anwar MN, Laskar SH.…”

Section: Motivation For This Bookmentioning

confidence: 99%

PID Control for Linear and Nonlinear Industrial Processes

2023

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Meet the editorsDr. Mohammad Shamsuzzoha holds a Ph.D. in chemical engineering and has recently completed an MBA in entrepreneurship and leadership. He conducts research and provides process engineering, modelling and simulation support to clients in a wide range of process industries. His previous position was a Senior Process Engineering Specialist for ADNOC Refining Co., where he focused on technical support, including process modelling and simulation, modification, and design for refinery units, and associated gas plants. He is a pioneering researcher and practicing engineer with work experience in academia, industry, and research and development in multicultural environments. Dr. G. Lloyds Raja was born in Kanyakumari, India. He received his bachelor's and master's degrees in engineering from Anna University. He was awarded a Ph.D. from the Indian Institute of Technology Patna. He was briefly employed as a postdoctoral researcher at the

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“…Two testing methodologies have been designed to simulate both ideal conditions and real-world hardware limitations, and a MATLAB-based simulation environment serves for the evaluation. Key performance indicators such as percentage overshoot, rise time to a 5-second threshold, settling time, and displacement are considered [23]- [25]. It is also crucial to understand the distinguishing elements between Type-1 and interval Type-2 fuzzy logic systems.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Takagi-Sugeno-Kang and Madani Algorithms in Type-1 and Interval Type-2 Fuzzy Control for Self-Balancing Wheelchairs

Kiew-ong-art,

Chotikunnan,

Wongkamhang

et al. 2023

IJRCS

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This study examines the effectiveness of four different fuzzy logic controllers in self-balancing wheelchairs. The controllers under consideration are Type-1 Takagi-Sugeno-Kang (TSK) FLC, Interval Type-2 TSK FLC, Type-1 Mamdani FLC, and Interval Type-2 Mamdani FLC. A MATLAB-based simulation environment serves for the evaluation, focusing on key performance indicators like percentage overshoot, rise time, settling time, and displacement. Two testing methodologies were designed to simulate both ideal conditions and real-world hardware limitations. The simulations reveal distinct advantages for each controller type. For example, Type-1 TSK excels in minimizing overshoot but requires higher force. Interval Type-2 TSK shows the quickest settling times but needs the most force. Type-1 Mamdani has the fastest rise time with the lowest force requirement but experiences a higher percentage of overshoot. Interval Type-2 Mamdani offers balanced performance across all metrics. When a 2.7 N control input cap is imposed, Type-2 controllers prove notably more efficient in minimizing overshoot. These results offer valuable insights for future design and real-world application of self-balancing wheelchairs. Further studies are recommended for the empirical testing and refinement of these controllers, especially since the initial findings were limited to four-wheeled self-balancing robotic wheelchairs.

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Design and implementation of adaptive neuro-fuzzy inference system for the control of an uncertain ball and beam apparatus

Cited by 7 publications

References 22 publications

Performance Comparison of the Ball and Beam System using Linear Quadratic Regulator Controller

Performance Comparison of the Ball and Beam System using Linear Quadratic Regulator Controller

PID Control for Linear and Nonlinear Industrial Processes

Comparative Study of Takagi-Sugeno-Kang and Madani Algorithms in Type-1 and Interval Type-2 Fuzzy Control for Self-Balancing Wheelchairs

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