Neural Fuzzy Control of Ball and Beam System

Kharola, Ashwani; Patil, Pravin P.

doi:10.4018/ijeoe.2017040104

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…Typically, a backpropagation-type learning algorithm is applied to identify fuzzy rules and learn the membership functions of the fuzzy reasoning. Several studies 4,30–33,118–120 have shown that neural fuzzy systems can be successfully used to control underactuated systems.…”

Section: Control Methods Based On Fuzzy Systems For Underactuated Sysmentioning

confidence: 99%

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A survey on the application of fuzzy systems for underactuated systems

Huang

Ralescu

Gao

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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Underactuated systems represent a class of mechanical systems with fewer independent control actuators than degrees of freedom. The control problem of underactuated systems is one of the most challenging in the theory of nonlinear control because such systems exhibit strong coupling and highly nonlinear characteristics. Fuzzy systems have been successfully applied to control underactuated systems in both theoretical analysis and practical application. This survey describes the model and application of underactuated systems and classifies fuzzy systems in a straightforward manner. Moreover, this study analyzes problems facing conventional control methods and presents the development of fuzzy systems to solve these problems.

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Section: Control Methods Based On Fuzzy Systems For Underactuated Sysmentioning

confidence: 99%

“…The neuro-fuzzy system’s parameter was updated using a critic module. In Kharola and Patil, 120 the authors applied a neural fuzzy system to control a BAB system.…”

Section: Control Methods Based On Fuzzy Systems For Underactuated Sysmentioning

confidence: 99%

A survey on the application of fuzzy systems for underactuated systems

Huang

Ralescu

Gao

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…In [29], the equations of the ball and beam system in [28] were scaled according to time and torque. In the past few years, more and more new control methods have been used to control a ball and beam system, such as fuzzy logic, neural networks, robust control and backstepping [30][31][32][33]. A comparative study was conducted for models of ball and beam systems in [34].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Controller Based on Interconnection and Damping Assignment Passivity-Based Control for Underactuated Mechanical Systems: Application to the Ball and Beam System

Liu,

Shao,

Liu

et al. 2023

Actuators

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In this paper, an adaptive technology and the interconnection and damping assignment passivity-based control method are combined to solve the stabilization problem for underactuated mechanical systems with uncertainties (including matched and unmatched). These uncertainties include unknown friction coefficients and unknown terms in kinetic energy and potential energy. A novel adaptive interconnection and damping assignment passivity-based control scheme is proposed and an adaptive stabilization controller is designed to make the closed-loop system locally stable. Verification is conducted on the ball and beam system. The locally asymptotic stability is demonstrated using the LaSalle’s invariance principle and approximate linearization. The effectiveness of the proposed control law is verified through numerical simulations.

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“…This approach however necessitates the utilization of sophisticated tools and hardware, potentially limiting its applicability to smaller or less complex control systems. Previous studies have also explored the use of nonlinear controllers such as neural networks [22]- [25], fuzzy logic [26]- [29], backstepping [30], deep reinforcement learning [31], and passivity-based control [32], as alternative strategies for stabilizing the BnB system.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Hybrid PSO-WOA-Based Intelligent PID and State-Feedback Control for Ball and Beam Systems

Ahmad

2023

IEEE Access

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The ball and beam (BnB) system serves as a benchmark in control engineering as it provides a foundational concept applicable to addressing stabilization challenges of various underactuated nonlinear systems. This includes tasks like maintaining the balance of goods carried by mobile robots and controlling the attitude of unmanned aerial vehicles. In this study, the focus is on enhancing control optimization strategies for BnB systems that take into account inherent nonlinearities arising from actuator constraints and state measurements. The work introduces a novel intelligent control approach, termed hybrid PSO-WOA, which combines Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) to automate optimal parameter search for proportional-integral-derivative (PID) and state feedback (SF) controllers. The collaborative technique between PSO and WOA is formulated to strike a balance between exploration and exploitation phases, and to mitigate premature convergence risks due to the system's complexities. Additionally, three control schemes, namely cascade PID-PID, cascade PID-SF, and cascade PID-observer are introduced, each with tailored cost functions for optimization through the hybrid PSO-WOA algorithm, accommodating both measurable and unmeasurable state scenarios. Simulation results consistently demonstrate the superior performance of the hybrid approach compared to individual PSO and WOA methods, as well as conventional PID and linear quadratic regulator approaches. Notable, the hybrid approach exhibits a significant improvement in error metrics, reducing integral-time absolute error by 18.99%, integral squared error by 35.37%, and steady-state error by 92.86%. This substantial enhancement suggests promising directions for future research in automated control parameter tuning for underactuated nonlinear systems.

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Neural Fuzzy Control of Ball and Beam System

Cited by 6 publications

References 30 publications

A survey on the application of fuzzy systems for underactuated systems

A survey on the application of fuzzy systems for underactuated systems

An Adaptive Controller Based on Interconnection and Damping Assignment Passivity-Based Control for Underactuated Mechanical Systems: Application to the Ball and Beam System

Modeling and Hybrid PSO-WOA-Based Intelligent PID and State-Feedback Control for Ball and Beam Systems

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