Design and Real Time Implementation of Sliding Mode and Adaptive Fuzzy Control on Quanser Gyroscope
01 Jan 2019-
TL;DR: This paper deals with practical implementation of sliding mode and direct adaptive fuzzy control on quanser gyroscope and shows how these control schemes are able to drive the error to zero and thus stabilize the plant about a given fixed point ensuring stability and boundedness of all the signals.
Abstract: 3-DoF Gyroscope is one of the fundamental building blocks of any guidance, navigation and control system. The physical properties exhibited by the gyroscope are relevant in the field of sea, air and space vehicles. Attitude control, momentum wheel control and satellite orientation are some of the applications of the 3-DoF gyroscope. While sliding mode control is one of the promiment variable structure control that has been used to solve a lot of nonlinear control problems, adaptive fuzzy belongs to the family of intelligent control that can solve some of the very complex nonlinear control problems where there is partial to no knowledge of the system or where there is a substantial dynamic variation in the plant. It can adapt itself over the domain of compact input set. Both the control schemes are able to drive the error to zero and thus stabilize the plant about a given fixed point ensuring stability and boundedness of all the signals. This paper deals with practical implementation of sliding mode and direct adaptive fuzzy control on quanser gyroscope. Practical results are shown to be in comformity with the simulation results.
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01 Jan 2022
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Book•
01 Jan 1991
TL;DR: Covers in a progressive fashion a number of analysis tools and design techniques directly applicable to nonlinear control problems in high performance systems (in aerospace, robotics and automotive areas).
Abstract: Covers in a progressive fashion a number of analysis tools and design techniques directly applicable to nonlinear control problems in high performance systems (in aerospace, robotics and automotive areas).
15,545 citations
Additional excerpts
...This in turn leads to the boundedness of errors |eji (t)| ≤ 2λ j−ri+1 i √ 2 γ1 α1 ([11]) IV....
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TL;DR: Design and analysis forVariable structure systems are surveyed in this paper and it is shown that advantageous properties result from changing structures according to this switching logic.
Abstract: Variable structure systems consist of a set of continuous subsystems together with suitable switching logic. Advantageous properties result from changing structures according to this switching logic. Design and analysis for this class of systems are surveyed in this paper.
5,076 citations
"Design and Real Time Implementation..." refers background in this paper
...Sliding Mode control is a very popular control technique that belongs to a class of control known as variable structure control[5]....
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15 Oct 1995
TL;DR: In this article, the authors present a model for dynamic control systems based on Adaptive Control System Design Steps (ACDS) with Adaptive Observers and Parameter Identifiers.
Abstract: 1. Introduction. Control System Design Steps. Adaptive Control. A Brief History. 2. Models for Dynamic Systems. Introduction. State-Space Models. Input/Output Models. Plant Parametric Models. Problems. 3. Stability. Introduction. Preliminaries. Input/Output Stability. Lyapunov Stability. Positive Real Functions and Stability. Stability of LTI Feedback System. Problems. 4. On-Line Parameter Estimation. Introduction. Simple Examples. Adaptive Laws with Normalization. Adaptive Laws with Projection. Bilinear Parametric Model. Hybrid Adaptive Laws. Summary of Adaptive Laws. Parameter Convergence Proofs. Problems. 5. Parameter Identifiers and Adaptive Observers. Introduction. Parameter Identifiers. Adaptive Observers. Adaptive Observer with Auxiliary Input. Adaptive Observers for Nonminimal Plant Models. Parameter Convergence Proofs. Problems. 6. Model Reference Adaptive Control. Introduction. Simple Direct MRAC Schemes. MRC for SISO Plants. Direct MRAC with Unnormalized Adaptive Laws. Direct MRAC with Normalized Adaptive Laws. Indirect MRAC. Relaxation of Assumptions in MRAC. Stability Proofs in MRAC Schemes. Problems. 7. Adaptive Pole Placement Control. Introduction. Simple APPC Schemes. PPC: Known Plant Parameters. Indirect APPC Schemes. Hybrid APPC Schemes. Stabilizability Issues and Modified APPC. Stability Proofs. Problems. 8. Robust Adaptive Laws. Introduction. Plant Uncertainties and Robust Control. Instability Phenomena in Adaptive Systems. Modifications for Robustness: Simple Examples. Robust Adaptive Laws. Summary of Robust Adaptive Laws. Problems. 9. Robust Adaptive Control Schemes. Introduction. Robust Identifiers and Adaptive Observers. Robust MRAC. Performance Improvement of MRAC. Robust APPC Schemes. Adaptive Control of LTV Plants. Adaptive Control for Multivariable Plants. Stability Proofs of Robust MRAC Schemes. Stability Proofs of Robust APPC Schemes. Problems. Appendices. Swapping Lemmas. Optimization Techniques. Bibliography. Index. License Agreement and Limited Warranty.
4,378 citations
"Design and Real Time Implementation..." refers background or methods in this paper
...It has been proved in [3] that adaptive law Eq(20) cannot ensure boundedness of parameter θ̃ in presence of approximation errors....
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...We now bring gradient descent algorithm[3] to minimize the cost function Eq(16)....
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...So to overcome this problem and improve robustness of the control scheme we add σ term[3] to the above Eq(20)....
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Book•
01 Feb 1994TL;DR: This paper presents a meta-analysis of the design and stability analysis of fuzzy identifiers of nonlinear dynamic systems fuzzy adaptive filters of adaptive fuzzy controllers using input-output linearization concepts.
Abstract: Description and analysis of fuzzy logic systems training of fuzzy logic systems using back-propagation training of fuzzy logic systems using orthogonal least squares training of fuzzy logic systems using a table-lookup scheme training of fuzzy logic systems using nearest neighbourhood clustering comparison of adaptive fuzzy systems with artificial neural networks stable indirect adaptive fuzzy control of nonlinear systems stable direct adaptive fuzzy control of nonlinear systems design of adaptive fuzzy controllers using input-output linearization concepts design and stability analysis of fuzzy identifiers of nonlinear dynamic systems fuzzy adaptive filters.
2,455 citations
"Design and Real Time Implementation..." refers methods in this paper
...A fuzzy system of the form mentioned above can approximate continuous nonlinear functions as proved in [2]....
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Book•
01 Jun 2013
TL;DR: The sliding mode control and observation (SOMO) approach has proven to be effective in dealing with complex dynamical systems affected by disturbances, uncertainties and unmodeled dynamics as discussed by the authors.
Abstract: The sliding mode control methodology has proven effective in dealing with complex dynamical systems affected by disturbances, uncertainties and unmodeled dynamics. Robust control technology based on this methodology has been applied to many real-world problems, especially in the areas of aerospace control, electric power systems, electromechanical systems, and robotics. Sliding Mode Control and Observation represents the first textbook that starts with classical sliding mode control techniques and progresses toward newly developed higher-order sliding mode control and observation algorithms and their applications.The present volume addresses a range of sliding mode control issues, including:*Conventional sliding mode controller and observer design*Second-order sliding mode controllers and differentiators*Frequency domain analysis of conventional and second-order sliding mode controllers*Higher-order sliding mode controllers and differentiators*Higher-order sliding mode observers *Sliding mode disturbance observer based control *Numerous applications, including reusable launch vehicle and satellite formation control, blood glucose regulation, and car steering control are used as case studiesSliding Mode Control and Observation is aimed at graduate students with a basic knowledge of classical control theory and some knowledge of state-space methods and nonlinear systems, while being of interest to a wider audience of graduate students in electrical/mechanical/aerospace engineering and applied mathematics, as well as researchers in electrical, computer, chemical, civil, mechanical, aeronautical, and industrial engineering, applied mathematicians, control engineers, and physicists. Sliding Mode Control and Observation provides the necessary tools for graduate students, researchers and engineers to robustly control complex and uncertain nonlinear dynamical systems. Exercises provided at the end of each chapter make this an ideal text for an advanced coursetaught in control theory.
1,774 citations
"Design and Real Time Implementation..." refers background in this paper
...SMC is inherently robust in nature as it can deal with known uncertainities and variations in plant model[4], [10]....
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