Self-organizing fuzzy optimal control for under-actuated systems

Zhang, Haiyun; Wang, Jin; Lu, Guodong

doi:10.1177/0959651814533681

Cited by 16 publications

(20 citation statements)

References 39 publications

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“…In order to synthesize a stable self-tuning LQR, the statefeedback gains are dynamically adjusted after every sampling interval by using an indirect hierarchical adaptive modulation scheme. Where in, the coefficients of state-penalty matrix, q x , are modified online using phase-driven nonlinear scaling functions [25]. This arrangement obviates the requirement of empirically tuning the q x under different operating conditions.…”

Section: Proposed Adaptive Controller Designmentioning

confidence: 99%

“…However, it is quite hard to accurately define the statedependent coefficient matrices that fully realize the system's nonlinear characteristics. The technique involving online dynamic adjustment of weighting-factors of the optimal controller's cost-function, to indirectly self-tune the state-feedback gains, has garnered a lot of attention recently [25].…”

Section: Introductionmentioning

confidence: 99%

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Indirect Adaptive State-Feedback Control of Rotary Inverted Pendulum Using Self-Mutating Hyperbolic-Functions for Online Cost Variation

Saleem

Mahmood-ul-Hasan

2020

IEEE Access

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This paper presents the development of an indirect adaptive state-feedback controller to improve the disturbance-rejection capability of under-actuated multivariable systems. The ubiquitous Linear-Quadratic-Regulator (LQR) is employed as the baseline state-feedback controller. Despite its optimality, the LQR lacks robustness against parametric uncertainties. Hence, the main contribution of this paper is to devise and retrofit the LQR with a stable online gain-adjustment mechanism that dynamically adjusts the state weighting-coefficients of LQR's quadratic cost-function via state-error dependent nonlinear-scaling functions. An original self-mutating phase-based adaptive modulation scheme is systematically formulated in this paper to self-adjust the state weighting-coefficients. The scheme employs pre-calibrated secanthyperbolic-functions whose waveforms are dynamically reconfigured online based on the variations in magnitude and polarity of state-error variables. This augmentation dynamically alters the solution of the Riccati-Equation which modifies the state-feedback gains online. The proposed adaptation flexibly manipulates the system's control effort as the response converges to or diverges from the reference. The efficacy of proposed adaptive controller is validated by conducting hardware-in-the-loop experiments to vertically stabilize the QNET 2.0 Rotary Pendulum system. As compared to the standard LQR, the proposed adaptive controller renders rapid transits in system's response with improved damping against oscillations, while maintaining its asymptotic-stability, under bounded exogenous disturbances. INDEX TERMS Hyperbolic functions, linear quadratic regulator, cost-function, rotary inverted pendulum, self-tuning control, state weighting-coefficients.

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Section: Proposed Adaptive Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Indirect Adaptive State-Feedback Control of Rotary Inverted Pendulum Using Self-Mutating Hyperbolic-Functions for Online Cost Variation

Saleem

Mahmood-ul-Hasan

2020

IEEE Access

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“…The Rotary-Inverted-Pendulum (RIP) is a nonlinear, open-loop unstable, and under-actuated system [24]. Its inherent instability makes it an ideal mechatronic platform to analyse and validate the performance of the proposed control scheme [25].…”

Section: Mathematical Model Of Systemmentioning

confidence: 99%

Self-tuning state-feedback control of a rotary pendulum system using adjustable degree-of-stability design

2020

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“…Specifically, using the product inference engine, singleton fuzzifier and center average defuzzifier, 29,41 equation (31) is obtained…”

Section: Control Design For Trajectory Trackingmentioning

confidence: 99%

Trajectory tracking for robotic airships using sliding mode control based on neural network approximation and fuzzy gain scheduling

Yang

Yan

2015

Proceedings of the Institution of Mechanical Engineers, Part I:

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The robotic airship is a unique and promising light-than-air platform, which has attracted worldwide developing interests for its broad applications. This article addresses the control problem of trajectory tracking for robotic airships. A neural network fuzzy sliding mode controller is proposed to steer a robotic airship along a referenced trajectory precisely. First, the dynamics model of a robotic airship is presented, and the problem of trajectory tracking is formulated. Second, a sliding mode controller is designed to track a time-varying trajectory. The neural network is employed to approximate the uncertain model of the airship, with the tracking error and its derivatives and the trajectory and its derivatives as neural network inputs and the approximation of the uncertain model as neural network output. And a fuzzy logic system is adopted to reduce the chattering results from the sliding mode controller. The control gains are tuned synchronously with the sliding surface according to fuzzy rules, with switching sliding surface as fuzzy logic inputs and control gains as fuzzy logic outputs. The stability and convergence of the closed-loop controller are proven using the Lyapunov stability theorem. Finally, the effectiveness and robustness of the proposed controller are demonstrated via simulation results. Contrasting simulation results indicate that the neural network fuzzy sliding mode controller reduces the chattering effectively and has better performance against the sliding mode controller.

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Self-organizing fuzzy optimal control for under-actuated systems

Cited by 16 publications

References 39 publications

Indirect Adaptive State-Feedback Control of Rotary Inverted Pendulum Using Self-Mutating Hyperbolic-Functions for Online Cost Variation

Indirect Adaptive State-Feedback Control of Rotary Inverted Pendulum Using Self-Mutating Hyperbolic-Functions for Online Cost Variation

Self-tuning state-feedback control of a rotary pendulum system using adjustable degree-of-stability design

Trajectory tracking for robotic airships using sliding mode control based on neural network approximation and fuzzy gain scheduling

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