Adaptive State-space Control of Under-actuated Systems Using Error-magnitude Dependent Self-tuning of Cost Weighting-factors

Saleem, Omer; Mahmood-ul-Hasan, Khalid

doi:10.1007/s12555-020-0209-z

Cited by 10 publications

(8 citation statements)

References 29 publications

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“…It is to be noted that the hyperbolic secant functions and zero-mean Gaussian functions can also be used instead of the PHF to mathematically program the said adaptation law [ 64 , 68 ]. The error-magnitude driven PHFs used to scale each state and control weighting-factor are formulated below [ 71 ]. where, a x and b x represent the prescribed upper and lower bounds of the state-weighting functions, and γ x represents the variance of the state-weighting functions.…”

Section: Hierarchical Self-tuning-regulator Designmentioning

confidence: 99%

“…A proper selection of the γ x enables the controller to apply a stiffer control effort under disturbed state and a softer control effort under equilibrium state of the system. This arrangement strengthens the system’s damping against fluctuations, yields minimum-time transient recovery and renders a smoother control activity [ 71 ]. It also averts the limit-cycles contributed by static-friction during dead-zones.…”

Section: Hierarchical Self-tuning-regulator Designmentioning

confidence: 99%

“…This arrangement prevents the actuator from getting saturated due to the rapid fluctuations and large overshoots in the control-input profile, without trading-off the system’s robustness, under exogenous disturbances [ 52 ]. It contributes rapid transits with strong damping against disturbances while economizing the control-energy expenditure [ 71 ].…”

Section: Hierarchical Self-tuning-regulator Designmentioning

confidence: 99%

“…The hyper-parameters associated with each weight-adjusting function are tuned by iteratively minimizing J c to attain the best position-regulation accuracy. The tuned parameters are presented in Table 6 [ 71 ]. The STR constructed via the error-magnitude driven PHFs is denoted as “EM-STR”.…”

Section: Hierarchical Self-tuning-regulator Designmentioning

confidence: 99%

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An experimental comparison of different hierarchical self-tuning regulatory control procedures for under-actuated mechatronic systems

2021

Self Cite

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This paper presents an experimental comparison of four different hierarchical self-tuning regulatory control procedures in enhancing the robustness of the under-actuated systems against bounded exogenous disturbances. The proposed hierarchical control procedure augments the ubiquitous Linear-Quadratic-Regulator (LQR) with an online reconfiguration block that acts as a superior regulator to dynamically adjust the critical weighting-factors of LQR’s quadratic-performance-index (QPI). The Algebraic-Riccati-Equation (ARE) uses these updated weighting-factors to re-compute the optimal control problem, after every sampling interval, to deliver time-varying state-feedback gains. This article experimentally compares four state-of-the-art rule-based online adaptation mechanisms that dynamically restructure the constituent blocks of the ARE. The proposed hierarchical control procedures are synthesized by self-adjusting the (i) controller’s degree-of-stability, (ii) the control-weighting-factor of QPI, (iii) the state-weighting-factors of QPI as a function of “state-error-phases”, and (iv) the state-weighting-factors of QPI as a function of “state-error-magnitudes”. Each adaptation mechanism is formulated via pre-calibrated hyperbolic scaling functions that are driven by state-error-variations. The implications of each mechanism on the controller’s behaviour are analyzed in real-time by conducting credible hardware-in-the-loop experiments on the QNET Rotary-Pendulum setup. The rotary pendulum is chosen as the benchmark platform owing to its under-actuated configuration and kinematic instability. The experimental outcomes indicate that the latter self-adaptive controller demonstrates superior adaptability and disturbances-rejection capability throughout the operating regime.

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Section: Hierarchical Self-tuning-regulator Designmentioning

confidence: 99%

Section: Hierarchical Self-tuning-regulator Designmentioning

confidence: 99%

Section: Hierarchical Self-tuning-regulator Designmentioning

confidence: 99%

Section: Hierarchical Self-tuning-regulator Designmentioning

confidence: 99%

See 2 more Smart Citations

An experimental comparison of different hierarchical self-tuning regulatory control procedures for under-actuated mechatronic systems

2021

Self Cite

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“…The optimization problem of self-balancing robots under depleting battery conditions was analyzed [ 7 ]. Additionally, under-actuated systems using the error-magnitude-dependent self-tuning of the cost weighting factor via the adaptive state-space control law has been studied [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Sliding Mode Neural Network Controller Design for Heterogeneous Multi-Agent Systems

Shen

Gao

Liu

2023

Sensors

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A class of heterogeneous second-order multi-agent consensus problems is studied, in which an event-triggered method is used to improve the feasibility of the control protocol. The sliding mode control method is used to achieve the robustness of the system. A special type of general radial basis function neural network is applied to estimate the uncertainties. The event-triggered mechanism is introduced to reduce the update frequency of the controller and the communication frequency among the agents. Zeno behavior is avoided by ensuring a lower bound between two adjacent trigger instants. Finally, the simulation results are provided to demonstrate that the time evolution of consensus errors eventually approaches zero. The consensus of multi-agent systems is achieved.

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Nonlinear control providing the plant inputs and outputs in given sets

Furtat,

Gushchin,

Nguyen

2024

European Journal of Control

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Adaptive State-space Control of Under-actuated Systems Using Error-magnitude Dependent Self-tuning of Cost Weighting-factors

Cited by 10 publications

References 29 publications

An experimental comparison of different hierarchical self-tuning regulatory control procedures for under-actuated mechatronic systems

An experimental comparison of different hierarchical self-tuning regulatory control procedures for under-actuated mechatronic systems

Event-Triggered Sliding Mode Neural Network Controller Design for Heterogeneous Multi-Agent Systems

Nonlinear control providing the plant inputs and outputs in given sets

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