Proportional-Integral Controllers for Minimum-Phase Nonaffine-in-Control Systems

Teo, Justin; How, Jonathan P.; Lavretsky, Eugene

doi:10.1109/tac.2010.2045693

Cited by 33 publications

(15 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, by choosing a special filter, the proposed controllers can be finally replaced by proportional-integral (PI) controllers. This is consistent with the controller form in [16] for a similar problem. However, compared with [16], the considered plant, analysis method and design procedure are all different, especially the analysis method and design procedure.…”

Section: Introductionsupporting

confidence: 87%

“…This is consistent with the controller form in [16] for a similar problem. However, compared with [16], the considered plant, analysis method and design procedure are all different, especially the analysis method and design procedure. The bound on a parameter, corresponding to the singular perturbation parameter in [16], is also given explicitly.…”

Section: Introductionsupporting

confidence: 87%

See 1 more Smart Citation

Additive-output-decomposition-based dynamic inversion tracking control for a class of uncertain linear time-invariant systems

Quan

Cai

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

This paper presents a new control, namely additive-state-decomposition dynamic inversion stabilized control, that is used to stabilize a class of multi-input multi-output (MIMO) systems subject to nonparametric time-varying uncertainties with respect to both state and input. By additive state decomposition and a new definition of output, the considered uncertain system is transformed into a minimum-phase uncertainty-free system with relative degree one, in which all uncertainties are lumped into a new disturbance at the output. Subsequently, dynamic inversion control is applied to reject the lumped disturbance. Performance analysis of the resulting closed-loop dynamics shows that the stability can be ensured. Finally, to demonstrate its effectiveness, the proposed control is applied to two existing problems by numerical simulation. Furthermore, in order to show its practicability, the proposed control is also performed on a real quadrotor to stabilize its attitude when its inertia moment matrix is subject to a large uncertainty.

show abstract

Section: Introductionsupporting

confidence: 87%

Section: Introductionsupporting

confidence: 87%

Additive-output-decomposition-based dynamic inversion tracking control for a class of uncertain linear time-invariant systems

Quan

Cai

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

show abstract

“…In this section, we present the adaptive neural network fault-tolerant control using the backstepping technique for helicopter yaw control system. The recursive design process contains two steps in the system of Equation (5).The design procedure is as follows:…”

Section: Controller Design and Stability Analysismentioning

confidence: 99%

“…As a result, it is a challenging task to determine the control input. To overcome this design difficulty for a non-affine system, the traditional approaches contain an inverse control strategy [5] that requires more accurate mathematical models. T-S fuzzy control [6], mean value theorem [7][8][9], which has many online adjustment parameters, and Taylor series expansion [10].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints

Zhang

Chen

2020

Applied Sciences

View full text Add to dashboard Cite

In this paper, an adaptive neural fault-tolerant tracking control scheme is presented for the yaw control of an unmanned-aerial-vehicle helicopter. The scheme incorporates a non-affine nonlinear system that manages actuator faults, input saturation, full-state constraints, and external disturbances. Firstly, by using a Taylor series expansion technique, the non-affine nonlinear system is transformed into an affine-form expression to facilitate the desired control design. In comparison with previous techniques, the actuator efficiency is explicit. Then, a neural network is considered to approximate unknown nonlinear functions, and a time-varying barrier Lyapunov function is employed to prevent transgression of the full-state variables using a backstepping technique. Robust adaptive control laws are designed to handle parameter uncertainties and unknown bounded disturbances to cut down the number of learning parameters and simplify the computational burden. Moreover, an auxiliary system is constructed to guarantee the pitch angle of the UAV helicopter yaw control system to satisfy the input constraint. Uniform boundedness of all signals in a closed-loop system is ensured via Lyapunov theory; the tracking error converges to a small neighborhood near zero. Finally, when the numerical simulations are applied to a yaw control of helicopter, the adaptive neural controller demonstrates the effectiveness of the proposed technique.

show abstract

“…However, since its estimates are conservative, it may waste power by using somewhat higher frequencies than needed. To improve efficiency, we use a simple PI controller [55] that observes the difference between the measured and predicted tail latencies over a rolling 1-second window and adjusts Rubik's internal latency target. Adjustments are minor, as the analytical model typically needs little correction.…”

Section: Rubik Implementationmentioning

confidence: 99%

Rubik

Kasture

Bartolini

Beckmann

et al. 2015

Proceedings of the 48th International Symposium on Microarchitecture

117

View full text Add to dashboard Cite

Latency-critical workloads (e.g., web search), common in datacenters, require stable tail (e.g., 95th percentile) latencies of a few milliseconds. Servers running these workloads are kept lightly loaded to meet these stringent latency targets. This low utilization wastes billions of dollars in energy and equipment annually.Applying dynamic power management to latency-critical workloads is challenging. The fundamental issue is coping with their inherent short-term variability: requests arrive at unpredictable times and have variable lengths. Without knowledge of the future, prior techniques either adapt slowly and conservatively or rely on application-specific heuristics to maintain tail latency.We propose Rubik, a fine-grain DVFS scheme for latency-critical workloads. Rubik copes with variability through a novel, general, and efficient statistical performance model. This model allows Rubik to adjust frequencies at sub-millisecond granularity to save power while meeting the target tail latency. Rubik saves up to 66% of core power, widely outperforms prior techniques, and requires no application-specific tuning.Beyond saving core power, Rubik robustly adapts to sudden changes in load and system performance. We use this capability to design RubikColoc, a colocation scheme that uses Rubik to allow batch and latencycritical work to share hardware resources more aggressively than prior techniques. RubikColoc reduces datacenter power by up to 31% while using 41% fewer servers than a datacenter that segregates latency-critical and batch work, and achieves 100% core utilization.

show abstract

Proportional-Integral Controllers for Minimum-Phase Nonaffine-in-Control Systems

Cited by 33 publications

References 19 publications

Additive-output-decomposition-based dynamic inversion tracking control for a class of uncertain linear time-invariant systems

Additive-output-decomposition-based dynamic inversion tracking control for a class of uncertain linear time-invariant systems

Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints

Rubik

Contact Info

Product

Resources

About