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"Feedback control"
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A universal biomolecular integral feedback controller for robust perfect adaptation
Homeostasis is a recurring theme in biology that ensures that regulated variables robustly—and in some systems, completely—adapt to environmental perturbations. This robust perfect adaptation feature is achieved in natural circuits by using integral control, a negative feedback strategy that performs mathematical integration to achieve structurally robust regulation
1
,
2
. Despite its benefits, the synthetic realization of integral feedback in living cells has remained elusive owing to the complexity of the required biological computations. Here we prove mathematically that there is a single fundamental biomolecular controller topology
3
that realizes integral feedback and achieves robust perfect adaptation in arbitrary intracellular networks with noisy dynamics. This adaptation property is guaranteed both for the population-average and for the time-average of single cells. On the basis of this concept, we genetically engineer a synthetic integral feedback controller in living cells
4
and demonstrate its tunability and adaptation properties. A growth-rate control application in
Escherichia coli
shows the intrinsic capacity of our integral controller to deliver robustness and highlights its potential use as a versatile controller for regulation of biological variables in uncertain networks. Our results provide conceptual and practical tools in the area of cybergenetics
3
,
5
, for engineering synthetic controllers that steer the dynamics of living systems
3
–
9
.
A synthetic gene circuit implementing an integral feedback topology is shown to achieve robust perfect adaptation in living cells--mathematical analysis proves this topology is necessary for adaptation in networks with noisy dynamics.
Journal Article
Neural Network Control of a Rehabilitation Robot by State and Output Feedback
In this paper, neural network control is presented for a rehabilitation robot with unknown system dynamics. To deal with the system uncertainties and improve the system robustness, adaptive neural networks are used to approximate the unknown model of the robot and adapt interactions between the robot and the patient. Both full state feedback control and output feedback control are considered in this paper. With the proposed control, uniform ultimate boundedness of the closed loop system is achieved in the context of Lyapunov’s stability theory and its associated techniques. The state of the system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Extensive simulations for a rehabilitation robot with constraints are carried out to illustrate the effectiveness of the proposed control.
Journal Article
Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control
A comparative study of model predictive control (MPC) schemes and robust
H
∞
state feedback control (RSC) method for trajectory tracking is proposed in this paper. The main objective of this paper is to compare MPC and RSC controllers’ performance in tracking predefined trajectory under different scenarios. MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire, which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode. RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison. Then, three test cases are built in CarSim-Simulink joint platform. Specifically, the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions. Besides, the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability. Furthermore, an extreme curve test is built where the road adhesion changes suddenly, in order to test the performance of both controllers under extreme conditions. Finally, the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.
Journal Article
Irreversibility, heat and information flows induced by non-reciprocal interactions
We study the thermodynamic properties induced by non-reciprocal interactions between stochastic degrees of freedom in time- and space-continuous systems. We show that, under fairly general conditions, non-reciprocal coupling alone implies a steady energy flow through the system, i.e., non-equilibrium. Projecting out the non-reciprocally coupled degrees of freedom renders non-Markovian, one-variable Langevin descriptions with complex types of memory, for which we find a generalized second law involving information flow. We demonstrate that non-reciprocal linear interactions can be used to engineer non-monotonic memory, which is typical for, e.g., time-delayed feedback control, and is automatically accompanied with a nonzero information flow through the system. Furthermore, already a single non-reciprocally coupled degree of freedom can extract energy from a single heat bath (at isothermal conditions), and can thus be viewed as a minimal version of a time-continuous, autonomous 'Maxwell demon'. We also show that for appropriate parameter settings, the non-reciprocal system has characteristic features of active matter, such as a positive energy input on the level of the fluctuating trajectories without global particle transport.
Journal Article
Strategies of linear feedback control and its classification
[...]numerical simulations are given to illustrate and verify the results. [...]a suitable controller is designed to transform the chaotic/hyperchaotic systems into stable systems and achieve the required disturbance attenuation performance. According to Theorem1, four case, the system (6) with control [0,0,0,-ð???ð??¥4]ð??? can't be suppress since ð???1∩ð???2=Ø. For the ordinary feedback control, the three controls with positive feedback coefficient ð??? obtained, their characteristic equations and corresponding of index of Routh-Hurwitz theorem, are listed in Table 1. According to Theorem1, third case, then system (6) can be suppress after add the term -ð???ð???ð??? to first equation, since ð???1∩ð???2=(9.6485,10)≠Ø. While, only one positive feedback coefficient ð???1<10 is obtain in the second control.
Journal Article