We consider the problem of learning time-varying functions in a distributed fashion, where agents collect local information to collaboratively achieve a shared estimate. This task is particularly relevant in control a...
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In this paper, novel parametric identification is addressed by the disturbance observer (DOB) relevant algorithm. To fully utilize the disturbance observer, a proper nominal model, which is a parametric model, is esse...
In this paper, novel parametric identification is addressed by the disturbance observer (DOB) relevant algorithm. To fully utilize the disturbance observer, a proper nominal model, which is a parametric model, is essential. The control performance of the DOB can be checked based on the robust stability criterion, which is related to the modeling error and the desired bandwidth of the DOB. Consequently, it is beneficial to evaluate the parametric model in the system identification procedure. Therefore, this paper proposes a novel disturbance observer-relevant system identification algorithm based on the robust stability criterion of the disturbance observer. The effectiveness of the proposed method is validated by showing the numerical simulations under specific conditions.
We consider the output consensus problem for a heterogeneous multi-agent system with one leader agent and $N$ follower agents. The dynamics of each follower agent is governed by a single-input single-output (SISO) s...
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We consider the output consensus problem for a heterogeneous multi-agent system with one leader agent and $N$ follower agents. The dynamics of each follower agent is governed by a single-input single-output (SISO) stable linear system. The dynamics of the leader agent is governed by a marginally stable SISO linear system, input to which is zero at all times. Each follower agent has input and output delays which can be nonuniform across the multi-agent system. For each follower agent, we construct a pre-compensator and every follower agent has access to the output of the pre-compensator associated with its neighboring agents. A subset of the follower agents can also access the output of the leader agent. The communication delay, which is the delay in the exchange of outputs, is uniform across all the follower agents. All the delays present in the multi-agent system can be arbitrarily large but are known. For each pre-compensator, under the assumed information exchange, we design a feedback controller that drives the state of all the pre-compensators to a consensus trajectory. The synchronized outputs of the pre-compensators is then used to ensure output consensus among the follower agents. The controller design is robust to small perturbations in the communication delays. We demonstrate the efficacy of our design technique using a numerical example.
Motivated by the problem of university admissions, we study the problem of correctly classifying the label of a feature vector when a sender can manipulate this vector to get a desired label. There are two players: se...
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Motivated by the problem of university admissions, we study the problem of correctly classifying the label of a feature vector when a sender can manipulate this vector to get a desired label. There are two players: sender and receiver and both know the target function over the feature vectors. The sender privately observes a feature vector and manipulates it according to its payoff criteria and sends it to the receiver. The receiver would like to perform multi-class classification to maximize the number of points for which it can recover the original label of the feature vector known to the sender. We pose this problem as a Stackelberg game with the receiver as the leader. Through this we characterise the optimal classifier in terms of the independence number of a certain graph which depends on the utility and cost of the sender and the target function. As a corollary, we characterise the optimal strategy of the receiver for the problem of strategic communication with cost. Finally we show that if the target function is incentive compatible condition, then the receiver can correctly classify all feature vectors.
In this paper, we consider the problem of constructing an output-feedback controller for stabilizing an un-stable linear PDE system with bounded control and observation operators driven by an unknown disturbance. The ...
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In this paper, we consider the problem of constructing an output-feedback controller for stabilizing an un-stable linear PDE system with bounded control and observation operators driven by an unknown disturbance. The disturbance is matched with the input of the PDE system and its derivative is assumed to be bounded. Under a smoothness assumption on the observation operator and an observability matching condition, we first develop a linear observer with disturbance decoupling for state estimation and then we develop a sliding mode observer for estimating the disturbance. Using the estimated state and disturbance we implement a state-feedback control law which guarantees the exponential decay of the state of the PDE system to zero for all initial states. Our approach assumes certain prior knowledge regarding the stabilization and estimation of the PDE system in the absence of disturbances. Our contribution lies in performing stabilization (convergence of state to zero) and estimation in the presence of unknown disturbances. We illustrate our controller design approach in simulations using an unstable 1D heat equation.
Motivated by increasing precision requirements for switched power amplifiers, this paper addresses the problem of model predictive control (MPC) design for discrete-time linear systems with a finite control set (FCS)....
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This article studies the application of discrete sliding mode predictive control (SMPC) in networked controlsystems, introducing sliding mode control (SMC) into model predictive control (MPC), and prediction the stat...
This article studies the application of discrete sliding mode predictive control (SMPC) in networked controlsystems, introducing sliding mode control (SMC) into model predictive control (MPC), and prediction the state and output vector based on sliding mode control. By combining the strong robustness of sliding mode control and the advantage of model predictive control in dealing with communication constraints in networkcontrol, an incremental SMPC trajectory tracking control scheme based on a state observer is designed. The effectiveness and practicability of the designed control scheme is verified by tracking experiments on reference inputs in a dSPACE-based networked lighting control system.
In this paper, we consider ODE-PDE cascade systems in which the input is applied to the ODE system whose output drives the PDE system and PDE-ODE cascade systems in which the input is applied to the PDE system whose o...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
In this paper, we consider ODE-PDE cascade systems in which the input is applied to the ODE system whose output drives the PDE system and PDE-ODE cascade systems in which the input is applied to the PDE system whose output drives the ODE system. In both the cascade systems we take the PDE system to be an 1D heat equation in polar coordinates containing a singular term. We address the problem of designing state-feedback control laws for stabilizing these cascade systems via finite-dimensional approximation and LQR design. Using the Galerkin technique we first obtain an N th -order ODE approximation for the cascade system. Next we fix a quadratic cost function for the cascade system and consider the corresponding operator algebraic Riccati equation (ARE). Then we associate an appropriate matrix ARE with the ODE approximation. Under some natural assumptions on the cascade system, we verify that certain well-known hypothesis in the literature hold, which imply that the solution Π N of the matrix ARE converges to the solution Π of the operator ARE strongly as N→∞. This implies that the stabilizing gain for the ODE approximation determined by Π N converges to a stabilizing gain for the cascade system. We illustrate this in simulations.
Time-varying hierarchical multi-agent systems are common in many applications. A well-known solution to control these systems is to use state feedback controllers that depend on the adjacency matrix to reach consensus...
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Rotary motors, such as hybrid stepper motors (HSMs), are widely used in industries varying from printing applications to robotics. The increasing need for productivity and efficiency without increasing the manufacturi...
Rotary motors, such as hybrid stepper motors (HSMs), are widely used in industries varying from printing applications to robotics. The increasing need for productivity and efficiency without increasing the manufacturing costs calls for innovative control design. Feedforward control is typically used in tracking control problems, where the desired reference is known in advance. In most applications, this is the case for HSMs, which need to track a periodic angular velocity and angular position reference. Performance achieved by feed-forward control is limited by the accuracy of the available model describing the inverse system dynamics. In this work, we develop a physics–guided neural network (PGNN) feedforward controller for HSMs, which can learn the effect of parasitic forces from data and compensate for it, resulting in improved accuracy. Indeed, experimental results on an HSM used in printing industry show that the PGNN outperforms conventional benchmarks in terms of the mean–absolute tracking error.
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