• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.515-524
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• 2004

We prove the local existence of solution for nonconvex-valued differential inclusion under time-varying state constraints.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1905-1910
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• 2003

It is well known that parameter uncertainties and time-delays cannot be avoided in practice and result in poor performance and even instability. Nevertheless, to the authors' best knowledge, there exist few results on model predictive control (MPC) handling explicitly uncertain time-delayed systems. In this paper, we present an MPC algorithm for uncertain time-varying systems with input constraints and state-delay. An optimization problem is suggested to find a memoryless state-feedback MPC law and the closed-loop stability is established under feasibility and certain conditions.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.287-292
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• 2005

In this paper, a state steering strategy using digital control method for chained system is presented. The chained system can be derived from the velocity or acceleration constraints that cannot be integrable. Especially, the chained system derived from an acceleration constraints is called the high order chained system. Such a system classified as a nonholonomic systems and cannot be controlled to its equilibrium points by continuous and time-invariant controller. Therefore discontinuous and time varying controller should be applied to control nonholonomic system. Using variable transformation, two sub system can be obtained from the chained or high order chained system. Deadbeat control and iterative state steering methods are proposed to control the systems that obtained from the variable transformation. Simulation results are given to show the effectiveness of the proposed control scheme.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.7
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• pp.1019-1023
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• 2012

This paper considers a model predictive control problem of discrete-time constrained systems with time-varying delay. For this problem, a delay dependent state feedback control approach is used to achieve asymptotic stabilization of systems with input constraints. Based on Lyapunov stability theory, a new stability condition is obtained via linear matrix inequality formulation to find cost monotonicity condition of the model predictive control algorithm which guarantee the closed loop stability. Finally, the proposed method is applied to a numerical example in order to show the effectiveness of our results.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.191-212
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• 2012

Since a social network by definition is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications, which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary; estimating a user's interests typically involves non-stationary distributions. The consequent time varying nature of the distribution to be tracked imposes stringent constraints on the "unlearning" capabilities of the estimator used. Therefore, resorting to strong estimators that converge with a probability of 1 is inefficient since they rely on the assumption that the distribution of the user's preferences is stationary. In this vein, we propose to use a family of stochastic-learning based Weak estimators for learning and tracking a user's time varying interests. Experimental results demonstrate that our proposed paradigm outperforms some of the traditional legacy approaches that represent the state-of-the-art technology.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.31-39
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• 2000

We present a robust and reliable H$\infty$ state-feedback controller design for linear uncertain systems, which have norm-bounded time-varying uncertainty in the state matrix, and their prespecified sets of actuators are susceptible to failure. These controllers should guarantee robust stability of the systems and H$\infty$ norm bound against parameter uncertainty and/or actuator failures. Based on the linear matrix inequality (LMI) approach, two state-feedback controller design methods are constructed by formulating to a set of LMIs corresponding to all failure cases or a single LMI that covers all failure cases, with an additional costraint. Effectiveness and geometrical property of these controllers are validated via several numerical examples. Furthermore, the proposed LMI frameworks can be applied to multiobjective problems with additional constraints.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.397-402
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• 2005

The guaranteed cost control problem for a class of linear systems with norm-bounded time-varying parameter uncertainties and input constraints is considered. A sufficient condition for the existence of guaranteed cost state feedback controllers is derived via the linear matrix inequality (LMI) approach, and a design procedure to guaranteed cost controllers is given. Furthermore, a convex optimization problem is formulated to determine the optimal guaranteed cost controller. An example is given to illustrate the effectiveness of the proposed results.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.1-9
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• 2024

This paper proposes an integration of the ED-H scheduling algorithm, known for optimal real-time scheduling, with the LoRaEnergySim simulator. This integration facilitates the simulation of interactions between real-time scheduling algorithms for tasks with time constraints in Class-A LoRaWAN Class-A devices using a super-capacitor-based energy harvesting system. The time and energy characteristics of LoRaWAN status and state transitions are extracted in a log format, and the task model is structured to suit the time-slot-based ED-H scheduling algorithm. The algorithm is extended to perform tasks while satisfying time constraints based on CPU executions. To evaluate the proposed approach, the ED-H scheduling algorithm is executed on a set of tasks with varying time and energy characteristics and CPU occupancy rates ranging from 10% to 90%, under the same conditions as the LoRaEnergySim simulation results for packet transmission and reception. The experimental results confirmed the applicability of co-simulation by demonstrating that tasks are prioritized based on urgency without depleting the supercapacitor's energy to satisfy time constraints, depending on the scheduling algorithm.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.27-36
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• 2014

This paper presents an adaptive wide-area damping controller (WADC) based on generalized predictive control (GPC) and model identification for damping the inter-area low frequency oscillations in large-scale inter-connected power system. A recursive least-squares algorithm (RLSA) with a varying forgetting factor is applied to identify online the reduced-order linearlized model which contains dominant inter-area low frequency oscillations. Based on this linearlized model, the generalized predictive control scheme considering control output constraints is employed to obtain the optimal control signal in each sampling interval. Case studies are undertaken on a two-area four-machine power system and the New England 10-machine 39-bus power system, respectively. Simulation results show that the proposed adaptive WADC not only can damp the inter-area oscillations effectively under a wide range of operation conditions and different disturbances, but also has better robustness against to the time delay existing in the remote signals. The comparison studies with the conventional lead-lag WADC are also provided.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5A
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• pp.355-362
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• 2009

In real-time communication services, delay constraints are among the most important QoS (Quality of Service) factors. In particular, it is difficult to guarantee the delay requirement over wireless channels, since they exhibit dynamic time-varying behavior and even severe burst-errors during periods of deep fading. Channel throughput may be increased, but at the cost of the additional delays when ARQ (Automatic Repeat Request) schemes are used. For real-time communication services, it is very essential to predict data deliverability. This paper derives the delay distribution and the successful delivery probability within a given delay budget using a priori channel model and a posteriori information from the perspective of queueing theory. The Gilbert-Elliot burst-noise channel is employed as an a Priori channel model, where a two-state Markov-modulated Bernoulli process $(MMBP_2)$ is used. for a posteriori information, the channel parameters, the queue-length and the initial channel state are assumed to be given. The numerical derivation is verified and analyzed via Monte Carlo simulations. This numerical derivation is then applied to a rate control scheme for real-time video transmission, where an optimal encoding rate is determined based on the future channel capacity and the distortion of the reconstructed pictures.