• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.502-502
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• 2000

An input constrained receding horizon predictive control algorithm for uncertain systems with nonzero set points is proposed. For constant nonzero set points, models with uncertainty can be converted into an augmented incremental system through the use of integrators and the problem is transformed into a zero-state regulation problem for the incremental system. But the original constraints on inputs are converted into constraints on the sum of control inputs at each time Instants, which have not been dealt in earlier constrained robust receding horizon control problems. Recursive state bounding technique and worst case minimizing strategy developed in earlier works are applied to the augmented incremental system to yield an of set error free controller. The resulting algorithm is formulated so that it can be solved using LP.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.777-780
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• 1995

Accounting for actuator nonlinearities in control loops has often been perceived as an implementation issue and usually excluded in the design of controllers. Nonlinearities treated in this paper are saturation, and they are modelled as an inequality constraint. The CRHPC(Constrained Receding Horizon Predictive Control) with inequality constraints algorithm is used to handle actuator rate and amplitude limits simultaneously or respectively. Optimum values of future control signals are obtained by quadratic programming. Simulated examples show that predictive control law with inequality constraints offers good performance as compared with input clipping.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1140-1142
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• 1996

In this paper, a fixed-horizon $H_{\infty}$ tracking control (HTC) for continuous time-varying systems is proposed in state-feedback case. The solution is obtained via the dynamic game theory. From HTC, an intervalwise receding horizon $H_{\infty}$ tracking control (IHTC) for continuous periodic systems is obtained using the intervalwise strategy. The conditions under which IHTC stabilizes the closed-loop system are proposed. Under proposed stability conditions, it is shown that IHTC guarantees the $H_{\infty}$-norm bound.

• 전기의세계
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• v.49 no.9
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• pp.4-8
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• 2000

Recently we have shown based on Lyapunov theorem that the closed loop system with the constrained infinite horizon H$\infty$ optimal controller is exponentially stable. moreover the on-line feedback implementation of the constrained infinite horizon H$\infty$ optimal control based on quadratic programs has been proposed. n this paper we summarize and discuss these results.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.2
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• pp.110-115
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• 2000

A receding horizon FIR filter is suggested for discrete time-varying systems, combining the Kalman filter with the receding horizon strategy. The suggested filter is shown to be an FIR structure that has many good ingerent properties. The suggested filter is represented in an iterative form and also in a standard FIR form. The suggested filter turns out to be a remarkable deadbeat observer that is often robust against system and measurement noises. It is also shown that the suggested filter is an unbiased estimator irrespective of any horizon initial condition. For the amenability to parallel and systolic implementation as well as the numerical stability, a square-root algorithm for the suggested filter is presented. To evaluate performance, the suggested filter is applied to a problem of unknown input estimation and compared with the existing Kalman filter based approach.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1911-1916
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• 2003

Parameter set of an LPV system is divided into a number of subsets so that robust feedback gains may be designed for each subset of parameters. A concept of quasi-invariant set is introduced, which allows finite steps of delay in reentrance to the set. A feasible and positively invariant set with respect to a gain-scheduled state feedback control can be easily obtained from the quasi-invariant set. A receding horizon control strategy can be derived based on this feasible and invariant set.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2471-2473
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• 2005

We present a receding horizon control [RHC] algorithm for compensation of backlash at the input of a stable linear system under control rate constraints. The problem is posed as a receding horizon optimal control [RHOptC] problem for a piecewise affine [PWA] system by modelling the backlash nonlinearity as a PWA system with a state space partition consisting of three regions. The RHC problem involves solving, at each step, $3^N$ quadratic programmes[QP], where N is the optimization horizon. This strategy leads, at the cost of some performance degradation, to much smaller computational load since a feasible rather than optimal solution has to be obtained at each step.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.307-308
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• 2006

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.567-573
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• 1998

본 논문에서는 입력과 상태변수에 hard constraint가 있는 이산시간 시스템에 대한 새로운 이동구간 제어기를 제안한다. 제안된 이동구간제어기가 terminal ellipsoid constraint를 이용하여 지수 안정성(exponential stability)을 보장함을 보인다. Feasibility를 증가시키기 위하여 시스템을 안정 모드와 불안정 모드로 바꾸는 방법, constraint를 완화하는 방법을 제안한다. Constraint를 완화할 때 constraint를 만족시키지 못하는 부분의 성능을 개선시키는 방법을 제안하고 예제를 통해서 몇 가지 제안한 방법들을 비교한다.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.272-279
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• 1997

In this paper, a fuzzy generalized predictive control (FGPC) for non-linear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input-output data without identifying a plant model. A parameter estimation algorithm is used for identifying the fuzzy predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus may possess inherent good properties of the GPC. Computer simulations show that the performance of the FGPC is satisfactory.