PI Control with Input and Output Constraints

Glattfelder, A. H.; Schaufelberger, W.

doi:10.1007/978-1-4471-0047-8_4

Cited by 40 publications

(52 citation statements)

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“…The basic idea underlying these methods is to drop the stringent condition of forming a relaxation sequence (9). Instead, fast gradient methods make use of so-called estimate sequences:…”

Section: B Fast Gradient Methodsmentioning

confidence: 99%

Real-time input-constrained MPC using fast gradient methods

Richter

Jones

Morari

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

124

102

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Abstract-Linear quadratic model predictive control (MPC) with input constraints leads to an optimization problem that has to be solved at every instant in time. Although there exists computational complexity analysis for current online optimization methods dedicated to MPC, the worst case complexity bound is either hard to compute or far off from the practically observed bound. In this paper we introduce fast gradient methods that allow one to compute a priori the worst case bound required to find a solution with pre-specified accuracy. Both warmand cold-starting techniques are analyzed and an illustrative example confirms that small, practical bounds can be obtained that together with the algorithmic and numerical simplicity of fast gradient methods allow online optimization at high rates.

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“…The basic idea underlying these methods is to drop the stringent condition of forming a relaxation sequence (9). Instead, fast gradient methods make use of so-called estimate sequences:…”

Section: B Fast Gradient Methodsmentioning

confidence: 99%

Real-time input-constrained MPC using fast gradient methods

Richter

Jones

Morari

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

124

102

View full text Add to dashboard Cite

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“…While linear control theory is not formally applicable to process systems in the presence of hard input constraints, the recent trends in process control focus on seeking advanced nonlinear control strategies that are able to provide the capability of overcoming the difficulties that arose from the negative effects of hard input constraints. Although various advanced anti-windup designs have been reported recently (Doyle, 1999;Glattfelder and Schaufelberger, 2003;Gomes and Tarbouriech, 2005;Adegbege and Heath, 2011), there is no general framework that is able to deal with all kinds of problems. Therefore, there are still some unsolved and open questions that need to be further investigated, especially for nonlinear process systems (Tarbouriech and Turner, 2009).…”

Section: An Extension Of Nonlinear Processes With Hard Input Constraintsmentioning

confidence: 98%

A Direct Adaptive Control Strategy for Nonlinear Process Control Using a Shape-Tunable Nonlinear Controller

Chen

2014

Chemical Engineering Communications

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This paper proposes a novel model-based direct adaptive control (DAC) strategy for a class of nonlinear processes whose nominal model is input-output linearizable but may not be accurate enough to represent the actual process. The proposed DAC scheme is composed of two parts: the first is a model based part that makes use of the available process model for a nominal performance, and the second is an adaptive, nonlinear shape-tunable controller (STC) that is used to compensate for the model errors. To update the STC's shape parameters such that the modeling errors can be accommodated adaptively, a simple and effective parameter tuning algorithm is devised. The offset-free control performance and the stability of the resultant nonlinear control system are guaranteed by a Lyapunov-based approach, which involves two distinctive performance indices to relate, respectively, the process output error and a specially designed auxiliary signal. Furthermore, by simply manipulating the output range of the STC, the proposed DAC scheme is further extended to one that is able to handle nonlinear processes in the presence of hard input constraints. The effectiveness and applicability of the proposed DAC control scheme were examined through the regulatory control of a nonlinear, continuous stirred-tank reactor and the temperature trajectory tracking control of an input-constrained batch reactor under the influence of diversified process disturbances. Simulation results reveal that the proposed model-based DAC scheme is superior to a model-based linearizing control strategy, a sliding mode controller, and an intelligent, model-free DAC scheme, especially when faced with significant plant=model mismatch and unanticipated uncertainties.

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“…The effects of saturation are addressed through antiwindup strategies [2][3][4][5][6][7]. Within the context of modern multi-variable control, techniques for dealing with saturation are presented in [8][9][10][11][12]. Saturation within the context of adaptive control is addressed in [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Adaptive control with convex saturation constraints

Yan

Santos

Bernstein

2014

IET Control Theory & Applications

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This study applies retrospective cost adaptive control to command following in the presence of multi-variable convex input saturation constraints. To account for the saturation constraint, the authors use convex optimisation to minimise the quadratic retrospective cost function. The use of convex optimisation bounds the magnitude of the retrospectively optimised input and thereby influences the controller update to satisfy the control bounds. This technique is applied to a multi-rotor helicopter with constraints on the total thrust magnitude and inclination of the rotor plane.

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PI Control with Input and Output Constraints

Cited by 40 publications

References 0 publications

Real-time input-constrained MPC using fast gradient methods

Real-time input-constrained MPC using fast gradient methods

A Direct Adaptive Control Strategy for Nonlinear Process Control Using a Shape-Tunable Nonlinear Controller

Adaptive control with convex saturation constraints

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