Generalised predictive control with input constraints

Tsang, T.T.C.; Clarke, D.W.

doi:10.1049/ip-d.1988.0069

Cited by 96 publications

(45 citation statements)

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“…In [6], an extension to the basic Hildreth algorithm is discussed, which accelerates convergence in practice, but still has linear convergence properties. However, as observed by Tsang & Clarke [15], for short control horizons (when either one or two control moves are constrained), and under the assumption that only rate or amplitude constraints are present, then there is no need to employ a full QP solution or row-action method, as the active set is trivially determined. However, in [15], no method is outlined as to how rate and amplitude constraints can be simultaneously handled.…”

Section: Previous Workmentioning

confidence: 99%

“…However, as observed by Tsang & Clarke [15], for short control horizons (when either one or two control moves are constrained), and under the assumption that only rate or amplitude constraints are present, then there is no need to employ a full QP solution or row-action method, as the active set is trivially determined. However, in [15], no method is outlined as to how rate and amplitude constraints can be simultaneously handled. In the MPC controller to be developed in the following section, details of such a method are provided.…”

Section: Previous Workmentioning

confidence: 99%

“…Although Tsang & Clarke [15] did not consider the extension of these methods to the situation when both rate and amplitude constraints are simultaneously present with short control horizons, it is relatively straightforward to implement as will be discussed in the next section.…”

Section: Short-horizon Mpc With Amplitude-only Constraintsmentioning

confidence: 99%

“…The use of the reduced control horizon has the advantage of significantly reducing the problem's complexity. As discussed, for short control horizons (when either one or two control moves are computed) and under the assumption that only rate or amplitude constraints are present, there is no need to employ a full QP solution, as the active set is trivially determined [15]. As a preliminary, note that the unconstrained optimal control vector ∆u in MPC is given by the solution of the 'normal' MPC equations:…”

Section: Constrained Model Predictive Controllermentioning

confidence: 99%

“…Techniques for the solution of such an input-constrained MPC problem have been previously studied in the literature. Most approaches are based around primal or dual active set QP solvers (see, e.g., [1,2,14,15]). Such QP solvers are suitable for PC-based implementation, but are unwieldy and exhibit large code and data memory overhead on microcontroller-based embedded platforms; this makes them unsuitable for use unless specialized implementations (e.g., dedicated co-processing hardware [16]) are employed.…”

Section: Previous Workmentioning

confidence: 99%

See 4 more Smart Citations

A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications

Short

Abugchem

2017

Electronics

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Model predictive control (MPC) schemes employ dynamic models of a process within a receding horizon framework to optimize the behavior of a process. Although MPC has many benefits, a significant drawback is the large computational burden, especially in adaptive and constrained situations. In this paper, a computationally efficient self-tuning/adaptive MPC scheme for a simple industrial process plant with rate and amplitude constraints on the plant input is developed. The scheme has been optimized for real-time implementation on small, low-cost embedded processors. It employs a short (2-step) control horizon with an adjustable prediction horizon, automatically tunes the move suppression (regularization) parameter to achieve well-conditioned control, and presents a new technique for generating the reference trajectory that is robust to changes in the process time delay and in the presence of any inverse response. In addition, the need for a full quadratic programming procedure to handle input constraints is avoided by employing a quasi-analytical solution that optimally fathoms the constraints. Preliminary hardware-in-the-loop (HIL) test results indicate that the resulting scheme performs well and has low implementation overhead.

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Short-horizon Mpc With Amplitude-only Constraintsmentioning

confidence: 99%

Section: Constrained Model Predictive Controllermentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

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A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications

Short

Abugchem

2017

Electronics

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Constrained long‐range adaptive predictive control of arterial blood pressure

Kwok

Mutha

Shah

et al. 1991

Adaptive Control & Signal

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An adaptive blood pressure control system based on the long-range or finite-time-horizon prediction strategy known as generalized predictive control has been examined. The system was designed to run as a real-time, multitasking operating system providing real-time monitoring and control of mean arterial pressure. The ability to control in the presence of constraints was also incorporated into the system.Results from both unconstrained and constrained control runs were obtained from experiments on aneasthetized dogs. The problems caused by model-plant mismatch, varying time delays and nonlinearity were handled satisfactorily by the system. The disturbance rejection of the constrained algorithm is better than the unconstrained one. The results demonstrate the applicability of both the constrained and unconstrained generalized predictive control algorithm for regulation of mean arterial pressure.

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Output shaping in continuous‐time predictive control

Yavuzyilmaz

Demircioĝlu

2007

Adaptive Control & Signal

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In many control applications, it is expected that the system output tracks a desired reference signal. This paper describes a new approach in which the tracking problem is formulated as an output shaping problem. The approach uses the framework of predictive control and imposes the desired reference signal as a constraint on the output. Some simulation results and a real-time application are also given to illustrate the effectiveness of the proposed method.

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Generalised predictive control with input constraints

Cited by 96 publications

References 0 publications

A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications

A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications

Constrained long‐range adaptive predictive control of arterial blood pressure

Output shaping in continuous‐time predictive control

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