Adaptive model predictive control for constrained nonlinear systems

Adetola, Veronica; DeHaan, Darryl; Guay, Martin

doi:10.1016/j.sysconle.2008.12.002

Cited by 204 publications

(97 citation statements)

References 11 publications

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“…k=t with respect to the control sequence UtC) (where UtC) = {u(t),u(t + 1),··· ,u(t + N -I)}) over the prediction horizon [t, t + N], subject to the dynamic equation (1) and the mixed state and input constraints (2). Here t is the current time instant, and N is the prediction horizon.…”

Section: A Problem Formulationmentioning

confidence: 99%

“…In particular, the "separation principle" used for most certainty equivalence designs in indirect adaptive control may not hold [2,3] . In addition, the control law does not have a closed-form solution and has to be computed numerically.…”

Section: Introductionmentioning

confidence: 99%

“…Another approach to integrating the adaptation into MPC is by ensuring sufficient "robustness" while the estimation is still in progress. This is the approach taken, for instance, by [2,[15][16][17], where a set valued description of parameter uncertainty is adapted, and a robust (min max) MPC is implemented for the identified parameter set. While this approach can deal with constraints, the underlying min-max feedback MPC problem can be computationally prohibitive to solve, especially in real-time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive model predictive control in the IPA-SQP framework

Sun

Park

Kolmanovsky

et al. 2013

52nd IEEE Conference on Decision and Control

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Ahstract-In this paper, we propose an approach and a specific algorithm to integrate a parameter estimation with the receding horizon model predictive control. We derive this adaptive MPC algorithm based on the integrated perturbation analysis and sequential quadratic programming (IPA-SQP) framework. Previously this approach was exploited for repeated constrained optimization in MPC when the initial conditions change. It is now shown that a similar algorithm can be derived to perform MPC updates when model parameters change. The detailed algorithm derivation is presented , along with discussions on the performance and implementation. An example based on the nonlinear dynamics of an inverted pendulum on a cart is included to demonstrate the effectiveness of the proposed algorithm.

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Section: A Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive model predictive control in the IPA-SQP framework

Sun

Park

Kolmanovsky

et al. 2013

52nd IEEE Conference on Decision and Control

View full text Add to dashboard Cite

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“…This can make the realization of the algorithms very difficult for real time control. With the increasing complexity of control systems, more rigorous solutions for the saturation control problem are needed, and new approaches have been proposed as the aim of allowing for general designs with stability and performance guarantees [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Dynamic Surface Control of a Class of Nonlinear Systems With Unknown Direction Control Gains and Input Saturation

Jian-jun

Zhang

2015

IEEE Trans. Cybern.

212

110

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In this paper, adaptive neural network based dynamic surface control (DSC) is developed for a class of nonlinear strict-feedback systems with unknown direction control gains and input saturation. A Gaussian error function based saturation model is employed such that the backstepping technique can be used in the control design. The explosion of complexity in traditional backstepping design is avoided by utilizing DSC. Based on backstepping combined with DSC, adaptive radial basis function neural network control is developed to guarantee that all the signals in the closed-loop system are globally bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design parameters. Simulation results demonstrate the effectiveness of the proposed approach and the good performance is guaranteed even though both the saturation constraints and the wrong control direction are occurred.

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“…Different strategies to reduce the conservativeness of robust MPC approaches have been studied in the literature using the information obtained about the system [9]. Recently, [10] presented a dual control approach to linear MPC using expected cost and uncertainty reduction via iterative learning using recursive least squares method and [11] presented a method that takes into account in the prediction the expected reduction of the uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Towards dual robust nonlinear model predictive control: A multi-stage approach

Thangavel

Lucia

Paulen

et al. 2015

2015 American Control Conference (ACC)

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This paper presents an approach to dual robust nonlinear model predictive control (NMPC). Dual control is traditionally formulated as a technique that seeks to solve the trade-off between probing actions, which result in a better estimation of the unknown parameters, and the optimal operation of the uncertain dynamic system. We propose a dual robust NMPC method based on the multi-stage approach that represents the uncertainty as a scenario tree of its possible realizations. We achieve a dual control formulation by taking explicitly into account the reduction of the uncertainty that future probing actions would provide over the prediction horizon. Using such formulation, our approach does not require any a priori decision on the relative importance of the probing actions with respect to the optimal operation of the system, as proposed in some recent approaches. The results obtained for a chemical engineering example show the advantages of using a dual NMPC approach based on multi-stage NMPC over the sequential use of parameter estimation with a posteriori approximation of the covariance of the parameter estimates.

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Adaptive model predictive control for constrained nonlinear systems

Cited by 204 publications

References 11 publications

Adaptive model predictive control in the IPA-SQP framework

Adaptive model predictive control in the IPA-SQP framework

Adaptive Dynamic Surface Control of a Class of Nonlinear Systems With Unknown Direction Control Gains and Input Saturation

Towards dual robust nonlinear model predictive control: A multi-stage approach

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