Embedded Optimization Methods for Industrial Automatic Control

Ferreau, Hans Joachim; Almér, Stefan; Verschueren, Robin; Diehl, Moritz; Frick, Damian; Domahidi, Alexander; Jerez, Juan L.; Stathopoulos, Giorgos; Jones, Colin N.

doi:10.1016/j.ifacol.2017.08.1946

Cited by 32 publications

(8 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One popular technique consists of the real-time iteration (RTI) algorithm that performs a single SQP iteration per time step, in combination with a sufficiently high sampling rate and a prediction-based warm starting in order to allow for closed-loop stability of the system [16]. The RTI algorithm can be implemented efficiently based on (fixed-step) integration schemes with tailored sensitivity propagation for discretization and linearization of the system dynamics [36] in combination with structure-exploiting quadratic programming solvers [21]. In addition, a lifted algorithm implementation has been proposed in [37] to directly embed the iterative procedure of implicit integration schemes, e.g., collocation methods, within a Newton-type optimization framework for optimal control.…”

Section: Arxiv:190308720v1 [Mathoc] 20 Mar 2019mentioning

confidence: 99%

“…(7). Therefore, state of the art block-structured QP solvers can be used, for which an overview can be found in [21]. After solving the condensed QP in (55), the collocation variables can be obtained from the expansion step in Eq.…”

Section: Tailored Structure Exploitation For Direct Collocationmentioning

confidence: 99%

“…They allow a model-based design framework, in which the system dynamics, performance metrics and constraints can directly be taken into account. Receding horizon techniques such as model predictive control (MPC) and moving horizon estimation (MHE) have been studied extensively because of their desirable properties [30] and these optimization-based techniques have already been applied in a wide range of applications [21]. One of the main practical challenges in implementing such an optimization-based predictive control or estimation scheme, lies in the ability to solve the corresponding nonlinear and generally non-convex optimal control problem (OCP) under strict timing constraints and typically on embedded hardware with limited computational capabilities and available memory.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adjoint-based SQP method with block-wise quasi-Newton Jacobian updates for nonlinear optimal control

Hespanhol

Quirynen

2019

Optimization Methods and Software

View full text Add to dashboard Cite

Nonlinear model predictive control (NMPC) generally requires the solution of a non-convex optimization problem at each sampling instant under strict timing constraints, based on a set of differential equations that can often be stiff and/or that may include implicit algebraic equations. This paper provides a local convergence analysis for the recently proposed adjoint-based sequential quadratic programming (SQP) algorithm that is based on a block-structured variant of the twosided rank-one (TR1) quasi-Newton update formula to efficiently compute Jacobian matrix approximations in a sparsity preserving fashion. A particularly efficient algorithm implementation is proposed in case an implicit integration scheme is used for discretization of the optimal control problem, in which matrix factorization and matrix-matrix operations can be avoided entirely. The convergence analysis results as well as the computational performance of the proposed optimization algorithm are illustrated for two simulation case studies of nonlinear MPC.

show abstract

Section: Arxiv:190308720v1 [Mathoc] 20 Mar 2019mentioning

confidence: 99%

Section: Tailored Structure Exploitation For Direct Collocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adjoint-based SQP method with block-wise quasi-Newton Jacobian updates for nonlinear optimal control

Hespanhol

Quirynen

2019

Optimization Methods and Software

View full text Add to dashboard Cite

show abstract

“…In this case, it can be solved quickly and efficiently, even for larger problem instances [3], [20]. There are many excellent software tools available, some of which also support code generation for embedded systems [6], [12], [18], [21].…”

Section: Computational Complexitymentioning

confidence: 99%

A collision avoidance system at intersections using Robust Model Predictive Control

Schildbach

Soppert

Borrelli

2016

2016 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

Deadbeat Robust Model Predictive Control (DRMPC) is introduced as a new approach of Robust Model Predictive Control (RMPC) for linear systems with additive disturbances. Its main idea is to completely extinguish the effect of the disturbances in the predictions within a small number of time steps, called the deadbeat horizon. To this end, explicit deadbeat input sequences are calculated for the vertices of the disturbance set. They generalize to a nonlinear disturbance feedback policy for all disturbances by means of a barycentric function. Similar to previous approaches, this disturbance feedback policy can be either part of the online optimization (Online DRMPC) or pre-calculated during the design phase of the controller (Offline DRMPC). The main advantage over all other RMPC approaches is that no Robust Positive Invariant (RPI) set has to be calculated, which is often intractable for systems with higher dimensions. Nonetheless, for Online DRMPC and Offline DRMPC recursive feasibility and input-to-state stability can be guaranteed. A small numerical example compares the two versions of DRMPC and demonstrates that the performance of DRMPC is competitive with other state-of-the-art RMPC approaches. Its main advantage is its easy extension to linear time-varying (LTV) and linear parameter-varying (LPV) systems.

show abstract

“…Maintaining an industrial process under safe conditions is the main purpose of a control system [1]. For implementing a certain control process, the production plants and their embedded systems must be modernized and optimized [2], this allows to monitor the variables that are being controlled and grants that the plant is working under safety conditions for the operator and even for the control system. It is also made to establish the method or the way in which the system will be regulated, this adaptation is known as instrumentation [3].…”

Section: Introductionmentioning

confidence: 99%

Experimental Development of Fuzzy Controllers for Thermal and Pneumatic Processes

et al. 2021

View full text Add to dashboard Cite

In this project, a Fuzzy control system is proposed in an industrial process training module with two independent systems between them, one thermal and the other pneumatic. The control algorithm is developed in Python language v3.6 executed by a Raspberry Pi B+, both controllers depend on the error and change in error that are updated in times of 2 s and 1 s, for temperature and pressure respectively, communication with the plants uses A/D and D/A converters, the thermal Fuzzy was analyzed with three temperature references [50,100 and 150]°C, with a rise time of 191 s, 360 s and 505 s; steady state error of 5.5%, 0.7% y 0.7%, in the pneumatic system the speed of change between references is evaluated from 10 psi to 15 psi varying the activation of the compressor at the beginning of the experiments, the settling times obtained are 111 s and 106 s, with the compressor off the result is 116 s and 88 s, besides a maximum excess of 13% with inherent oscillations to the type system that are in an acceptable range.

show abstract

Embedded Optimization Methods for Industrial Automatic Control

Cited by 32 publications

References 89 publications

Adjoint-based SQP method with block-wise quasi-Newton Jacobian updates for nonlinear optimal control

Adjoint-based SQP method with block-wise quasi-Newton Jacobian updates for nonlinear optimal control

A collision avoidance system at intersections using Robust Model Predictive Control

Experimental Development of Fuzzy Controllers for Thermal and Pneumatic Processes

Contact Info

Product

Resources

About