Performance evaluation of multiplexed model predictive control for a large airliner in nominal and contingency scenarios

Model predictive control allows systematic han- dling of physical and operational constraints through the use of constrained optimisation. It has also been shown to successfully exploit plant redundancy to maintain a level of control in scenarios when faults are present. Unfortunately, the computa- tional complexity of each individual iteration of the algorithm to solve the optimisation problem scales cubically with the number of plant inputs, so the computational demands are high for large MIMO plants. Multiplexed MPC only calculates changes in a subset of the plant inputs at each sampling instant, thus reducing the complexity of the optimisation. This paper demonstrates the application of multiplexed model predictive control to a large transport airliner in a nominal and a contingency scenario. The performance is compared to that obtained with a conventional synchronous model predictive controller, designed using an equivalent cost function.This work was supported by Engineering and Physical Sciences Research Council grant number EP/G030308/1.The 2012 American Control Conference, June 27-29, Montreal, Canada

Embedded ADMM-based QP solver for MPC with polytopic constraints

We propose an algorithm for solving quadratic programming (QP) problems with inequality and equality constraints arising from linear MPC. The proposed algorithm is based on the ‘alternating direction method of multipliers’ (ADMM), with the introduction of slack variables. In comparison with algorithms available in the literature, our proposed algorithm can handle the so-called sparse MPC formulation with general inequality constraints. Moreover, our proposed algorithm is suitable for implementation on embedded platforms where computational resources are limited. In some cases, our algorithm is division-free when certain fixed matrices are computed offline. This enables our algorithm to be implemented in fixed-point arithmetic on a FPGA. In this paper, we also propose heuristic rules to select the step size of ADMM for a good convergence rate.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ECC.2015.733106

Improved Bernstein Optimization Based Nonlinear Model Predictive Control Scheme for Power Systems

© 2017 This paper presents a improved Bernstein global optimization algorithm based model predictive control (MPC) scheme for the nonlinear systems. A new improvement in the Bernstein algorithm is the introduction of a box pruning operator, which during a branch-and-bound search, discard portions of the solution search space that do not contain global solution, thereby speeding up the algorithm. The applicability of this MPC scheme is demonstrated with a simulation studies on a nonlinear single machine infinite bus power system over a wide range of operating conditions. The simulation results show improvement in the system damping and settling time compared with the classical power system stabilizer and partial feedback linearization control schemes.National Research Foundation, Singapore

Banded Null Basis and ADMM for Embedded MPC

© 2017 In this paper, we propose an improved QP solver for embedded implementations of MPC controllers. We adopt a “reduced Hessian” approach for handling the equality constraints that arise in the well-known “banded” formulation of MPC (in which the predicted states are not eliminated). Our key observation is that a banded basis exists for the null space of the banded equality-constraint matrix, and that this leads to a QP of the same size as the “condensed” formulation of MPC problems, which is considerably smaller than the “banded” formulation. We use the Alternating Direction Method of Multipliers (ADMM) - which is known to be particularly suitable for embedded implementations - to solve this smaller QP problem. Our C implementation results for a particular MPC example (a 9-state, 3-input quadrotor) show that our proposed algorithm is about 4 times faster than an existing well-performing ADMM variant (“indirect indicator” ADMM or “iiADMM”) and 3 times faster than the well-known QP solver CVXGEN. The convergence rate and code size of the proposed ADMM variant is also comparable with iiADMM.National Research Foundation, Singapore

Nonlinear model predictive control based on Bernstein global optimization with application to a nonlinear CSTR

© 2016 EUCA. We present a model predictive control based tracking problem for nonlinear systems based on global optimization. Specifically, we introduce a 'Bernstein global optimization' procedure and demonstrate its applicability to the aforementioned control problem. This Bernstein global optimization procedure is applied to predictive control of a nonlinear CSTR system. Its strength and benefits are compared with those of a sub-optimal procedure, as implemented in MATLAB using fmincon function, and two well established global optimization procedures, BARON and BMIBNB.National Research Foundation, Singapore

Optimal nonlinear model predictive control based on Bernstein polynomial approach

© 2017 IEEE. In this paper, we compare the performance of Bernstein global optimization algorithm based nonlinear model predictive control (NMPC) with a power system stabilizer and linear model predictive control (MPC) for the excitation control of a single machine infinite bus power system. The control simulation studies with Bernstein algorithm based NMPC show improvement in the system damping and settling time when compared with respect to a power system stabilizer and linear MPC scheme. Further, the efficacy of the Bernstein algorithm is also compared with global optimization solver BMIBNB from YALMIP toolbox in terms of NMPC scheme and results are found to be satisfactory.National Research Foundation, Singapore

A generic method to model carbon emission of combined cycle for environmental power dispatch

© 2017 IEEE. This paper proposes a generic methodology for combined cycle gas turbines (CCGT) modeling. The main objectives are the estimation of the CO2 emissions for specific units and their integration in an environmental power dispatch that considers several plants. At first a design procedure aims at calibrating the model using the sparse information advised by the manufactures. Off-design points are also investigated in order to estimate the CO2 emissions on the whole operating range of the units. The obtained results show a good consistency with the emission coefficients found in the literature for that type of units. Then those carbon costs are used as input parameters for a unit commitment problem (UC). The Mixed Integer Linear Programming (MILP) formulation minimizes the global emissions for a set of different units on Jurong Island in Singapore. The grid emission factor finally obtained for the simulated network displays values close to the registered field data which validates the developed model.National Research Foundation, Singapore

Application of quadratically-constrained model predictive control in power systems

Simulations for the quadratically-constrained model predictive control (qc-MPC) with power system linear models are studied in this work. In qc-MPC, the optimization is imposed with two additional constraints to achieve the closed-loop system stability and the recursive-feasibility simultaneously. Instead of engaging the traditional terminal constraint for MPC, both constraints in qc-MPC are imposed on the first control vector of the MPC control sequence. As a result, qc-MPC has the potential for further extension to the control of network centric power systems. The algorithm of qc-MPC has been developed in a previous paper. Here, simulation studies with small-signal linear models of three typical power systems are presented to demonstrate its efficacy. We also develop a computational strategy for the decentralized static state-feedback control using the same quadratic dissipativity constraint as of the qc-MPC. Only state constraints are considered in the state feedback design. A comparison is then provided in the simulation study of qc-MPC relatively to the constrained-state feedback control.This publication is made possible by the Singapore National Research Foundation under its Campus for Research Excellence And Technological Enterprise (CREATE) programmeThis is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICARCV.2014.706430

Multiplexed model predictive control of interconnected systems

A Multiplexed Model Predictive Control (MMPC) scheme with Quadratic Dissipativity Constraint (QDC) for interconnected systems is presented in this paper. A centralized MMPC is designed for the global system, wherein the controls of subsystems are updated sequentially to reduce the computational time. In MMPC, the global state vector of the interconnected system is required by the optimization. The QDC is converted into an enforced stability constraint for the MMPC as an alternative to the terminal constraint and terminal cost in this approach. The nominal recursive feasibility for the global system and the iterative feasibility for the local subsystems are obtained via set operations on the invariant sets. The admissible sets for the control inputs are obtained and employed in this approach for the QDC-based stability constraint. The set operations are speed up by multiple magnitudes thanks to the implementation of multiplexed inputs in MMPC. Numerical simulations with Automatic Generation Control (AGC) in power systems having tie-lines demonstrate the theoretical development.The authors acknowledge the support by the Singapore National Research Foundation (NRF) under its Campus for Research Excellence And Technological Enterprise (CREATE) programme and the Cambridge Centre for Advanced Research in Energy Efficiency in Singapore (Cambridge CARES), C4T project.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/CDC.2015.740256

A trust-region based sequential linear programming approach for AC optimal power flow problems

© 2018 Elsevier B.V. This study proposes a new trust-region based sequential linear programming algorithm to solve the AC optimal power flow (OPF) problem. The OPF problem is solved by linearizing the cost function, power balance and engineering constraints of the system, followed by a trust-region to control the validity of the linear model. To alleviate the problems associated with the infeasibilities of a linear approximation, a feasibility restoration phase is introduced. This phase uses the original nonlinear constraints to quickly locate a feasible point when the linear approximation is infeasible. The algorithm follows convergence criteria to satisfy the first order optimality conditions for the original OPF problem. Studies on standard IEEE systems and large-scale Polish systems show an acceptable quality of convergence to a set of best-known solutions and a substantial improvement in computational time, with linear scaling proportional to the network size.National Research Foundation, Singapore