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

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

Distributed Model Predictive Consensus via the Alternating Direction Method of Multipliers

We propose a distributed optimization method for solving a distributed model predictive consensus problem. The goal is to design a distributed controller for a network of dynamical systems to optimize a coupled objective function while respecting state and input constraints. The distributed optimization method is an augmented Lagrangian method called the Alternating Direction Method of Multipliers (ADMM), which was introduced in the 1970s but has seen a recent resurgence in the context of dramatic increases in computing power and the development of widely available distributed computing platforms. The method is applied to position and velocity consensus in a network of double integrators. We find that a few tens of ADMM iterations yield closed-loop performance near what is achieved by solving the optimization problem centrally. Furthermore, the use of recent code generation techniques for solving local subproblems yields fast overall computation times.Comment: 7 pages, 5 figures, 50th Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, 201

Stability analysis of event-triggered anytime control with multiple control laws

To deal with time-varying processor availability and lossy communication channels in embedded and networked control systems, one can employ an event-triggered sequence-based anytime control (E-SAC) algorithm. The main idea of E-SAC is, when computing resources and measurements are available, to compute a sequence of tentative control inputs and store them in a buffer for potential future use. State-dependent Random-time Drift (SRD) approach is often used to analyse and establish stability properties of such E-SAC algorithms. However, using SRD, the analysis quickly becomes combinatoric and hence difficult to extend to more sophisticated E-SAC. In this technical note, we develop a general model and a new stability analysis for E-SAC based on Markov jump systems. Using the new stability analysis, stochastic stability conditions of existing E-SAC are also recovered. In addition, the proposed technique systematically extends to a more sophisticated E-SAC scheme for which, until now, no analytical expression had been obtained.Comment: Accepted for publication in IEEE Transactions on Automatic Contro

Distributed model predictive control for the atmospheric and vacuum distillation towers in a petroleum refining process

This paper develops a distributed model predictive control strategy for the atmospheric and vacuum distillation tower, which constitutes a key process involved in refining petroleum. When considering an MPC implementation, it is known that computational complexity can be reduced if the system is first decomposed into multiple smaller dimensional subsystems. Optimally exploiting the modern computer networks available in industry, a distributed model predictive control implementation is developed for the atmospheric and vacuum tower system, which is assumed to be part of a wider petroleum refining process comprised of a number of sub-systems connected in series. For each subsystem, given the availability of mutual communication channels between subsystems and by using an iterative calculation approach, it will be seen that Nash optimality can be achieved. A low-cost solution that is readily implementable online is seen to achieve the control objective. The effectiveness of the approach presented in the paper is validated by the results of nonlinear simulation experiments

Terminal spacecraft rendezvous and capture with LASSO model predictive control

The recently investigated $\ell_{\mathrm{asso}}$ model predictive control (MPC) is applied to the terminal phase of a spacecraft rendezvous and capture mission. The interaction between the cost function and the treatment of minimum impulse bit (MIB) is also investigated. The propellant consumption with $\ell_{\mathrm{asso}}$ MPC for the considered scenario is noticeably less than with a conventional quadratic cost and control actions are sparser in time. Propellant consumption and sparsity are competitive with those achieved using a zone-based $\ell_1$ cost function, whilst requiring fewer decision variables in the optimisation problem than the latter. The $\ell_{\mathrm{asso}}$ MPC is demonstrated to meet tighter specifications on control precision, and also avoids the risk of undesirable behaviours often associated with pure $\ell_1$ stage costs.This work was supported by Engineering and Physical Sciences Research Council Grants EP/G030308/1 and EP/G066477/1.This is an Author's Accepted Manuscript of an article published in {International Journal of Control}, first published online on 20 Aug 2013 [copyright Taylor & Francis], available online at: http://www.tandfonline.com/10.1080/00207179.2013.78960

Study on Neural Network Predictive Controller: Impact ofNetwork's Architecture and Plant-Model Mismatch

This final report explains about the extended research done on basic concept of the selected topic, which is Study on Neural Network Predictive Controller: Impact of Network's Architecture and Plant-model mismatch. The objective of the project is to study the effect ofdifferent network's architecture by manipulating the transfer fiinction, number ofneurons, weight and biases on NN-based Predictive Controller's performance. And to study the impact of parameters used in CSTR on the performance of the based model in Plant-Model Mismatch. In literature review section, NN-based Predictive Controller will be further discussed. The successful outcomes of this project can be applied in the industries in orderto help reducing the uncertainty or inaccuracy in process control

Distributed model predictive control of linear systems with persistent disturbances

This article presents a new form of robust distributed model predictive control (MPC) for multiple dynamically decoupled subsystems, in which distributed control agents exchange plans to achieve satisfaction of coupling constraints. The new method offers greater flexibility in communications than existing robust methods, and relaxes restrictions on the order in which distributed computations are performed. The local controllers use the concept of tube MPC – in which an optimisation designs a tube for the system to follow rather than a trajectory – to achieve robust feasibility and stability despite the presence of persistent, bounded disturbances. A methodical exploration of the trades between performance and communication is provided by numerical simulations of an example scenario. It is shown that at low levels of inter-agent communication, distributed MPC can obtain a lower closed-loop cost than that obtained by a centralised implementation. A further example shows that the flexibility in communications means the new algorithm has a relatively low susceptibility to the adverse effects of delays in computation and communication