# Jan MaciejowskiUniversity of Cambridge | CamĀ Ā·Ā Department of Engineering

Jan Maciejowski

## About

321

Publications

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## Publications

Publications (321)

In this paper, a recursive t-distribution noise model based maximum likelihood estimation algorithm for discrete-time dynamic state estimation is proposed. The proposed estimator is robust to outliers because the āthick tailā of the t-distribution reduces the effect of large errors in the likelihood function. A computationally efficient recursive a...

Techniques known as Nonlinear Set Membership prediction or Lipschitz Interpolation are approaches to supervised machine learning that utilise presupposed Lipschitz properties to perform inference over unobserved function values. Provided a bound on the true best Lipschitz constant of the target function is known a priori, they offer convergence gua...

The British government has been debating how to escape from the lockdown without provoking a resurgence of the COVID-19 disease. There is a growing recognition of the damage the lockdown has caused to economic and social life. This paper presents a simple costābenefit analysis inspired by optimal control theory and incorporating the SIR model of di...

This paper presents a self-triggered MPC controller design strategy for linear systems with state and input constraints. Based on the so-called relaxed dynamic programming inequality, the synthesis procedure determines both the updated MPC control action and the next triggering time. The resulting self-triggered MPC control law preserves stability...

Some recent results on a āGeneral Dissipativity Constraintā are briefly reviewed. Their application to a number of control scenarios (mostly involving MPC) is illustrated, using a quadratic form of the constraint and LMI machinery. Advantages and drawbacks of our approach are discussed briefly, as is potential future exploitation in networked syste...

This paper presents an optimal management (OM) strategy for distributed generation (DG) planning studies. The objective is the reduction of the CO2 emissions for the power generation on Jurong Island in Singapore. Different DG resources are investigated with solar panels, energy storage units, small gas turbines as well as controllable loads in add...

This paper presents a Guidance and Control (G&C) strategy to address 6-Degrees-Of-Freedom (6-DOF) spacecraft attitude and position control for future Rendezvous and Docking (RVD) missions. Future RVD missions, specifically when the target is uncooperative, are challenging as geometric constraints and parameter uncertainties are both present. In add...

In power system state estimation, the robust least absolute value robust dynamic estimator is well known. However, the covariance of the state estimation error cannot be obtained easily. In this article, an analytical equation is derived using influence function approximation to analyze the covariance of the robust least absolute value dynamic stat...

āGeneral Dissipativity Constraintā (GDC) is introduced to facilitate the design of stable feedback systems. A primary application is to MPC controllers when it is preferred to avoid the use of āstabilising ingredientsā such as terminal constraint sets or long prediction horizons. Some very general convergence results are proved under mild condition...

This paper investigates an optimal scheduling method for the operation of combined cycle gas turbines (CCGT). The objective is to minimize the CO
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emissions while supplying both electrical and thermal loads. This paper adopts a detailed model of th...

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...

This article presents an algorithm based on the Bernstein form of polynomials for solving the optimal power flow (OPF) problem in electrical power networks. The proposed algorithm combines local and global optimization methods and is therefore referred to as a āhybridā Bernstein algorithm in the context of this work. The proposed algorithm is a bra...

This paper proposes a generic methodology for combined-cycle gas turbine modeling. The main objectives are the estimation of the CO2emissions for specific units and their integration in an environmental power dispatch that considers several plants. First, a design procedure aims at calibrating the model using the information available from the manu...

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 advis...

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 s...

This paper presents stabilizing Model Predictive Controllers (MPC) in which prediction models are inferred from experimental data of the inputs and outputs of the plant. Using a nonparametric machine learning technique called LACKI, the estimated (possibly nonlinear) model function together with an estimation of Holder constant is provided. Based o...

In this paper, we propose an improved QP solver for embedded implementations of MPC controllers. We adopt a āreduced Hessianā approach for handling the equality constraints that arise in the well-known ābandedā formulation of MPC (in which the predicted states are not eliminated). Our key observation is that a banded basis exists for the null space...

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 sol...

In this paper, instead of the usual Gaussian noise assumption, $t$-distribution noise is assumed. A Maximum Likelihood Estimator using the most recent N measurements is proposed for the Auto-Regressive-Moving-Average with eXogenous input (ARMAX) process with this assumption. The proposed estimator is robust to outliers because the `thick tail' of t...

In contrast to the conventional model predictive control (MPC) approach to control of a given system where a positiveādefinite objective function is employed, economic MPC employs a generic cost which is related to the āeconomicsā of the process as the objective function in the regulation layer. Often, stability proofs of the closed-loop system are...

2016 Elsevier B.V.In contrast to the conventional model predictive control (MPC) approach to control of a given system where a positiveādefinite objective function is employed, economic MPC employs a generic cost which is related to the āeconomicsā of the process as the objective function in the regulation layer. Often, stability proofs of the clos...

Economic model predictive control, where a generic cost is employed as the objective function to be minimized, has recently gained much attention in model predictive control literature. Stability proof of the resulting closed-loop system is often based on strict dissipativity of the system with respect to the objective function. In this paper, star...

This paper presents a comparative study of two widely accepted model predictive control schemes based on mixed logical dynamical (MLD) and nonlinear modeling approaches with application to a continuous stirred tank reactor (CSTR) system. Specifically, we approximate the nonlinear behavior of a CSTR system with multiple local linear models in a MLD...

2016 Elsevier LtdEconomic model predictive control, where a generic cost is employed as the objective function to be minimized, has recently gained much attention in model predictive control literature. Stability proof of the resulting closed-loop system is often based on strict dissipativity of the system with respect to the objective function. In...

The traditional approach to optimal economic operation of industrial processes has been the use of a hierarchically structured control system. This hierarchical structure comprises a steady-state economic optimization layer that computes optimal operating set points of the system and a dynamic control layer using model predictive control to track t...

The traditional approach to optimal economic operation of industrial processes has been the use of a hierarchically structured control system. This hierarchical structure comprises a steady-state economic optimization layer that computes optimal operating set-points of the system and a dynamic control layer using model predictive control to track t...

The stabilizability conditions for constructing discrete-time control systems with General Dissipativity Constraint (GDC) is presented. The absolute value of a more general supply rate is considered in a GDC, which also takes the state and input interactions into account. When a system is stabilizable in the GDC sense, a control law exists such tha...

The feedback gains in state-of-the-art flight control laws for commercial aircraft are scheduled as a function of values such as airspeed, mass, and centre of gravity (CoG). If measurements or estimates of these are lost due to multiple simultaneous sensor failures, the pilot must revert to an alternative control law, or, in the ultimate case, dire...

The power distribution systems of medium or low voltages are currently upgraded by real-life demands in countries where the penetration of renewable energy is high, as well as for increasing the energy efficiency. Distribution automation and management systems are identified as the two new areas in the newly proposed IEC framework. Within this new...

Model Predictive Control (MPC) ā a feedback algorithm based on real-time optimization, has proved very successful for maximizing yield while minimizing energy usage in the refining and other industries for decades. Similar success is expected when applying MPC to energy efficiency problems in smart grids with chemical plants and industrial parks as...

Leveraging the advanced optimization algorithms for power systems is usually associated with the economically viable objectives for rational power generation, consumption stimulus and emission reduction. The late model predictive control (MPC) strategy that employs the economic-related cost function has proved to be highly potential for managing th...

We present a model predictive control based tracking problem for nonlinear systems based on global optimization. Specifically, we introduce a āBernstein global optimizationā procedure and demonstrate its applicability to the aforementioned control problem. This Bernstein global optimization procedure is applied to predictive control of a nonlinear...

The battery energy storage systems (BESSs) have been increasingly installed in the power system, especially with the growing penetration rate of the renewable energy sources. However, it is difficult for BESSs to be profitable due to high capital costs. In order to boost the economic value of BESSs, this paper proposes a hierarchical energy managem...

The feedback gains in state-of-the-art flight control laws for commercial aircraft are scheduled as a function of values such as airspeed, mass, and centre of gravity (CoG). If measurements or estimates of these are lost due to multiple simultaneous sensor failures, the pilot must revert to an alternative control law, or, in the ultimate case, dire...

This paper proposes the use of risk-sensitive costs in a model predictive controller (MPC) with Gaussian process (GP) models, for more effective online learning and control. Being a probabilistic model, a GP incorporates the uncertainty information due to imperfect knowledge of the system. The MPC then utilises this uncertainty information in a ris...

In this paper, we consider the problem of stabilizing discrete linear time invariant (LTI) systems with sparse input in both time and space via Model Predictive Control (MPC). As such, we firstly consider the case when the prediction horizon is of a given length, the control input is only active for some prediction steps (sparsity in time), i.e., t...

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 intercon...

We adapt and apply a known algorithm for Distributed Moving Horizon Estimation (DMHE) to power systems. In this distributed approach, the power system is partitioned into several control areas. At each time step the state of the whole system is estimated locally in each area, by solving a local optimization problem. A consensus weights update step...

This report shows that significant reduction in fuel use could be achieved by
the adoption of `free flight' type of trajectories in the Terminal Manoeuvring
Area (TMA) of an airport, under the control of an algorithm which optimises the
trajectories of all the aircraft within the TMA simultaneously while
maintaining safe separation. We propose the...

The data contains trajectories of 528 aircraft in the Gatwick TMA (approximately) between 00:00 and 23:59 on 14 September 2013. It was originally downloaded from 'FlightRadar24'. When the archive is unzipped it will contain a file 'README.txt' which explains in more detail what the data is. The data itself is in a Matlab-readable file 'gatwick_trim...

This is the accepted manuscript. It is currently embargoed pending publication.

The feedback gains in state-of-the-art flight control laws for commercial aircraft are scheduled as a function of values such as airspeed, mass, and centre of gravity (CoG). If estimates of these are lost due to multiple simultaneous sensor failures, the pilot must revert to an alternative control law, or, in the ultimate case, directly command con...

Economic model predictive control (eMPC), where an economic objective is used directly as the objective function of the control system, has gained much popularity in recent literature. However, with a purely economic objective, the control designer has no influence over the control performance of the process. In this paper, we propose a means of tu...

This paper deals with the control of systems for which there is a clear distinction between preferred and auxiliary actuators, the latter to be used only when the control error is large. Explicit MPC and exact penalty functions are used to show how āasso-MPC can implement this idea. Two āasso-MPC versions are reviewed, that allow the designer to im...

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 constr...

We apply stochastic Lyapunov theory to perform stability analysis of MPC controllers for nonlinear deterministic systems where the underlying optimisation algorithm is based on Markov Chain Monte Carlo (MCMC) or other stochastic methods. We provide a set of assumptions and conditions required for employing the approximate value function obtained as...

A key component of model predictive control (MPC) is the solving of quadratic programming (QP) problems. Interior point method (IPM) and active set method (ASM) are the most commonly employed approaches for solving general QP problems. This paper compares several performance aspects of the two methods when they are implemented on a FPGA for MPC app...

Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of
a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very
promising candidates for the first of these, and model predictive control has a proven capability for the...

This paper presents a development for the model predictive control (MPC) of nonlinear systems employing the quadratic dissipativity constraint (QDC). In this QDC strategy for nonlinear input-affine systems, a compound output vector is engaged to the supply rate such that the stability condition based on linear matrix inequality (LMI) can be rendere...

Nonlinear non-Gaussian state-space models are ubiquitous in statistics,
econometrics, information engineering and signal processing. Particle methods,
also known as Sequential Monte Carlo (SMC) methods, provide reliable numerical
approximations to the associated state inference problems. However, in most
applications, the state-space model of inter...

A model predictive control (MPC) scheme is deployed via the quadratic dissipativity constraint (QDC) in this
paper. Within the development, two new constraints, one a QDC-based stability constraint, the other an iterative-feasibility constraint, are derived for the model predictive control of linear systems. These two constraints are imposed on the...

The quadratic dissipativity constraint (QDC), a generalization of the asymptotically positive realness constraint
(APRC), has previously been introduced and developed into an enforced stability constraint for the model predictive control schemes. This paper presents the novel development for a static constrained-state feedback control of interconne...

Three decades have passed encompassing a flurry of research and commercial activities in model predictive control (MPC). However, the massive strides made by the academic community in guaranteeing stability through a state- space framework have not always been directly applicable in an industrial setting. This paper is concerned with a priori and/o...

Leveraging advanced estimation and control algorithms for power systems have always been associated with renewable energy sources, proactive power generation and management, consumer agility and emission reduction, as well as economically viable objectives. Model predictive control (MPC) with direct economic-related cost functions, called economic...

This paper presents how field-programmable gate arrays (FPGAs) are used to accelerate the Sequential Monte Carlo method for air traffic management. A novel data structure is introduced for a particle stream that enables efficient evaluation of constraints and weights. A parallel implementation for this streaming data structure is designed, and an a...

A constrained model predictive control technique for tracking is proposed for systems whose models become uncertain (for example after a sensor failure). A linear time invariant robust controller with integral action is used as a baseline and āreverse engineeredā into the form of a reduced order observer, steady state target calculator and control...

Alternative and more efficient computational methods can extend the applicability of model predictive control (MPC) to systems with tight real-time requirements. This paper presents a system-on-a-chip MPC system, implemented on a field-programmable gate array (FPGA), consisting of a sparse structure-exploiting primal dual interior point (PDIP) quad...

The Sequential Monte Carlo (SMC) method is a simulation-based approach to compute posterior distributions. SMC methods often work well on applications considered intractable by other methods due to high dimensionality, but they are computationally demanding. While SMC has been implemented efficiently on FPGAs, design productivity remains a challeng...

A method is proposed for on-line reconfiguration of the terminal constraint used to provide theoretical nominal stability guarantees in linear model predictive control (MPC). By parameterising the terminal constraint, its complete reconstruction is avoided when input constraints are modified to accommodate faults. To enlarge the region of feasibili...

Optimal planning and scheduling of trajectories for vehicles such as aircraft, road vehicles, or trains, generally involves non-convex optimization. Such problems are frequently regarded as intractable. But we show that it is effective to tackle such problems using stochastic optimization methods, even for real-time use, as in model predictive cont...

SUMMARYA field programmable gate array (FPGA) based model predictive controller for two phases of spacecraft rendezvous is presented. Linear time-varying prediction models are used to accommodate elliptical orbits, and a variable prediction horizon is used to facilitate finite time completion of the longer range manoeuvres, whilst a fixed and reced...

This paper demonstrates a method for finding the cost function and state observer to be used in model predictive control (MPC) so that when constraints are inactive a pre-existing low order controller is reproduced. The MPC controller thereby inherits its desirable properties. This can be used as a baseline for further tuning. The available degrees...

European Control Conference 2013 (ECC13), July 17-19, Zurich, Switzerland

This article presents an approach for mapping real-time applications based on particle filters (PFs) to heterogeneous reconfigurable systems, which typically consist of multiple FPGAs and CPUs. A method is proposed to adapt the number of particles dynamically and to utilise runtime reconfigurability of FPGAs for reduced power and energy consumption...

Model Predictive Control (MPC) is an optimization-based control strategy
that is considered extremely attractive in the autonomous space
rendezvous scenarios. The Online ReconĀ¦guration Control System
and Avionics Architecture (ORCSAT) study addresses its applicability in
Mars Sample Return (MSR) mission, including the implementation of the
develope...

This paper presents the parallelisation of a Sequential Monte Carlo algorithm, and the associated changes required when applied to the problem of conflict resolution and aircraft trajectory control in air traffic management. The target problem is non-linear, constrained, non-convex and multi-agent. The new method is shown to have a 98.5% computatio...

This paper investigates the robustness of a soft constrained LTI MPC for set-point tracking, using an ā1-regularised cost. The MPC using this type of cost (informally dubbed āasso-MPC) is suitable, for instance, for redundantly-actuated systems. This is because of its ability to select a set of preferred actuators, leaving the other ones at rest fo...

A reconfigurable field-programmable gate array (FPGA)-based predictive controller based on Nesterov's fast gradient method is designed using Simulink and converted to VHDL using Mathworks' HDL Coder. The implementation is verified by application to a spacecraft rendezvous and capture scenario, with communication between the FPGA and a simulation of...

This is the author's version of an article that has been published in IEEE Transactions on Automatic Control. Changes were made to this version by the publisher prior to publication. The final version of record is available at: http://dx.doi.org/10.1109/TAC.2013.2258781
(c) 2013 IEEE. Personal use of this material is permitted. Permission from IEE...

The brushless doubly-fed machine exhibits rotor-speed-dependent, cross-coupling effects between inputs and outputs when vector control is implemented. Manipulation of the model equations shows that these effects are represented by rotation angles. A parameter-independent decoupling method is presented which reduces these cross-coupling disturbances...

The recently investigated lasso 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 is also investigated. The propellant consumption with lasso MPC for the considered scenario is noticeably less than with a...

Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the...

The solution time of the online optimization problems inherent to Model Predictive Control (MPC) can become a critical limitation when working in embedded systems. One proposed approach to reduce the solution time is to split the optimization problem into a number of reduced order problems, solve such reduced order problems in parallel and selectin...

This paper reports on the use of a parallelised Model Predictive Control, Sequential Monte Carlo algorithm for solving the problem of conflict resolution and aircraft trajectory control in air traffic management specifically around the terminal manoeuvring area of an airport. The target problem is nonlinear, highly constrained, non-convex and uses...