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Ocean wave energy is one of the most concentrated sources of renewable energy. However, until now it has not reached the economic feasibility required to be commercialised. To improve the efficiency of wave energy converters, several advanced control strategies have been proposed, including Model Predictive Control (MPC). Nevertheless, the computational burden of the underlying optimisation problem is a drawback of conventional (Full-Degree of Freedom, F-DoF) MPC, which typically limits its application for real-time control of systems. In this paper, a Moving Window Blocking (MWB) approach is proposed to speed-up the time required for each optimisation problem by reducing the number of decision variables using input parameterised solutions. Numerical simulation of a generic single device point absorber wave energy converter controlled by this scheme confirms the potential of this approach.

This work focuses on Distributed Secondary Control (DSC) technique, for frequency regulation and Economic Load Dispatch of Microgrid (MG). The fluctuating nature and large quantity of Distributed Energy Resources (DER) in an autonomous MG result in complex control requirements, demanding fast and robust response. The contemporary DSC schemes are mostly based on Distributed Averaging Integration techniques, with slow response. This paper proposes Distributed Model Predictive based Secondary Control (DMPSC) which effectively complies with the control requirements of MG. DMPSC requires each DER-node to solve a local optimization problem with the cost function penalizing the deviation of states from their desired values and the differences between the assumed and predicted values. The desired-states are based on local intermediate-optimum values, computed using local and neighbouring information. Equality based terminal constraints are introduced to ensure the stability, where each node is forced to reach the desired-state value at the end of prediction horizon. The terminal-consensus of the network affirms convergence of the desired-states to a global optimal point of the network. The asymptotic stability of the proposed control is proved by using the sum of local cost-functions as a candidate Lyapunov function. Simulation results validate the effectiveness of the proposed control scheme.

This paper discusses some of the reasons for producing take home laboratory kits. This is then supplemented by detailed presentation of three different types of take home kit, alongside the motivations for their design and an evaluation of their efficacy with students.

This paper discusses some of the reasons for and consequences of changing assessment types away from traditional paper based exams. More specifically, consideration is given to the quiz environments available on virtual learning environments. Of particular interest to the reader are two core aspects: (i) the authenticity of the assessment for holistic assessment and (ii) managing student expectations of the process. The latter of these is the most challenging.

Presents information on the CS Society Technical Committee on Control Education: A First Course in Systems and Control Engineering.

Estimating the 6D pose of objects is an essential part of a robot’s ability to perceive their environment. This paper proposes a method for detecting a known object and estimating its 6D pose from a single RGB image. Unlike most of the state-of-the-art methods that deploy PnP algorithms for estimating 6D pose, the method here can output the 6D pose in one step. In order to obtain estimation accuracy that is comparable to RGB-D based methods, an efficient refinement algorithm, called contour alignment (CA), is presented; this can increase the predicted 6D pose accuracy significantly. We evaluate the new method in two widely used benchmarks, LINEMOD for single object pose estimation and Occlusion-LINEMOD for multiple objects pose estimation. The experiments show that the proposed method surpasses other state-of-the-art prediction approaches.

The availability of predictions of future system inputs has motivated research into preview control to improve set-point tracking and disturbance rejection beyond that achievable via conventional feedback control. The design of preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, is often performed in a monolithic nature, coupling the feedback and feed-forward problems. This can create problems, such as: (i) an additional feedback loop is introduced by MPC, which alters the closed-loop dynamics of the existing feedback compensator, potentially resulting in a deterioration of the nominal sensitivities and robustness properties of an existing closed-loop and (ii) the default preview action from MPC can be poor, degrading the original feedback control performance. In our previous work, the former problem is addressed by presenting a modular MPC design on top of a given output-feedback controller, which retains the nominal closed-loop robustness and frequency-domain properties of the latter, despite the addition of the preview design. In this paper, we address the second problem, the preview compensator design in the modular MPC formulation. Specifically, we derive the key conditions that ensure, under a given closed-loop tuning, the preview compensator within the modular MPC formulation is systematic and well-designed in a sense that the preview control actions complement the existing feedback control law rather than opposing it. In addition, we also derive some important results, showing that the modular MPC can be implemented in a cascade over any given linear controllers and the proposed conditions hold, regardless of the observer design for the modular MPC. The key benefit of the modular MPC is that the preview control with constraint handling can be implemented without replacing the existing feedback controller. This is illustrated through some numerical examples.

There are many software tools available for students to implement PID control laws, but these software tools are not all equally flexible, easy to use, accessible and hence effective. This paper gives a brief review of three tools: (i) author built tools in MATLAB (Mathworks, 2017); (ii) TSC software (TSC, 2017) and (iii) PISIM software (Postlethwaite, 2017). The evaluation considers aspects such as convenience and cost for the academic as well as accessibility and useability for the students. The main results in the paper are written in such a way as to assist staff in deciding in what software its worth investing time and effort developing resources and student activities.

This paper presents and implements a control structure solution based on MPC for two control problems affecting gas phase train in the existing oil and gas production plants: The disturbance growth in the series connected process and the control system dependency on operators. This work examines the integration of small size MPC’s with the classical PID control system to handle interactive control loops in three series gas treatment processes.

The efficacy of virtual laboratories in supporting student learning is now well accepted. This paper builds on that assumption and considers where there are obvious gaps in provision and a need for a response to typical student criticisms. Specifically here, the focus is on students who complete a control course and do not understand why they have studied it, why it is relevant and so forth. Four new interactive laboratories are introduced which are designed for aerospace engineers and chemical engineers. The former two focus on a context, to help aerospace students see a scenario where lead and lag design might be needed. The latter two, for chemical engineers, focus on uncertainty and why control is therefore essential to ensure good process outcomes.

This paper presents two significant contributions to the understanding of Predictive Functional Control (PFC). First, it gives novel insights and explanations into a poorly understood issue, that is the weak link between PFC tuning parameters and the resulting closed-loop behaviour. This new understanding is then exploited to proposed a modification to the existing PFC algorithm which creates a much stronger tuning link while retaining the critical properties of elementary coding and understanding. The efficacy of the proposal is demonstrated on several numerical examples.

Despite its popularity in industry and obvious efficacy, predictive functional control has few rigorous a priori stability results in the literature. In many cases, common sense and intuition with some trial and error are the main design tools. This paper seeks to tackle that gap by providing some analysis of the control law and showing what forms of stability assurances can be given and how these depend on the user choices of coincidence horizon and desired closed-loop pole. The conditions are separated into necessary, but not sufficient conditions for stability and, conversely, sufficient but not necessary conditions. Numerical examples demonstrate the efficacy of these conditions and the ease of use.

This paper gives new insight and design proposals for Predictive Functional Control (PFC) algorithms. Common practice and indeed a requirement of PFC is to select a coincidence horizon greater than one for high-order systems and for the link between the design parameters and the desired dynamic to be weak. Here the proposal is to use parallel first-order models to form an independent prediction model and show that with these it is possible both to use a coincidence horizon of one and moreover to obtain precisely the desired closed-loop dynamics. It is shown through analysis that the use of a coincidence horizon of one greatly simplifies coding, tuning, constraint handling and implementation. The paper derives the key results for high-order and non-minimum phase processes and also demonstrates the flexibility and potential industrial utility of the proposal.

The prime contribution of this paper is to provide a large scale system (LSS) model for the gas phase operation in upstream oil and gas plants. The process model consists of the three main gas conditioning processes which exist in most upstream oil and gas processing plants; these are gas sweetening, gas dehydration, and hydrocarbon dew-pointing. The function of such a model is to provide a realistic process representation to test and verify different process control approaches, specifically those which deal with highly interactive control loops.

This paper gives a brief review of work on virtual and remote laboratories along with a critique of their strengths and weaknesses. This is used as a motivation for a proposed method for producing virtual laboratories which, while relatively crude in comparison with professional alternatives, are much cheaper and faster to produce and thus can be created using the skill set and time of normal academics. Some examples and the coding processes are demonstrated.

A nonlinear gain scheduling control strategy is proposed for a concentrated solar thermal power plant. The strategy involves the identification of local linear time-invariant state space models around a family of operating points, the design of corresponding local linear dual mode model-based predictive controllers and the selection of an appropriate scheduling variable to govern the switching. The local models are estimated directly from input-output data using a subspace identification method while taking into account the frequency response of the plant. Input-output data are obtained from a nonlinear simulation model of the plant rather than the plant itself. The effectiveness of the proposed control strategy in terms of tracking and disturbance rejection is evaluated through two different scenarios created in a nonlinear simulation environment.

A model predictive control strategy for a concentrated solar thermal power plant is proposed. Design of the proposed controller is based on an estimated linear time-invariant state space model around a nominal operating point. The model is estimated directly from input-output data using the subspace identification method and taking into account the frequency response of the plant. Input-output data are obtained from a nonlinear distributed parameter model of a plant rather than the plant itself. Effectiveness of the proposed control strategy in terms of tracking and disturbance rejection is evaluated through two different scenarios created in a nonlinear simulation environment.

This paper gives a focussed summary of good practice taken primarily from engineers who are\ud responsible for teaching topics related to systems and control. This engineering specialisation\ud allows the paper to give some degree of focus in the discussions around laboratories, software\ud and assessment, although naturally many of the conclusions are generic. A key intention is\ud to provide a summary document or survey paper which can be used by academics as a start\ud point in studies of what is effective in the discipline. It is also hoped that such a summary\ud will will be useful to engineering institutions in drawing together and disseminating open\ud access resources that are freely available to the community at large.

Laboratories are a core part of any engineering degree, but access to laboratories is typically limited due to a combination of timetable, space and equipment restrictions [1]. In more recent years that has been a significant growth in so-called virtual laboratories (VL), that is laboratory like activities that can be accessed via software or even a web interface, e.g. [2], [3], [5]. The advantage of VL is that the accessibility can potentially be improved to 24/7 and often these may be available on a student's own computing device thus also giving no space restrictions. Improvements in accessibility mean that staff can integrate VL far more easily into the curriculum and student independent study schedule with the consequence that, in principle, students can learn more effectively.

Predictive functional control (PFC) is a highly successful strategy within industry, but for cases with challenging dynamics the most effective tuning approaches are still an active research area. This paper shows how one can deploy some insights from the more traditional model predictive control literature in order to enable systematic tuning and in particular, to ensure that the key PFC tuning parameter, that is the desired closed-loop time constant, is effective. In addition to enabling easier and more effective tuning, the proposed approach has the advantage of being simple to code and thus retaining the simplicity of implementation and tuning that is a key selling point of PFC. This paper focuses on design for open-loop unstable and also processes with significant under-damping in their open-loop behaviour.

The use of blade individual pitch control (IPC) offers a means of reducing the harmful turbine structural loads that arise from the uneven and unsteady forcing from the oncoming wind. In recent years, two different and competing IPC techniques have emerged that are characterised by the specific loads that they are primarily designed to attenuate. In the first instance, methodologies, such as single-blade control and Clarke transform-based control, have been developed to reduce the unsteady loads on the rotating blades, whilst tilt-yaw control and its many variants instead target load reductions in the non-rotating turbine structures, such as the tower and main bearing. Given the seeming disparities between these controllers, the aim of this paper is to show the fundamental performance similarities that exist between them and hence unify research in this area. Specifically, we show that single-blade controllers are equivalent to a particular class of tilt-yaw controller, which itself is equivalent ...

This paper gives an analysis of the efficacy of PFC strategies. PFC is widely used in industry for simple loops with constraint handling, as it is very simple and cheap to implement. However, the algorithm has had very little exposure in the mainstream literature. This paper gives some insight into when a PFC approach is expected to be successful and, conversely, when one should deploy with caution.

This paper gives a concise review of work on ensuring feasibility and stability within linear MPC during target changes. A summary of similarities and weaknesses of the various proposals in the literature is presented and this forms a useful baseline for establishing where improvements can be made.

This work aims to improve corporate functional departments’ confidence in adopting modern control approaches in new scenarios and thus presents control structure solutions based on MPC for two control problems facing existing upstream oil & gas production plants; these are the disturbance growth in the series connected process and the control system dependency on operators. The proposed approach integrates distributed MPC (DMPC) as a master controller for the existing classical control of each subsystem, with a focus on those with high interaction phenomena. The proposed DMPC also considers safeguarding, constraints and the enhancement of plant-wide optimal performance. The suggested control solution reduces the role of control room operators which is shown to reduce the growth in the impact of process disturbances. Compared with some alternative control structures (centralised MPC, decentralised MPC, distributed MPC (DMPC), and hierarchical DMPC) this proposal is simple, inexpensive to implement, and critically, builds on the local team operational experience and maintenance skills.

In this paper, the efficacy of structured and unstructured parameterisations of the degree of freedom within a predictive control algorithm is investigated. While several earlier papers investigated the enlargement of the region of attraction using structured prediction dynamics, little consideration has been given to the potential of unstructured parameterisations to handle the trade-off between the region of attraction, performance and computational burden. This paper demonstrates how unstructured dynamics can be both selected and used effectively and furthermore gives a comparison with structured methods.

This paper considers the tractability of parametric solvers for predictive control based optimisations, when future target information is incorporated. it is shown that the inclusion of future target information can significantly increase the implied parametric dimension to an extent that is undesirable and likely to lead to intractable problems. The paper then proposes some alternative methods for incorporating the desired target information, while minimising he implied growth in the parametric dimensions, at some possibly small cost to optimality.

Effective operation of small spacecraft implies processors with low cost, energy efficiency and low computational burdens while retaining accurate output tracking. This paper presents the extension of work in [1] on eliminating the chattering for Sliding Mode Control (SMC) using a decaying boundary layer design which is able to achieve these small spacecraft operation needs. The extension is applied on a spacecraft's attitude control, while orbiting the earth with angular velocity, ω 0 . In SMC, chattering is a main drawback as it can cause wear and tear to moving mechanical parts. Earlier work on a decaying boundary layer design was capable of reducing the chattering phenomena for a limited time only and hence this paper proposes a novel decaying boundary layer and switching function to improve the earlier version. The proposed technique is shown to reduce chattering permanently and also retain control output accuracy.

This paper looks into the modelling of the ACUREX distributed solar collector field at the Plataforma Solar de Almeria (PSA). ACUREX possesses resonance characteristics that lie well within the desired control bandwidth and quite commonly is modelled by dividing the receiver tube in the solar collector field into a number of segments. However, the number of segments has varied significantly in the literature. This paper provides an open-loop and closed-loop analysis with the aim of finding the number of segments needed to adequately model the resonance characteristics.

The use of upstream wind measurements has motivated the development of blade-pitch preview controllers for improving rotor speed tracking and structural load reduction beyond that achievable via conventional feedback control. Such preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, alter the closed-loop dynamics of the existing blade-pitch feedback control system. This can result in a deterioration of the robustness properties and performance of the existing feedback control system. Furthermore, performance gains from utilising the upcoming real-time measurements cannot be easily distinguished from the feedback control, making it difficult to formulate a clear business case for the use of preview control. Therefore, the aim of this work is to formulate a modular MPC layer on top of a given output-feedback blade-pitch controller, with a view to retaining the closed-loop robustness and frequency-domain performance of the latter. The separate nature of the proposed controller structure enables clear and transparent quantification of the benefits gained by using preview control, beyond that of the underlying feedback controller. This is illustrated by results obtained from high-fidelity closed-loop turbine simulations, showing the proposed control scheme incorporating knowledge of the oncoming wind and constraints achieved significant 43% and 30% reductions in the rotor speed and flap-wise blade moment standard deviations, respectively. Additionally, the chance of constraint violations on the rotor speed decreased remarkably from 2.15% to 0.01%, compared to the nominal controller. Copyright © 2017 John Wiley & Sons, Ltd.

The ways we can and do educate University students is changing rapidly and thus the paper gives a concise survey of opportunities and good practice within control and systems engineering. Rather than focussing on generic issues, more focus is given to specific approaches which are highly relevant to control topics. Hence, much of the paper discusses laboratory provision, locally, remotely and virtually. Nevertheless, there is also some discussion of general good practice and resources which have been used and evaluated in the control community.

This paper focuses on the production and use of learning resources within an engineering curriculum that support the development of independent learners. The most important contribution is to propose pragmatic learning approaches which improve the student learning experience and reward their engagement. The paper gives evidence that the introduction of new learning resources and approaches need not be labour intensive, expensive or complicated and thus be both cost effective and straightforward to implement, creating a win/win scenario for the staff and students. Several examples of novel learning resources and approaches developed by the author for use within an engineering curricula are presented and evaluated.

This paper makes contributions in the area of predictive control and in particular examines to what extent different methods for parameterising the degrees of freedom within the input trajectories can improve aspects of feasibility and performance. A few earlier papers have suggested the potential for Laguerre polynomials to be effective within a predictive control design, but without giving explicit design guidelines. This paper extends that work by looking not only at systematic choices for the Laguerre parameters but also other choices of orthogonal functions. Systematic mechanisms are discussed to choose the best tuned alternative parameterisation dynamics. The efficacy of the proposed parameterisations within existing predictive control algorithms are demonstrated by examples.

Proficiency in MATLAB is a common expectation for control and systems engineering, but the assessment of this can be time consuming and expensive. This paper reviews some of the ideas in the literature for improving the efficiency and efficacy of the assessment practices and proposes what might be considered a relatively radical approach which is far more efficient, for the academic, in general.

This paper extends efficient, robust model predictive control (MPC) approaches for linear parameter varying (LPV) systems to tracking scenarios. A dual mode approach is used and future information about target changes is included in the optimisation tracking problem. The controller guarantees recursive feasibility by adding an artificial target as an extra degree of freedom. Convergence to admissible targets is ensured by constructing a robustly invariant set to track any admissible target. The efficacy of the proposed algorithm is demonstrated by MATLAB simulations.

This paper proposes a distributed load frequency control (LFC) scheme for a deregulated power system, that can give acceptable dynamic performance and good constraint handling capabilities. Firstly, a 4-area deregulated benchmark system, where each control area has a different rated capacity, is developed. The benchmark system includes measured (contracted) and unmeasured (uncontracted) disturbances. A non-cooperative distributed model predictive control (DMPC) algorithm is developed and tested on the benchmark system. The DMPC scheme is developed to operate using output feedback, where distributed observers using local measurements supply local controllers with state and unmeasured disturbance estimations. The DMPC here, unlike other noncooperative schemes, is simple and devoid of extensive offline parameter tuning. Some comparisons and discussions are provided between the DMPC and alternative model predictive control schemes.

This paper presents a survey of different control methods deployed for spacecraft attitude control with a focus on strengths, weaknesses and opportunities for improvement. Com­mon methods discussed include Sliding Mode Control (SMC), Unscented Kalman Filter (UKF) and Adaptive Control. Common challenges are the disturbances and uncertainties in space and produced by the spacecraft. This overview is used as a foundation for proposing useful research directions for developing improved control methods for the spacecraft attitude and orientation, with core requirements being low cost, robustness, precision, high efficiency and low computational load. The paper also summarises key actuation types used for satellite orientation, magnetics actuator and reaction wheels actuator, as the control strategy deployed, robustness and performance have strong links to the actuation.

The main objectives of this paper are to assess the impact of disturbances in a natural gas processing train in the upstream oil & gas fields and to validate a representative model which can be used for developing/testing a swift and anticipatory control system. The impact of two different causes of process disturbances on a gas phase train comprising three main processes connected in series is presented. The paper provides answers about how feed disturbances, and process unit malfunctions affect series connected processes, and more specifically Gas Sweetening, Gas Dehydration, and Hydrocarbon Dew-Pointing units.

This paper tackles some important issues in the use of target information by model predictive control (MPC) that have largely been ignored. First the paper shows explicitly how far future information of target changes affects an optimal predictive control (OMPC) for both the unconstrained and constrained case. Secondly, the paper develops on proposal in the literature which deals with scenarios where the desired target is unreachable due to constraints. Numerical illustrations in MATLAB give evidence of the efficacy of the proposals.

The use of upstream wind measurements has motivated the development of blade-pitch preview controllers to improve rotor speed tracking and structural load reduction beyond that achievable via conventional feedback design. Such preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, alter the closed-loop dynamics of the existing blade-pitch feedback control system. This can result in the robustness properties of the original closed-loop system being no longer preserved. As a consequence, the aim of this work is to formulate a MPC layer on top of a given output-feedback controller, with a view to retaining the closed-loop robustness and frequency-domain performance of the latter. The separate nature of the proposed controller structure enables clear and transparent qualifications of the benefits gained by using preview and predictive control. This is illustrated by results obtained from closed-loop simulations upon a high-fidelity turbine, showing the performance comparison between a nominal feedback compensator and the proposed MPC-based preview controller.

This study presents a novel programmable logic controller (PLC) implementation of a constrained predictive control (MPC) algorithm based on Laguerre functions and multi-parametric quadratic programming (mp-QP). First, the study shows how Laguerre functions can be used in conjunction with mp-QP to achieve a large decrease in both the online computations and data storage requirements while increasing the feasible region of the optimisation problem for MPC; extensive simulation results are given to back this claim. Next, the study shows how the algorithm can be embedded on an industrial PLC using the IEC 61131-3 programming standard. A laboratory test was carried out to demonstrate the code on real hardware.

This paper introduces some novel ideas for presenting PI tuning to chemical engineers who take just a single control and modelling module. It is shown how the tuning approach is simpler or more intuitive than the more popular Zeigler-Nichols approach and thus easy to teach. Moreover, it is effective for plants with relatively tame dynamics. Some analysis is used to demonstrate that the method is expected to work well in many cases.

The prime aim of this paper is to embed a predictive control (MPC) algorithm with constraint handling capabilities into a programmable logic controller (PLC). In order to achieve it, this paper develops parametric approaches to MPC but differs from more conventional approaches in that it predefines the complexity of the solution rather than the allowable suboptimality. The paper proposes a novel parameterisation of the parametric regions which allows efficiency of definition, effective spanning of the feasible region and also highly efficient search algorithms. Despite the suboptimality, the algorithm retains guaranteed stability, in the nominal case. A laboratory test was carried out to demonstrate the code on real hardware and the effectiveness of the solution.

This paper will make two distinct contributions. First it will give some reflections on the practice of using student projects, both undergraduate and MSc, for developing teaching resources. Of particular interest is the both the utility of the resources and also the learning experience for the project student. The second contribution focuses specifically on a recent student project developing web accessible laboratories, both virtual and remote. There is some discussion of the pedagogy behind the laboratories and also detailed software specifications and reflections on how the laboratories in Sheffield are constructed. Finally, there is some discussion of how to log student participation which we believe is little discussed in the literature.

Interpolation methods are one means of tackling the classical performance versus feasibility compromise in model predictive control (MPC). However, although some details are available in various conferences, very little has appeared in the published journals and also there is no paper pulling all the various algorithms together. Hence this article seeks to give a brief but insightful survey of existing proposals along with their strengths and weaknesses, before proposing useful avenues for future studies.

Hybrid control systems are an exciting field of research. These contain two distinct types of system; one with continuous dynamics and the other with discrete dynamics, that interact with each other. Model-based fault detection methods were proposed to detect and isolate faults as may occur in such systems; however, these methods are computationally demanding. This paper enhances a diagnoser interpreted Petri net (IPN) model to locate and isolate faults in such systems. The enhanced IPN model has two notable features, one it is simpler and the other it is faster. The latter feature stemmed from the fact that the proposed model consists of a single place and the same number of transitions that the system model has. Unlike, the conventional IPN-based diagnoser, the enhanced IPN-based diagnoser deals with complex hybrid control systems with ease. Industrial processes are employed to test the enhanced IPN diagnoser.

Looper—tension control among various control problems in hot rolling mills (HRMs) has significance, in particular, because it affects both operational stability of the process and dimensional quality of the products. The control target in this control loop is to maintain looper angle and strip tension simultaneously at their desired values. The most difficult challenge in the controller design arises from the interaction, between strip tension and looper angle, and uncertainty coming from disturbances and a model mismatch. Recently, there has been some research to investigate the potential benefits of model predictive control (MPC) for this control loop. However, most of them used constrained optimization based on nominal models, and this often caused constraint violations for the uncertain case. The purpose of this paper is to develop robust MPC algorithms for the hot rolling mill process, in the presence of disturbance uncertainty, in order to improve the constraint handling performance.

This paper explores the issues of feasibility and performance within predictive control. Conventional thinking is that there is typically a trade off between performance and the volume of the feasible region. However, this paper seeks to show that the trade off is often not as stark as might be expected and in fact one can sometimes gain huge amounts in feasibility with an almost negligible loss in performance while using a simple and conventional MPC algorithm.

This paper considers the efficacy of disturbance models for ensuring offset free tracking and optimum steady-state target selection within linear model predictive control (MPC). Previously published methods for steady-state target determination can address model error, disturbances, and output target changes when the desired steady state is unconstrained, but may fail when there are active constraints. This paper focuses on scenarios where the most desirable target is unreachable, thus some constraints are active in steady state. Examples are given showing that the resulting �feasible steady-state target� can converge to a point which is not as close as possible to the true target. These failures have not been widely discussed in the literature. From the closed-loop behavior, hypotheses are put forward as necessary conditions for offset-free control. These hypotheses are then investigated through the use of Karush-Kuhn- Tucker (KKT) conditions of optimality.

This paper describes some of the preliminary outcomes of a UK project looking at control education. The focus is on two aspects: (i) the most important control concepts and theories for students doing just one or two courses and (ii) the effective use of software to improve student learning and engagement. There is also some discussion of the correct balance between teaching theory and practise. The paper gives examples from numerous UK universities and some industrial comment.