Search

This article focuses on extending, disseminating and interpreting the findings of an IEEE Control Systems Society working group looking at the role of control theory and engineering in solving some of the many current and future societal challenges. The findings are interpreted in a manner designed to give focus and direction to both future education and research work in the general control theory and engineering arena, interpreted in the broadest sense. The paper is intended to promote discussion in the community and also provide a useful starting point for colleagues wishing to re-imagine the design and delivery of control-related topics in our education systems, especially at the tertiary level and beyond.

One of several challenges that wave energy technologies face is their inability to generate electricity cost-competitively with other grid-scale energy generation sources. Several studies have identified two approaches to lower the levelised cost of electricity: reduce the cost over the device’s lifetime or increase its overall electrical energy production. Several advanced control strategies have been developed to address the latter. However, only a few take into account the overall efficiency of the power take-off (PTO) system, and none of them solve the optimisation problem that arises at each sampling time on real-time. In this paper, a detailed Nonlinear model predictive control (NMPC) approach based on the real-time iteration (RTI) scheme is presented, and the controller performance is evaluated using a time-domain hydrodynamics model (WEC-Sim). The proposed control law incorporates the PTO system’s efficiency in a control law to maximise the energy extracted. The study also revealed that RTI-NMPC clearly outperforms a simple resistive controller.

Predictive functional control (PFC) is the simplest model-based algorithm, equipped with the attributes of a fully fledged predictive controller but at the cost and complexity threshold of a standard PID regulator. It has proven benefits in controlling stable SISO dynamic systems, but similarly to its competitor PID, it loses efficacy when a challenging application is introduced. In this paper, we present a modified PFC approach, especially tailored for open-loop unstable processes, using pre-stabilisation to efficiently control the undesirable dynamics at hand. This is essentially a two-stage design scheme with implications for PFC tuning and constraint handling. The proposal, nevertheless, is straightforward and intuitive, and provides improved closed-loop control in the presence of external perturbations against the standard PFC, and significantly better performance overall compared to the common PID algorithm, as demonstrated in a numerical case-study.

The classic PFC design is simple and intuitive and yet this paper shows that, counter to expectations, the long standing use of the target information is flawed. Some simple illustrations will demo...

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 paper focuses on good practice in terms of pedagogies rather than technical content, but with reference to a control engineering curriculum. A new lecturer will need to ask questions not only about the content of a course but also, how should that content be delivered? The paper presents arguments for a holistic or blended approach to student development and illustrates how that can be wrapped around technical learning outcomes. A core part of this approach is effective use of modern technologies (Rossiter, 2011). This paper provides a summary of good practice and an illustrative benchmark module.

Many undergraduate engineering students have just a single introductory feedback control course in their study list. Often the curricula found in such courses is totally based on continuous time-domain classic control techniques. However, currently most control design techniques are implemented in digital machines. Thus, digital control concepts should be covered in introductory control courses. In this paper, the issue of how to implement and test digital industrial controllers is addressed. Teaching experiments based on the APMonitor temperature control lab (TCLab) are proposed. It will be shown that TCLab as an Arduino based portable kit, provides an excellent means to test digital controllers, as it is a compact and portable lab to be used by lecturers and students. While there are many low-cost and portable hardware options for teaching dynamics and control, a novel aspect of this paper is the digital control education methods that are validated with classroom experience, particularly with Biomedical and Bioengineering students. Preliminary results are presented.

This paper addresses the problem of controlling an Autonomous Surface Vehicle (ASV) in rough sea-states, with a view towards minimising wave-induced forces, whilst maintaining headway. This is a challenging control application since, and as is derived in the paper, the interaction between the vessel and the wave disturbance is nonlinear and coupled. This subsequently motivates the novel application of the Real Time Iteration Scheme (RTI) for Nonlinear Model Predictive Control (NMPC) of the ASV. Analysis of the resulting control signal provides an important insight into the role of the wave encounter frequency. Specifically, by actuating at twice the average wave encounter frequency, the nonlinear controller is able to reduce the wave forces, compared to an open-loop controller that achieves the same average velocity.

Virtual laboratories have become a common tool in recent years for supporting student learning and engagement. This paper presents a new tool for helping students self-assess their competence in basic dynamics for 1st and 2nd order systems alongside simple PI compensation techniques. The tools provide a supported environment for helping students work towards the correct answer by providing succinct feedback on incorrect responses and opportunities to try again, while displaying relevant information. A partner interactive tool is also provided which focuses solely on assessment with no feedback, so that students can assess their ability to get correct answers in a scenario that only the first attempt counts. This paper gives the thinking behind the tools, their coding and also accessibility for students.

This paper summarizes the results from a large-scale survey on the content and teaching philosophy of the first, and in many cases the only, control course taken by undergraduate students in engineering and applied sciences around the world. The IFAC Technical Committee on Education developed and administered the survey in 2019. At the time of writing of this manuscript 201 control professionals have responded to the survey. The majority view is that the first course in control should focus on concepts and avoid excessive mathematical proofs. The responders ranked the depth of coverage of 63 topics commonly found in introductory control texts. A model curriculum for a first control course is suggested based on the ranking results.

This work presents a novel intelligent robot perception system, including a real-time, high-quality, 3D scanning pipeline for texture-less scenes and a human-supervised grasping system. Comparison is carried out with the state of the art 3D reconstruction systems, and the performance of the proposed system is demonstrated. The scanning methods are applied to a new user interface with object 6D-pose estimation. This work supports human-robot interaction in remote handling operations in hazardous environments by providing a high-quality telepresence. Current teleoperation systems primarily utilise 2D images or point clouds to display the remote workspace to the operator. Operators require extensive training to be able to perceive the spatial relationship between the robot and the target objects by remotely looking at multiple 2D images. Therefore, this paper proposes a new teleoperation system that exploits artificial intelligence to improve the efficiency of operators. The experiments show that the proposed method surpasses state-of-the-art reconstruction systems and successfully complements a simulated nuclear waste handling experiment.

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 proposes a novel modification to the predictive functional control (PFC) algorithm to facilitate significant improvements in the tuning efficacy. The core concept is the use of an alternative parameterisation of the degrees of freedom in the PFC law. Building on recent insights into the potential of Laguerre functions in traditional MPC (Rossiter et al., 2010; Wang, 2009), the paper develops an appropriate framework for PFC and then demonstrates that these functions can be exploited to allow easier and more effective tuning in PFC as well as facilitating strong constraint handling properties. The proposed design approach and the associated tuning methodology are developed and their efficacy is demonstrated with a number of numerical examples.

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.

Predictive functional control (PFC) is a popular alternative to PID because it exploits model information better and enables systematic constraint handling while also being cheap and computationally efficient. A recent overview paper reviewed some recent proposals for improving the tuning efficacy. This paper extends and develops upon that review paper by introducing some exciting new proposals for how to making tuning more intuitive and, thus, easier for unskilled operators. Moreover, there are early indications that these proposals are easily modified for use in nonlinear cases while maintaining a very low cost and a simple and fast online computation.

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.

This paper focuses on the combined challenge of encouraging students to engage with learning and assessment of their competence levels. A core challenge for many staff is the need to distinguish different levels of learning, and to evidence core competence clearly, especially for students with lower marks. This paper proposes a novel assessment strategy which separates core competencies from the more challenging application of the learning in engineering problem solving. The assessment design is efficient for staff and students and allows a reduction in student stress levels while simultaneously giving them strong incentives to adopt good working practices. Evaluation evidence is given to demonstrate the efficacy of the approach.

Predictive functional control (PFC) has emerged as a popular industrial choice owing to its simplicity and cost-effectiveness. Nevertheless, its efficacy diminishes when dealing with challenging dynamics because of prediction mismatch in such scenarios. This paper presents a proposal for reducing prediction mismatch and thus improving behaviour for simple unstable processes; a two-stage design methodology pre-stabilises predictions via proportional compensation before introducing the PFC component. It is demonstrated that pre-stabilisation reduces the dependency of the closed-loop pole on the coincidence point and also improves robustness to uncertainty. Simulation results verify the improved performance as compared to conventional PFC.

Our research aims at facilitating the design of 'Remotely Instructed Robots' for future glove-boxes in the nuclear industry. The two main features of such systems are: (1) They can automatically model the working environment and relay that information to the operator in virtual reality (VR). (2) They can receive instructions from the operator that are executed by the robot. However, the deficiency of these kind of systems is that they heavily rely on knowledge of expert programmers when the robot's capabilities or hardware are to be reconfigured, altered or upgraded. This late breaking report proposes to introduce a third important advancement on remotely instructed robots: (3) Intuitive programming modifications by operators who are non-programmers but have basic knowledge of hardware, and most importantly, have experience of the weaknesses in particular handling tasks.

Individual pitch control (IPC) provides an important means of attenuating harmful fatigue and extreme loads upon the load bearing structures of a wind turbine. Conventional IPC architectures determine the additional pitch demand signals required for load mitigation in response to measurements of the flap-wise blade-root bending moments. However, the performance of such architectures is fundamentally limited by bandwidth constraints imposed by the blade dynamics. Seeking to overcome this problem, we present a simple solution based upon a local blade inflow measurement on each blade. Importantly, this extra measurement enables the implementation of an additional cascaded feedback controller that overcomes the existing IPC performance limitation and hence yields significantly improved load reductions. Numerical demonstration upon a high-fidelity and nonlinear wind turbine model reveals (i) 60% reduction in the amplitude of the dominant 1P fatigue loads, and (ii) 59% reduction in the amplitude of extreme wind shear induced blade loads, compared to a conventional IPC controller with the same robust stability margin. This paper therefore represents a significant alternative to wind turbine IPC load mitigation as compared to LiDAR-based feedforward control approaches.

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.

Predictive functional control (PFC) is a fast and effective controller that is widely used in preference to PID for single-input single-output processes. Nevertheless, the core advantages of simplicity and low cost come alongside weaknesses in tuning efficacy. This paper summarises and consolidates the work of the past decade, which has focused on proposing more effective tuning approaches while retaining the core attributes of simplicity and low cost. The paper finishes with conclusions on the more effective approaches and links to context.

This work addresses secondary level control of Autonomous Microgrid composed of Distributed Renewable Energy Sources (DRES). The interconnection of DRES forms a nonlinear system with coupled dynamics, unknown network parameters, and network topologies. The challenging features of DRES, such as low inertia, large number, volatile production capacities, and geographically wide distribution pose critical control design challenges for Economic Dispatch (ED) and frequency regulation. Whereas the distributed control schemes proposed in contemporary research are too slow to provide ED in the presence of fluctuating power demand, based on impractical assumptions or too restrictive in terms of network topology to have practical significance. Moreover, the control schemes are unable to handle the intermittent and uncertain nature of DRES and lead to instability in the presence of volatile power production capacities. Considering the above, we propose Secondary Distributed Model Predictive Control that provides fast and robust convergence and avoids impractical assumptions. A unique decoupling technique enables the application of control to generic network topologies. A constrained ED solution is derived and successfully achieved in presence of volatile production capacities of DRES. The Lyapunov stability of control is proved through terminal constraints. The performance of the proposed control is validated using an IEEE 14-Bus System.

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.

This work presents a novel constraint handling strategy for Predictive Functional Control (PFC). First, to improve prediction consistency, the constant input assumption of nominal PFC approaches is replaced with Laguerre based prediction. This substitution improves the effectiveness of using a constrained solution to prevent long-term constraint violations. Secondly, for state constraints, a simpler single regulator approach is proposed instead of switching between regulators, an approach common in the PFC literature. Simulation results verify that the proposed method manages the constraints better than the traditional approach. Moreover, despite all the modifications, the controller formulation and framework remain simple and straightforward which thus are in line with the key ethos of PFC.

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.

Many engineering students have little inherent interest in control topics and struggle to relate these to their career goals. This paper looks at a novel mechanism for encouraging students to take more ownership of their learning of control topics and, by doing so, improve engagement and learning. Consequently it also fits well into research led teaching philosophies. The main focus here is on the student products for an assignment the author designed in the curriculum which challenged students to develop a learning resource. The paper includes description of one of these student generated resources, which could be shared with or used by the community, and the personal experiences and reflections of the students who produced this resource.

Discussions on how to make effective use of advance information on target changes are discussed relatively rarely in the predictive control literature. While earlier work has indicated that the def...

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.

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.

This work proposes an improved method for Predictive Functional Control (PFC) to handle an integrating process. Instead of assuming a constant future input, the dynamic is shaped with a first-order Laguerre polynomial so that it converges to the expected steady state value. This modification provides simpler coding and tuning compared to the conventional method in the literature. Simulation results show that the proposed controller improves the consistency of the open-loop prediction of an integrating process and thus improves closed-loop performance and constraint handling properties. The practicality of this algorithm is also validated on laboratory hardware.

The application of predictive control methods in real-time to fast systems, such as quad-rotors, remains a challenge for its implementation in low-power embedded systems. This paper presents the application of an Adaptive Laguerre-based Model Predictive Controller (MPC) to the Attitude Stabilization of a Quadrotor. The formulation uses an Online System Identification algorithm based on Recursive Least Squares (RLS) with forgetting factor for parameter estimation, and a Laguerre-based Model Predictive Controller for achieving real-time calcu-lation/update of the control law. The developed control system was experimentally tested in a real quad-rotor, and the results demonstrate its real-time applicability in a low-power embedded platform.

This paper focuses on the potential and practice of alternative forms of assessment to the conventional written exam. Readers will be interested in issues such as why would a staff member want to replace the written exam and how can I be sure that the replacement is equally effective at capturing student attainment. The paper summarises a trial at the University of Sheffield where the written exam was replaced by a computer quiz which was marked automatically.

A Laguerre Predictive Functional Control (LPFC) is a simple input shaping method, which can improve the prediction consistency and closed-loop performance of the conventional approach (PFC). However, it is well-known that an input shaping method, in general, will affect the loop sensitivity of a system. Hence, this paper presents a formal sensitivity analysis of LPFC by considering the effect of noise, unmeasured disturbance and parameter uncertainty. Sensitivity plots from bode diagrams and closed-loop simulation are used to illustrate the controller robustness and indicate that although LPFC often provides a better closed-loop tracking response and disturbance rejection, this may involve some trade-off with the sensitivity to noise and parameter uncertainty. Finally, to validate the practicality of the results, the sensitivity of the LPFC control law is illustrated on real-time laboratory hardware.

An Independent Model (IM) structure has become a standard form used in Predictive Functional Control (PFC) for handling uncertainty. Nevertheless, despite its popularity and efficacy, there is a lack of systematic analysis or academic rigour in the literature to justify this preference. This paper seeks to fill this gap by analysing the effectiveness of different prediction models, specifically the IM structure and T-filter, for handling noise and disturbances. The observations are validated via both closed-loop simulation and real-time implementation and show that the sensitivity relationships are system dependent, which in turn emphasises the importance of performing this analysis to ensure a robust PFC implementation.

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 ...