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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.

This paper proposes a computationally efficient predictive control law for non-linear systems, that is one that can easily be coded and implemented on low cost hardware. Moreover, it has a secondary core benefit that the core tuning parameter reduces to a single choice which is: how much faster than open-loop would you like the closed-loop to converge? Conceptually the approach builds on the PFC approach but proposes a very different type of coincidence condition which removes the lag associated to the conventional approach. Simulations demonstrate that for some non-linear systems this is a cheap and simple way of ensuring effective feedback, with constraint handling.

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.

Several solutions have been proposed in the literature to maximise the harvested ocean energy, but only a few consider the overall efficiency of the power take-off system. The fundamental problem of incorporating the power take-off system efficiency is that it leads to a nonlinear and non-convex optimal control problem. The main disadvantage of the available solutions is that none solve the optimal control problem in real-time. This paper presents a nonlinear model predictive control (NMPC) approach based on the real-time iteration (RTI) scheme to incorporate the power take-off system's efficiency when solving the optimal control problem at each time step in a control law aimed at maximising the energy extracted. The second contribution of this paper is the derivation of a condensing algorithm O(N2) for ‘output-only’ cost functions required to improve computational efficiency. Finally, the RTI-NMPC approach is tested using a scaled model of the Wavestar design, demonstrating the benefit of this new control formulation.

Due to the ability to handle constraints systematically and predict system evolution with models, model predictive control (MPC) methods have been widely studied and implemented in many industries. At the same time, low-cost MPC has received widespread attention due to its simple principle and easy implementation. This paper proposes a new low-cost MPC method and uses this method to put forward new insights on the performance improvement of complex dynamic system control. Firstly, using the concept of preprocessing, a novel MPC prediction structure is proposed under the independent model mode. Then through rigorous proofs, the properties of the proposed MPC algorithm are analyzed. Finally, through the study of three industrial cases, the proposal’s deployment procedure and efficacy are illustrated in detail. Compared with other low-cost predictive control algorithms, the effectiveness of the method has been presented.

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.

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

Predictive Functional Control is a simple alternative to the traditional PID controller which has the capability to handle process constraints more systematically. Nevertheless, the most basic form of PFC has suffered from ill-posed prediction due to its simplicity in formulation and assumption of constant future input dynamics. Although some constraints can be satisfied, nevertheless the performance may be very conservative due to this issue. The main objective of this paper is to improve the constrained performance of a PFC controller with a minimum modification of the existing formulation. Specifically, a novel constraint handling approach for PFC is proposed based on an implied closed-loop prediction. Instead of assuming a constant input as deployed in the conventional open-loop prediction, the implied closed-loop input dynamics are utilised to detect future constraint violations. In addition, a future perturbation is introduced into the prediction structure as an extra degree of freedom for satisfying the constraints. Two simulation results confirm that the proposed approach gives far less conservative constraint handling and thus better control performance compared to the nominal PFC. Furthermore, this novel implementation also alleviates the well-known tuning difficulties and prediction inconsistency issues that are associated with conventional PFC when handling constraints. ******************************************************************************* Kawalan Kefungsian Ramalan adalah alternatif mudah kepada kawalan tradisional PID yang mempunyai kekangan keupayaan bagi mengawal proses secara lebih tersusun. Namun, keadaan paling asas pada kesan PFC adalah daripada ramalan tak teraju-rapi yang disebabkan oleh formula ringkas dan anggapan dinamik input yang sama bagi masa depan. Walau kekangan ini dapat diatasi, namun prestasi akan berubah secara konservatif disebabkan oleh isu ini. Objektif utama kajian ini adalah bagi membaiki kekangan prestasi kawalan PFC dengan modifikasi minimum formula yang ada. Secara spesifik, pendekatan nobel kawalan PFC dicadangkan berdasarkan ramalan lingkaran-tertutup. Selain anggapan input tetap seperti yang dilakukan pada ramalan lingkaran-terbuka yang konservatif, dinamik input yang dibuat pada lingkaran-tertutup telah digunakan bagi mengesan kekangan masa depan yang bertentangan. Tambahan, gangguan yang bakal berlaku pada masa depan telah diperkenalkan ke dalam struktur ramalan sebagai tambahan darjah pada kebebasan bagi mengatasi kekangan. Dua dapatan simulasi kajian menyetujui pendekatan yang dicadangkan dan menyebabkan sangat kurang kekangan pengendalian pada sistem konservatif, oleh itu kawalan yang lebih bagus pada prestasi berbanding pada PFC nominal. Selain itu, pendekatan nobel ini juga menghilangkan kesukaran pelarasan yang dikenali ramai dan ramalan isu tidak konsisten yang terdapat pada PFC konvensional apabila mengendali kekangan.

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.

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.

This paper presents a novel modification and insights in the topic of predictive functional control (PFC). Of particular interest is the consistency between the predictions deployed by PFC and the desired closed-loop behaviour. This paper focuses on integrator systems which pose some challenges to a conventional PFC algorithm and compares and contrasts two simple alternative but very effective modifications (one of which is novel) which enable better consistency of predictions and thus enable easier tuning and more reliable closed-loop behaviour. These insights are used to give some conclusions and proposals for how more challenging dynamics might be handled.

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.