Keywords: Energy systems, Process control
Abstract: Networks of open channels are widely used in agricultural regions for distributing water from reservoirs to irrigators under the power of gravity. In this talk, we will explore the role of modelling, feedback, and optimization in the development of an automatic control system that enables demand-driven operation of large-scale irrigation networks. At the lowest level of the control hierarchy, distributed feedback compensators regulate the capacity for gravity-powered supply at points along the channels, by adjusting upstream flow. Corresponding water-level reference planning on the basis of uncertain flow-demand preview, and order scheduling, arise at the higher levels. The automatic control system is the outcome of a long-standing and on-going collaboration between The University of Melbourne and industry partner Rubicon Water. Irrigation-district scale installations of the technology operate throughout South-Eastern Australia, and other locations around the world. Substantial operational gains and water savings have been achieved in terms of the quality of service delivered to irrigators, and reducing, if not eliminating, as often the case, unproductive spillage at the ends of the channels.

Keywords: Novel control theory and techniques
Abstract: The goal of my research is to develop simple yet rigorous methods to solve problems of engineering significance. These transformations unify and provide a systematic framework for industrial approaches using nonlinear blocks and cascade control and academic methods on nonlinear control, in particular, feedback linearization. The method is simple and it works in practice.

Keywords: Process control
Abstract: A nonlinear model for the surge tank of Sibanye-Stillwater's Platinum tailings treatment plant is derived and linearised. Three controllers (two classical feedback and one model predictive controller (MPC)) are presented for control of the plant, and it is shown that a decoupled proportional-integral (PI) control structure, as would be employed in practice, performs the worst, while a nonlinear MPC controller provides the best performance. To illustrate an access economy model concept for industrial process control, a cloud platform to facilitate the competition between various controllers is presented and a scenario given with the three controllers competing to control the surge tank process. The platform is shown to provide the plant access to a controller that performs better than what is available locally.

Keywords: Process control, Novel control theory and techniques
Abstract: One of the methods that has gained popularity in controller design for nonlinear systems is to nd some global transformations which converts the nonlinear model in the original variable into an exact linear model in a dierent set of variables. Linear controller design methods can then be easily applied on the equivalent linear system with a back transformation to the original variables for implementation purposes. Consequent upon this, two types i.e. Type I and Type II of global linearizing transformations for a single, isothermal continuous stirred tank reactor (CSTR) used for ethylene hydrogenation are derived using literature techniques for rst-order nonlinear systems. These are then used to carry out alternate nonlinear control designs for the set point and regulatory control of the exit concentration of an isothermal CSTR used for ethylene hydrogenation which exhibits multiplicity of steady-states. For set point change control, simulation results show that the nominal performance of the Type II variable transformation controller is better than that of the Type I variable transformation controller while the latter seemed to have better regulatory control performance. However, simulation results of the sensitivity/robustness of the nonlinear controllers to modeling errors and measurement noise investigated showed the Type I variable transformation controller to be better than the Type II for both set point change over a wide operating span, and regulatory control at an unstable steady-state point.

Keywords: Modeling and system identification, Machine learning, Big data
Abstract: In this study, the slump data for cement mixtures with an added dispersing agent have been modelled. The slump measurement for cement mixtures is an important indicator for cement consistency, which affects workability. The slump data consists of five measurements taken at 15-minute intervals, starting with an initial value. Twenty such tests were analysed. When normalising all the data by the initial value, it is observed that the trajectories of the data are reasonably linear. This implies that all the data can be fitted with two parameters, the initial value, and a rate of decay. The conclusion is that if a model can be found for the initial slump value as a function of variables in the process used to produce the dispersant, then a general model to predict the slump behaviour is found. Using data from the process that produces the dispersing agent, a support vector machine approach was used to model the initial values. Reasonable fits are obtained with five inputs.

Keywords: Process control
Abstract: This paper describes the application of decentralised model predictive control (DMPC) to parallel unit operations. A decentralised approach may provide advantages in terms of maintenance, online time, and tuning complexity. Total flow control and linearisation, flow biasing and mass balance baselayer schemes are discussed as well as the DMPC structures. Finally, an industrial case study is presented where a DMPC approach is applied to steam header pressure control and steam generation optimisation.

Keywords: Artificial intelligence, Process control, Modeling and system identification
Abstract: Process industries are confronted with multifaceted problems, including a high degree of nonlinearity and integrated processes, high energy costs, and stringent environmental regulations. The traditional methods for solving these problems are suboptimal. The quest for an optimal solution for industrial processes with reduced product variability and increased profit margin has since birthed the need to develop efficient design methods. Hence, this study investigated two artificial neural networks (ANN) applications for modelling and predictive control of an experimental cascaded three-tank system – a 3-by-3 multivariable and nonlinear process. To achieve this, the tank process was excited by well-designed input signals to obtain input-output data at a sampling time of 5s. The datasets obtained were used to fit recurrent neural network (RNN) and feedforward neural network (FFNN) models for the system. Thereafter, the identified models were used in the design of predictive controllers. Validation results showed that FFNN gave a better fit than RNN. The closed-loop experimental results also showed the FFNN-based predictive controller displaying an overall superior performance for both servo and regulatory control problem.

Keywords: Modeling and system identification
Abstract: Congestion is a phenomenon that impacts most cities in the world. Due to car emissions, it is a significant source of pollution. Even though mobility restrictions can reduce congestion and emissions, essential activities still need cars. With lockdown measures during the global pandemic of Covid-19, measuring essential traffic data has been made possible. This paper concerns analysis and modelling of such essential traffic. It appears that congestion dynamics of essential traffic exhibits dynamics than can be represented with a linear model. This paper introduces such a model and provide a method to jointly estimate the parameters and the model input. The model is validated with data collected in Johannesburg, South Africa.

Keywords: Robotic systems, Artificial intelligence, Automotive systems
Abstract: Path planners based on basic rapidly-exploring random trees (RRTs) are quick and efficient, and thus favourable for real-time robot path planning, but are almost-surely suboptimal. In contrast, the optimal RRT (RRT*) converges to the optimal solution, but may be expensive in practice. Recent work has focused on accelerating the RRT*'s convergence rate. The most successful strategies are informed sampling, path optimisation, and a combination thereof. However, informed sampling and its combination with path optimisation have not been applied to the basic RRT. Moreover, while a number of path optimisers can be used to accelerate the convergence rate, a comparison of their effectiveness is lacking. This paper investigates the use of informed sampling and path optimisation to accelerate planners based on both the basic RRT and the RRT*, resulting in a family of algorithms known as optimised informed RRTs. We apply different path optimisers and compare their effectiveness. The goal is to ascertain if applying informed sampling and path optimisation can help the quick, though almost-surely suboptimal, path planners based on the basic RRT attain comparable or better performance than RRT*-based planners. Analyses show that RRT-based optimised informed RRTs do attain better performance than their RRT*-based counterparts, both when planning time is limited and when there is more planning time.

Keywords: Novel control theory and techniques, Automotive systems, Modeling and system identification
Abstract: Vehicle routing problem (VRP) is one of the most important contexts that has attracted engineers’ attention nowadays. Using static VRP gives us improper solutions in the real world because of numerous uncertainties and challenges such as traffic congestion, car crashes, and adding and/or countermand orders. To overcome the challenges, we propose a dynamic vehicle routing problem (DVRP) solution considering traffic congestion, which uses a distributed cooperative predictive approach. The normal distribution is used for modeling traffic congestion. Also, we consider multi-route between each node. After specific sample time, the situation will be checked; then, as the occasion arises, we use capacitated clustering (CC) and binary integer programming to solve DVRP in view of the predictive method. Experiments present that the proposed algorithm reduces the final cost up to 8% compared to the static VRP.

Keywords: Control engineering education, Robotic systems, Modeling and system identification
Abstract: This paper proposes a sliding mode approach for leader-following formation control of perturbed second-order autonomous unmanned systems (AUSs) under directed topology, which is the case with constant velocity for the leader. Based on the practical situations, the kinematic equations are established for the AUSs. By assuming the leader's velocity constant, sufficient conditions are presented to guarantee the formation of AUSs. The formation controller consists of two parts. The first part aims to establish the formation of all the followers. The second part is designed to form the consensus of velocity. Finally, a numerical example with comparison to existing approaches, demonstrate the efficacy of the proposed method.

Keywords: Robotic systems, Novel control theory and techniques
Abstract: The paper deals with path following under digital control for a car-like robot. Assuming the existence of a continuous-time feedback based on transverse feedback linearization, a multi-rate sampled-date control strategy is proposed and its effectiveness validated through simulations.

Keywords: Aerospace systems
Abstract: Unmanned aircraft have been in use since the Second World War (for example the DH Queen Bee) but their commercial application has largely stayed in the realm of the Military. This is partly due to the airspace regulations but also the cost and complexity of the systems needed to operate them beyond the line of visual and radio sight. It also goes against the global trend in automation and control across most industries.

Indeed the aviation industry is ripe for disruption: Why do we need approx. 10 tonne helicopters to fly expensive cameras around for security; why do we need pilots to fly cargo over treacherous mountain ranges; why do we need pilots to fly instruments to measure methane, carbon dioxide or volcano ash? At the same time Musk's Skylink global broadband has meant the cost of satellite communications has been falling dramatically.

Can we now create low cost unmanned aviation services using smart communications and autopilot control systems? On the face of it this should be a relatively `simple' problem to solve as we know all the physics of flight and off-the-shelf Inertial + GPS systems give excellent tracking performance to control against. But our experience has shown that this is far from the case once you add the harsh realities of the operating environment.

I will explore some of the challenges and opportunities that we have encountered in developing a low cost automatic take-off and landing system, and what it takes to build a truly scalable and global Beyond Visual Line of Sight Command and Control link. In doing so I will show what a key role control engineers can play in achieving this next epoch in aviation services.

Keywords: Modeling and system identification, Machine learning, Process control
Abstract: The bottoms product of a debutaniser column in a Fischer-Tropsch refining catpoly unit should be maximised to ensure optimal operation of the downstream units. An accurate estimate of the C4 hydrocarbons in the bottoms product is required to ensure that the specification is not violated. This work demonstrates a practical implementation of a soft sensor to estimate the %C4 material in the bottoms product of the debutaniser using the General Distillation Shortcut (GDS) method and a random forest (RF) machine learned model. The paper highlights practical challenges when deploying a soft sensor to an industrial plant. It is shown how the GDS method soft sensor had to be refitted after unit maintenance was carried out. In comparison the RF model soft sensor uses more reliable measurements and did not require refitting after unit commissioning. Both soft sensors performed well and the choice of soft sensor depends on the available measurements and measurement reliability.

Keywords: Process control
Abstract: Many plants with recycle suffer from the detrimental effects caused by the dynamics of the recycle stream; the quantification of such effects and their implications on the effective control of recycle plants are investigated in this work. Specifically, this work employs a robust control framework based on gap metric and stability margin to investigate the controllability of a given plant with recycle by a controller designed based on the forward-path transfer function, to guarantee some stability and performance requirements in the absence and presence of the recycle stream. When such controller is not obtainable, the recycle compensator is then justifiably proposed to eliminate the detrimental effects caused by the recycle stream; or alternatively, the global plant model is used during controller parameterization.

Keywords: Artificial intelligence, Machine learning, Modeling and system identification
Abstract: Leaks on the high pressure cooling system supporting the Convertor at the Anglo Converting Process (ACP) poses safety risks and requires plant downtime to repair. The leaks are difficult to detect, as the accuracy of available sensors are insufficient at the operating conditions of the system. Fault detection using digital twins have been employed successfully in the process industry. This paper discusses the construction of digital twins for the leak detection problem at ACP and focuses on the practical considerations of data preparation and modelling. This analytic has been implemented in the control room Human Machine Interface and a web enabled dashboard.

Keywords: Process control, Novel control theory and techniques, Modeling and system identification
Abstract: This work deals with the extension of the generalized internal model control method to plants with integrators. In order to facilitate the computation of the m/n simplified moment approximant of the plant model, the poles at the origin of the complex plane are suitably shifted to the left half plane. The simplified model is then used to parameterize a feedback controller which contains a filter parameter that can be tuned to obtain desired closed loop characteristics

Keywords: Aerospace systems, Artificial intelligence, Novel control theory and techniques
Abstract: This paper presents the modelling and control of a single-axis tilting quadrotor unmanned aerial vehicle (UAV). The single-axis tilting of the rotors transforms the quadrotor into an overactuated system, enabling control in all 6 degrees-of-freedom (DOF). The development of a detailed mathematical model for single-axis tilting quadrotor UAV is presented. A proportional-integral-derivative (PID) controller implementation and a control-allocated sliding mode control (CASMC) implementations are investigated. Stability analysis of the proposed sliding mode controller was carried out using Lyapunov stability method. The method was validated within the MATLAB/Simulink environment, in order to generate data for comparison purposes. It was determined that the single-axis tilting quadrotor CASMC technique provides better overall performance when compared to the PID controller.

Keywords: Modeling and system identification, Aerospace systems, Robotic systems
Abstract: Transporting a suspended payload with a multirotor has many applications. Knowledge of the payload dynamics is required to adjust the control of the system to damp payload oscillations. Often, the dynamics of the payload are unknown and cannot be represented with an a priori model before a flight. This paper proposes a Model Predictive Control(MPC) architecture that controls a multirotor carrying an unknown suspended payload using a plant model from data-driven system identification techniques. Dynamic Mode Decomposition with Control (DMDc) and Hankel Alternative View Of Koopman with Control (HAVOKc)are the regression techniques used to identify system models without relying on modelling assumptions and by using only time-series measurements. The standard Hankel Alternative ViewOf Koopman (HAVOK) is adapted slightly in this work for use with controlled systems. These two techniques are combined in the MPC architecture and compared against a conventional Proportional Integral Derivative (PID) system to control a multirotor with an unknown suspended payload within simulation. The results show that both MPC systems outperform the conventional system and achieve velocity control while simultaneously damping the payload swing angle. The proposed systems also show good adaptability with different payload parameters. Both system identification methods perform well with the presence of measurement noise.

Keywords: Aerospace systems, Robotic systems
Abstract: The docking of two satellites requires accurate estimates of relative position and attitude. This paper describes a LED marker based pose estimation method that can be incorporated into a docking subsystem. The paper further reports on the accuracy of the method. The results are compared to that obtained by Jansen (2020), where ArUco markers and a fixed observation camera are used. The LED marker pose estimation system is to demonstrated on a planar air-bearing table with gas propelled carts in a closed-loop control experiment, where the ArUco system will serve as the ground truth measurements. It was concluded that the LED marker system could be at least as accurate as the ArUco marker system however issues with lens distortion caused deterioration in the accuracy when the LED markers were not in the centre of the image.

Keywords: Aerospace systems, Novel control theory and techniques, Process control
Abstract: Aerospace is considered as one of the most critical areas of application requiring a certain level of precision and security. In fact, in the attitude control system of a satellite, reaction wheels are one of the most commonly used actuators presenting the highest percentage of failures that can appear in a spacecraft. Thus, the main challenge of the present research is to develop a fault diagnosis module for uncertain nonlinear process. An optimized interval fault detection and isolation method based on a Midpoint-Radii Kernel Principal Component Analysis (MR-KPCA) is then designed. Actually, the Kernel Principal Component Analysis is adopted to properly estimate the nonlinear process of the reaction wheels. A generalized squared prediction error denoted SPE-MR to interval data is adopted in the detection phase. To improve the performance of the diagnosis module, a merge between the SPE index and the exponentially weighted moving average EWMA filter is proposed. Based on data provided by a "high fidelity" industrial simulator developed by Thales Alenia Space, the obtained results proved the effectiveness of the proposed interval fault diagnosis method on detecting and isolating reaction wheels' faults.