In the last two decades, the development of specific methodologies for the control of systems described by nonlinear mathematical models has attracted an ever increasing interest. New breakthroughs have occurred which have aided the design of nonlinear control systems. However there are still limitations which must be understood, some of which were addressed at the IFAC Symposium in Capri. The emphasis was on the methodological developments, although a number of the papers were concerned with the presentation of applications of nonlinear design philosophies to actual control problems in chemi

The application of nonlinear control schemes to electro-hydraulic actuators often requires several alterations in the design of the controllers during their implementation. This is to overcome challenges that frequently arise in such control algorithms owing to model nonlinearities. Moreover, advanced control solutions for this type of systems often introduce input algebraic loops that pose significant design and tuning difficulties. Conventional methods to avoid such loops introduce chatter, which considerably degrade tracking performance and has oil degradation and wear as side effects. This study presents a nonlinear control architecture for hydraulic actuators that comprises low-complexity modules that facilitate robust high performance in tracking and avoids the drawbacks of chatter. The salient feature is a dynamic input-mapping inversion module that avoids algebraic loops in the control input and is followed by dedicated position control. The stability of the closed-loop system is analyzed using arguments from Lyapunov theory for cascaded non-autonomous nonlinear systems. The effectiveness of the proposed solution is evaluated on a high-fidelity simulator of a wind turbine pitch system, and validated on a full-scale laboratory setup that includes a hydraulic pitch system and blade bearing. Appropriate quantitative metrics are used to evaluate the closed-loop system performance in comparison to a state-of-the-art nonlinear design.

The subject of this thesis is the investigation of multibody system modelling and control analysis techniques for the development of advanced suspension systems in passenger cars. A review of the application of automatic control to all areas of automotive vehicles illustrated the important factors in such developments, including motivating influences, constraints and methodologies used. A further review of specific applications for advanced suspension systems highlighted a major discrepancy between the significant claims of theoretical performance benefits and the scarcity of successful practical implementations. This discrepancy was the result of idealistic analytical studies producing unrealistic solutions with little regard for practical constraints. The predominant application of prototype testing methods in implementation studies also resulted in reduced potential performance improvements. This work addressed this gap by the application of realistic modelling and control design techniques to practical realistic suspension systems. Multibody system modelling techniques were used to develop vehicle models incorporating realistic representations of the suspension system itself, with the ability to include models of the controllers, and facilitate control analysis tasks. These models were first used to address ride control for fully active suspension systems. Both state space techniques, including linear quadratic regulator and pole placement and frequency domain design methods were applied. For the multivariable frequency domain study, dyadic expansion techniques were used to decouple the system into single input single output systems representing each of the sprung mass modes. Both discretely and continuously variable damping systems were then addressed with a range of control strategies, including analytical solutions based on the active results and heuristic rule-based approaches. The controllers based on active solutions were reduced to satisfy realistic practical limitations of the achievable damping force. The heuristic techniques included standard rule-based controllers using Boolean logic for the discretely variable case, and fuzzy logic controllers for the continuously variable case

Electromechanical transients in power networks are mostly caused by a mismatch between power consumption and production, causing generators to deviate from the nominal frequency. To that end, feedback control algorithms have been designed to perform frequency and load/renewables-following control. In particular, the literature addressed a plethora of grid- and frequency-control challenges with a focus on linearized, differential equation models whereby algebraic constraints (i.e., power flows) are eliminated. This is in contrast with the more realistic nonlinear differential algebraic equation (NDAE) models. Yet, as grids are increasingly pushed to their limits via intermittent renewables and varying loads, their physical states risk escaping operating regions due to either a poor prediction or sudden changes in renewables or demands -- deeming a feedback controller based on a linearization point virtually unusable. In lieu of linearized differential equation models, the objective of this paper is to design a simple, purely decentralized, linearization-free, feedback control law for NDAE models of power networks. The aim of such a controller is to primarily stabilize frequency oscillations after a significant, unknown disturbance in renewables or loads. Although the controller design involves advanced NDAE system theory, the controller itself is as simple as a decentralized proportional or linear quadratic regulator in its implementation. Case studies demonstrate that the proposed controller is able to stabilize dynamic and algebraic states under significant disturbances.

This paper presents the simulation in ESL of a non-linear 6 degree-of-freedom missile model with an advanced, non-linear, multivariable autopilot designed using Rate Actuated Inverse Dynamics (RAID) methods. High performance control of non-linear systems requires the design of advanced, non-linear control systems, such as those used in autopilot design. Traditional linear control system design and analysis techniques are not sufficient for non-linear systems and current non-linear analysis methods are extremely limited. Therefore, the only method available to fully assess the performance of non-linear controller designs is simulation of the non-linear system. For this reason it is an essential part of the analysis and design process of these types of controllers. Non-linear dynamics can be continuous or discontinuous, the aerodynamics of a missile are non-linear but since they are continuous they do not represent a simulation challenge. However, there are multiple sets of discontinuous dynamics present in both the missile control surface model and the autopilot which can lead to multiple discontinuities being reached simultaneously, providing a challenging modeling exercise. The paper demonstrates how this kind of behavior can be successfully modeled and simulated within ESL using a simple switching logic

Model predictive control has gained popularity for its ability to satisfy constraints and guarantee robustness for certain classes of systems. However, for systems whose dynamics are characterized by a high state dimension, substantial nonlinearities, and stiffness, suitable methods for online nonlinear MPC are lacking. One example of such a system is a vehicle thermal management system (TMS) with integrated thermal energy storage (TES), also referred to as a hybrid TMS. Here, hybrid refers to the ability to achieve cooling through a conventional heat exchanger or via melting of a phase change material, or both. Given increased electrification in vehicle platforms, more stringent performance specifications are being placed on TMS, in turn requiring more advanced control methods. In this paper, we present the design and real-time implementation of a nonlinear model predictive controller with 77 states on an experimental hybrid TMS testbed. We show how, in spite of high-dimension and stiff dynamics, an explicit integration method can be obtained by linearizing the dynamics at each time step within the MPC horizon. This integration method further allows the first-order gradients to be calculated with minimal additional computational cost. Through simulated and experimental results, we demonstrate the utility of the proposed solution method and the benefits of TES for mitigating highly transient heat loads achieved by actively controlling its charging and discharging behavior.

Advanced Driver Assistance Systems (ADAS) have been one of the most active areas of ITS studies in the last two decades. ADAS aim to support drivers by either providing warning to reduce risk exposures, or automating some of the control tasks to relieve a driver from manual control of a vehicle. ADAS functions can be achieved through an autonomous approach with all instrumentation and intelligence on board the vehicle, or through a cooperative approach, where assistance is provided from roadways and/or from other vehicles. In this article, recent research and developments of longitudinal control assistance systems are reviewed including adaptive cruise control, forward collision warning and avoidance, and platooning assistants. The review focuses on comparing between autonomous systems and cooperative systems in terms of technologies used, system impacts and implementation. The main objective is to achieve common understanding on ADAS functional potentials and limitations and to identify research needs for further studie

Advanced control strategies for delivering heat to users in a district heating network have the potential to improve performance and reduce wasted energy. To enable the design of such controllers, this paper proposes an automated plant modeling framework that captures the relevant system dynamics, while being adaptable to any network configuration. Starting from the network topology and system parameters, the developed algorithm generates a state-space model of the system, relying on a graph-based technique to facilitate the combination of component models into a full network model. The accuracy of the approach is validated against experimental data collected from a laboratory-scale district heating network. The verification shows an average normalized root mean square error of 0.39 in the mass flow rates delivered to the buildings, and 0.15 in the network return temperature. Furthermore, the ability of the proposed modeling technique to rapidly generate models characterizing different network configurations is demonstrated through its application to topology optimization. The optimal design, obtained via a branch and bound algorithm, reduces network heat losses by 15% as compared to the conventional length-minimized topology.

As a result of the significant growth of wind turbines in size, blade load control has become the main challenge for large wind turbines. Many advanced techniques have been investigated aiming at developing control devices to ease blade loading. Individual pitch control system, adaptive blades, trailing edge microtabs, morphing aerofoils, ailerons, trailing edge flaps, and telescopic blades are among these techniques. Most of the above advanced technologies are currently implemented in, or are under investigation to be utilised, for blade load alleviation. The present study aims at investigating the potential benefits of these advanced techniques in enhancing the energy capture capabilities rather than blade load alleviation. To achieve this goal the research is carried out in three directions: (i) development of a simulation software tool suitable for wind turbines utilising nonconventional control systems, (ii) development of a blade design optimisation tool capable of optimising the topology of blades equipped with nonconventional control systems, and (iii) carrying out design optimisation case studies with the objective of power extraction enhancement towards investigating the feasibility of advanced technologies, initially developed for load alleviation of large blades, for power extraction enhancement. Three nonconventional control systems, namely, microtab, trailing edge flap and telescopic blades are investigated. A software tool, AWTSim, is especially developed for aerodynamic simulation of wind turbines utilising blades equipped with microtabs and trailing edge flap as well as telescopic blades. As part of the aerodynamic simulation of these wind turbines, the control system must be also simulated. The simulation of the control system is carried out via solving an optimisation problem which gives the best value for the controlling parameter at each wind turbine run condition. Developing a genetic algorithm optimisation tool which is especially designed for wind turbine blades and integrating it with AWTSim, a design optimisation tool for blades equipped with nonconventional control system is constructed. The design optimisation tool, AWTSimD, is employed to carry out design case studies. The results of design case studies reveal that for constant speed rotors, optimised telescopic blades are more effective than flaps and microtabs in power enhancement. However, in comparison with flap and microtabs, telescopic blades have two disadvantages: (i) complexity in telescopic mechanism and the added weight and (ii) increased blade loading. It is also shown that flaps are more efficient than microtabs, and that the location and the size of flaps are key parameters in design. It is also shown that optimisation of the blade pretwist has a significant influence on the energy extraction enhancement. That is, to gain the maximum benefit of installing flaps and microtabs on blades, the baseline blades must be redesigned

This paper shows that the design constraints of the Disturbance Observer (DOb) based robust motion control systems become stricter when they are implemented using computers or microcontrollers. The stricter design constraints put new upper bounds on the plant-model mismatch and the bandwidth of the DOb, thus limiting the achievable robustness against disturbances and the phase-lead effect in the inner-loop. Violating the design constraints may yield severe stability and performance issues in practice; therefore, they should be considered in tuning the control parameters of the robust motion controller. This paper also shows that continuous-time analysis methods fall-short in deriving the fundamental design constraints on the nominal plant model and the bandwidth of the digital DOb. Therefore, we may observe unexpected stability and performance issues when tuning the control parameters of the digital robust motion controllers in the continuous-time domain. To improve the robust stability and performance of the motion controllers, this paper explains the fundamental design constraints of the DOb by employing the generalised continuous and discrete Bode Integral Theorems in a unified framework. Simulation and experimental results are given to verify the proposed analysis method.

A robust controller is developed, using advanced nonlinear inverse dynamics (NID) controller design and genetic algorithm optimisation, for room temperature control. The performance is evaluated through application to a single zone dynamic building model. The proposed controller produces superior performance when compared to the NID controller optimised with a simple optimisation algorithm, and classical PID control commonly used in the buildings industry. An improved level of thermal comfort is achieved, due to fast and accurate tracking of the setpoints, and energy consumption is shown to be reduced, which in turn means carbon emissions are reduced

Cooling-limited exothermic semi-batch reactors require coordinated feed and cooling control to shorten batch time while maintaining the prescribed temperature. We develop a theoretical basis for advanced regulatory control (ARC) design by combining minimum-time and local safety analyses. Minimum-time analysis leads to an economic valve position control structure that adjusts feed using the temperature control system's cooling request, while cooling regulates temperature. Local safety analysis specifies the controller form and tuning conditions for reducing feed during cooling overload and restoring it as capacity becomes available. We also provide guidelines for industrial implementation and tuning. A reduced benchmark verifies the analytical tuning conditions, and an industrial-scale polymerization model evaluates the design. In simulations with parameter mismatch and unmodeled reaction dynamics, ARC achieves batch times comparable to those of parameter adaptive nonlinear model predictive control, using regulatory feedback without online nonlinear optimization.

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Complex adaptive systems are usually difficult to design and control. There are several particular methods for coping with complexity, but there is no general approach to build complex adaptive systems. The challenges of designing complex adaptive systems in a highly dynamic world drive the need for anticipatory capacity within engineering organizations, with a goal of enabling the design of systems that can cope with an unpredictable environment. This thesis explores this question of enhancing anticipatory capacity through the study of a complex adaptive system design methodology and complexity management competencies. A general introduction to challenges and issues in complex adaptive systems design is given, since a good understanding of the industrial context is considered necessary in order to avoid oversimplification of the problem, neglecting certain important factors and being unaware of important influences and relationships. In addition, a general introduction to complex thinking is given, since designing complex adaptive systems requires a non-classical thought, while practical notions of complexity theory and design are put forward. Building on these, the research proposes a Complex Systems Life-Cycle Understanding and Design (CXLUD) methodology to aid system architects and engineers in the design and control of complex adaptive systems. Starting from a creative anticipation construct - a loosening mechanism to allow for more options to be considered, the methodology proposes a conceptual framework and a series of stages to follow to find proper mechanisms that will promote elements to desired solutions by actively interacting among themselves. To illustrate the methodology, a financial systemic risks infrastructure systems architecture development case study is presented. The final part of this thesis develops a conceptual model to analyse managerial complexity competency model from a qualitative phenomenological study perspective. The model developed in this research is called Understanding-Perception-Action (UPA) managerial complexity competency model. The results of this competency model can be used to help ease project manager’s transition into complex adaptive projects, as well as serve as a foundation to launch qualitative and quantitative research into this area of project complexity management

A predictive triggering (PT) framework for the distributed control of resource constrained multi-agent systems is proposed. By predicting future communication demands and deriving a probabilistic priority measure, the PT framework is able to allocate limited communication resources in advance. The framework is evaluated through simulations of a cooperative adaptive cruise control system and experiments on multi-agent cart-pole systems. The results of these studies show its effectiveness over other event-triggered designs at reducing network utilization, while also improving the control error of the system.

Designing and tuning a proportional-integral-derivative (PID) controller appears to be conceptually intuitive, but can be hard in practice, if multiple (and often conflicting) objectives such as short transient and high stability are to be achieved. Usually, initial designs obtained by all means need to be adjusted repeatedly through computer simulations until the closed-loop system performs or compromises as desired. This stimulates the development of "intelligent" tools that can assist engineers to achieve the best overall PID control for the entire operating envelope. This development has further led to the incorporation of some advanced tuning algorithms into PID hardware modules. Corresponding to these developments, this paper presents a modern overview of functionalities and tuning methods in patents, software packages and commercial hardware modules. It is seen that many PID variants have been developed in order to improve transient performance, but standardising and modularising PID control are desired, although challenging. The inclusion of system identification and "intelligent" techniques in software based PID systems helps automate the entire design and tuning process to a useful degree. This should also assist future development of "plug-and-play" PID controllers that are widely applicable and can be set up easily and operate optimally for enhanced productivity, improved quality and reduced maintenance requirements

A new model-free setting and the corresponding "intelligent" P and PD controllers are employed for the longitudinal and lateral motions of a vehicle. This new approach has been developed and used in order to ensure simultaneously a best profile tracking for the longitudinal and lateral behaviors. The longitudinal speed and the derivative of the lateral deviation, on one hand, the driving/braking torque and the steering angle, on the other hand, are respectively the output and the input variables. Let us emphasize that a "good" mathematical modeling, which is quite difficult, if not impossible to obtain, is not needed for such a design. An important part of this publication is focused on the presentation of simulation results with actual and virtual data. The actual data, used in Matlab as reference trajectories, have been obtained from a properly instrumented car (Peugeot 406). Other virtual sets of data have been generated through the interconnected platform SiVIC/RTMaps. It is a dedicated virtual simulation platform for prototyping and validation of advanced driving assistance systems. Keywords- Longitudinal and lateral vehicle control, model-free control, intelligent P controller (i-P controller), algebraic estimation, ADAS (Advanced Driving Assistance Systems).

This research concerns issues arising in the design and implementation of advanced driver assistance systems, specifically longitudinal and lateral controllers; their effects on the dynamic performance of the vehicle, and their impact on the driver. The current state of the art is discussed as part of an extensive literature review, which highlights prominent gaps in the published research. There is a lack of understanding as to the effects of adverse environmental factors on the vehicle dynamics, and the effects of the systems' on the comfort of the driver. A novel twin track approach was taken to investigate these issues: the effects of the systems' on the vehicle dynamics were monitored using a range of off-line simulation tools, while the systems' impact on the driver was considered using an on-line driving simulator experiment. An adaptive cruise control system was developed, tuned to provide a comfortable response and implemented on a sophisticated off-line 9 degree of freedom vehicle model, with a non-linear tyre model. The system was tested in a range of environmental conditions. These simulations highlighted the good performance of the system in wet conditions, and revealed some possible driver conflicts. Two lateral control systems were developed, one based on a look down methodology, and the second on a more driver emulating look ahead approach. The systems were tested using the same high fidelity vehicle model, and an extensive range of suitable motorway manoeuvres. The systems were compared, proving the comfort and stability benefits of the look ahead system. The longitudinal and lateral control systems were integrated with the University of Leeds driving simulator. Ten subject drivers drove with and without the systems through a range of scenarios, some of which required evasive action. The impact of the systems on the driver, and the driver's response to safety critical scenarios was assessed. Results displayed little safety benefit of the systems in evasive scenarios, but drivers perceived improved awareness and comfort when under their control. The potential of advanced driver assistance systems to make driving a more comfortable and safe experience has been demonstrated, although the system engineer must consider the impact of the systems on the driver throughout their design and implementation

Space mission design places a premium on cost and operational efficiency. The search for new science and life beyond Earth calls for spacecraft that can deliver scientific payloads to geologically rich yet hazardous landing sites. At the same time, the last four decades of optimization research have put a suite of powerful optimization tools at the fingertips of the controls engineer. As we enter the new decade, optimization theory, algorithms, and software tooling have reached a critical mass to start seeing serious application in space vehicle guidance and control systems. This survey paper provides a detailed overview of recent advances, successes, and promising directions for optimization-based space vehicle control. The considered applications include planetary landing, rendezvous and proximity operations, small body landing, constrained attitude reorientation, endo-atmospheric flight including ascent and reentry, and orbit transfer and injection. The primary focus is on the last ten years of progress, which have seen a veritable rise in the number of applications using three core technologies: lossless convexification, sequential convex programming, and model predictive control. The reader will come away with a well-rounded understanding of the state-of-the-art in each space vehicle control application, and will be well positioned to tackle important current open problems using convex optimization as a core technology.

This research study focuses on the design of advanced propulsion cycles, having as primary design goal the improvement on noise emissions and fuel consumption. In this context, a preliminary cycle design method has been developed and applied on four novel propulsion systems; ultra high bypass ratio, recuperated, intercooled-recuperated, constant volume combustion turbofans. The analysis has shown significant improvement in jet noise, and fuel consumption, as a result of high bypass ratio. Additionally, a comparison to future fuel-optimised cycle has revealed the trade-off between noise emissions and fuel consumption, where a reduction of ~30dBs in jet noise may be achieved in the expense of ~10% increase of mission fuel. A second aspect of this study is the integration of the propulsion system for improving fan noise. A novel approach is followed, by half-embedding the turbofan in the upper surface of the wing of a Broad Delta airframe. Such an installation aids in noise reduction, by providing shielding to component (fan) noise. However, it leads to significant inlet distortion levels. In order to assess the effect of installation-born distortion on performance an enhanced fan representation model has been developed, able to predict fan and overall engine performance sensitivity to three-dimensional distorted inlet flow. This model that comprises parallel compressor theory and streamline curvature compressor modelling, has been used for proving a linear relation between the loss in fan stability margins and engine performance. In this way, the design engineer can take into consideration distortion effects on off-design performance, as early as, at the stage of preliminary cycle design

Iterative Learning Control (ILC) can achieve perfect tracking performance for mechatronic systems. The aim of this paper is to present an ILC design tutorial for industrial mechatronic systems. First, a preliminary analysis reveals the potential performance improvement of ILC prior to its actual implementation. Second, a frequency domain approach is presented, where fast learning is achieved through noncausal model inversion, and safe and robust learning is achieved by employing a contraction mapping theorem in conjunction with nonparametric frequency response functions. The approach is demonstrated on a desktop printer. Finally, a detailed analysis of industrial motion systems leads to several shortcomings that obstruct the widespread implementation of ILC algorithms. An overview of recently developed algorithms, including extensions using machine learning algorithms, is outlined that are aimed to facilitate broad industrial deployment.