Modular model predictive control upon an existing controller

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

Improving Tracking in Optimal Model Predictive Control

The thesis deals with the improvement in the tracking in model predictive control(MPC). The main motivation is to explore high embedding performance controllers with constraint handling capabilities in a simple fashion. There are several techniques available for effectively using an infinite horizon rather than a finite horizon. First, there has been relatively little discussion so far on how to make effective use of advance information on target changes in the predictive control literature. While earlier work has indicated that the default solutions from conventional algorithms are often poor, very few alternatives have been proposed. This thesis demonstrates the impact of future information about target changes on performance, and proposes a pragmatic method for identifying the amount of future information on the target that can be utilised effectively in infinite horizon algorithms. Numerical illustrations in MATLAB demonstrate that the proposal is both systematic and beneficial. This thesis introduces several important issues related to model predictive control (MPC)tracking that have been hitherto neglected in the literature, by first deriving a control law for future information about target changes within optimal predictive control (OMPC) for both nominal and constraints cases. This thesis proposes a pragmatic design for scenarios in which the target is unreachable. In order to deal with an unreachable target, the proposed design allows an artificial target into the MPC optimisation problem. Numerical illustrations in MATLAB provide evidence of the efficacy of the proposals. This thesis extends efficient, robust model predictive control (MPC) approaches for Linear Parameter-Varying (LPV) systems to tracking scenarios. A dual-mode approach is used and future information about target changes is included in the optimisation tracking problem. The controller guarantees recursive feasibility by adding an artificial target as an extra degree of freedom. Convergence to admissible targets is ensured by constructing a robustly invariant set to track any admissible target. The efficacy of the proposed algorithm is demonstrated by MATLAB simulation. The thesis considers the tractability of parametric solvers for predictive control based optimisations, when future target information is incorporated. It is shown that the inclusion of future target information can significantly increase the implied parametric dimension to an extent that is undesirable and likely to lead to intractable problems. The thesis then proposes some alternative methods for incorporating the desired target information, while minimising the implied growth in the parametric dimensions, at some possibly small cost to optimality. Feasibility is an important issue in predictive control, but the influence of many important parameters such as the desired steady-state, the target and the current value of the input is rarely discussed in the literature. At this point, the thesis makes two contributions. First, it gives visibility to the issue that including the core parameters, such as the target and the current input, vastly increases the dimension of the parametric space, with possible consequences on the complexity of any parametric solutions. Secondly, it is shown that a simple re-parametrization of the degrees of freedom to take advantage of allowing steady-state offset can lead to large increases in the feasible volumes, with no increases in the dimension of the required optimisation variables. Simulation with MAT LAB 2017a provides the evidence of the efficacy of all proposals

Blade-pitch Control for Wind Turbine Load Reductions

Large wind turbines are subjected to the harmful loads that arise from the spatially uneven and temporally unsteady oncoming wind. Such loads are the known sources of fatigue damage that reduce the turbine operational lifetime, ultimately increasing the cost of wind energy to the end users. In recent years, a substantial amount of studies has focused on blade pitch control and the use of real-time wind measurements, with the aim of attenuating the structural loads on the turbine blades and rotor. However, many of the research challenges still remain unsolved. For example, there exist many classes of blade individual pitch control (IPC) techniques but the link between these different but competing IPC strategies was not well investigated. In addition, another example is that many studies employed model predictive control (MPC) for its capability to handle the constraints of the blade pitch actuators and the measurement of the approaching wind, but often, wind turbine control design specifications are provided in frequency-domain that is not well taken into account by the standard MPC. To address the missing links in various classes of the IPCs, this thesis aims to investigate and understand the similarities and differences between each of their performance. The results suggest that the choice of IPC designs rests largely with preferences and implementation simplicity. Based on these insights, a particular class of the IPCs lends itself readily for extracting tower motion from measurements of the blade loads. Thus, this thesis further proposes a tower load reduction control strategy based solely upon the blade load sensors. To tackle the problem of MPC on wind turbines, this thesis presents an MPC layer design upon a pre-determined robust output-feedback controller. The MPC layer handles purely the feed-forward and constraint knowledge, whilst retaining the nominal robustness and frequency-domain properties of the pre-determined closed-loop. Thus, from an industrial perspective, the separate nature of the proposed control structure offers many immediate benefits. Firstly, the MPC control can be implemented without replacing the existing feedback controller. Furthermore, it provides a clear framework to quantify the benefits in the use of advance real-time measurements over the nominal output-feedback strategy