K.R. Uren

Predictive PID Control of Non-Minimum Phase Systems

Control engineers have been aware of non-minimum phase systems showing either undershoot or time-delay characteristics for some considerable time (Linoya & Altpeter, 1962; Mita & Yoshida, 1981; Vidyasagar, 1986; Waller & Nygardas, 1975). A number of researchers that addressed this problem from a predictive control point of view mainly followed one of two approaches: a classical (non-optimal) predictive approach or a modern optimisation based predictive approach (Johnson & Moradi, 2005). The common characteristic of all these approaches is that they are model-based. Predictive control allows the controller to predict future changes in the output signal and to use this prediction to generate a desirable control variable. The classical predictive controllers that are most widely considered include the Smith predictor structure and the internal model control (IMC) structure (Katebi & Moradi, 2001; Morari & Zafiriou, 1989; Tan et al., 2001). Modern predictive controllers consider generalised predictive control (GPC) or model-based predictive control (MPC) structures (Johnson & Moradi, 2005; Miller et al., 1999; Moradi et al., 2001; Sato, 2010). The performance of a PID controller degrades for plants exhibiting non-minimum phase characteristics. In order for a PID controller to deal with non-minimumphase behaviour, some kind of predictive control is required (Hagglund, 1992). Normally the derivative component of the PID controller can be considered as a predictive mechanism, however this kind of prediction is not appropriate when addressing non-minimum phase systems. In such a case the PI control part is retained and the prediction is performed by an internal simulation of plant inside the controller. This chapter starts with a quick review of the system-theoretic concept of a pole and zero and then draws the relationship to non-minimum phase behaviour. The relationship between the undershoot response and time-delay response will be discussed using Pade approximations. Classical and modern predictive PID control approaches are considered with accompanying examples. The main contribution of the chapter is to illustrate the context and categories of predictive PID control strategies applied to non-minimum phase systems by:

A study of the loss characteristics of a single cell PEM electrolyser for pure hydrogen production

In this paper the losses of a proton exchange membrane (PEM) single cell electrolyser were investigated. The ohmic, activation and mass transfer losses are the most prominent losses in a PEM electrolyser. The Electrochemical Impedance Spectroscopy (EIS) method was applied to identify these losses. A parametric study of each loss component was performed by changing a component or condition responsible for the loss. The membrane thickness was varied in the Membrane Electrode Assembly (MEA) to identify the ohmic loss and the temperature was changed to capture the activation loss. Two different diffusion media were used to investigate the mass transfer effect. The results were confirmed with polarisation curves and Tafel plots.

Equivalent electrical circuit modelling of a Proton Exchange Membrane electrolyser based on current interruption

A Proton Exchange Membrane (PEM) electrolyser is characterised and modelled to identify important electrochemical effects. These electrochemical effects include the ohmic, activation, and concentration losses within the PEM electrolyser during hydrogen production. The electrochemical effects of the PEM electrolyser are modelled by means of equivalent electrical circuits. The equivalent electrical circuit components of importance are the membrane resistance, the charge transfer resistance, the double layer capacitance and the Warburg impedance. The current interrupt method is proposed by using, (i) the natural voltage response method and (ii) system identification, for solving the parameters of the equivalent electrical circuits. In this paper both simulation and experimental results are provided.

Sizing of renewable energy hydrogen systems

This paper explains the simulation and sizing optimization of renewable energy systems for hydrogen production. These systems are complex and challenging to size correctly due to the nonlinearities of the components and the stochastic nature of the sources. The same system will perform completely different at two separate sites. The simulation includes renewable energy sources, storage elements and loads. Matlab® and Simulink− are the tools utilized for the simulation and optimization. The simulation and optimization of the Renewable Energy (RenEn) system, to be constructed in Potchefstroom, South Africa, is described and some of the results obtained are discussed. The sizing of components is essential to ensure that the most efficient and cost-effective system is specified.

Heading alignment with summarized inertial pose constraints

A discontinuity seems to exist in present aided inertial navigation systems - the operational availability of high cost inertial navigation currently depends on the availability of a valid GPS lock. In general GPS solutions can be two or more orders cheaper than their inertial sensors counterparts. This paper addresses the north alignment by gyrocompassing in the context of more modern smoothing techniques. Incremental smoothing is becoming a serious contender for in-situ state estimation. The work here presents a probabilistic model for north alignment with an analysis towards the achievable accuracy of such a system. A pose graph formulation is used to estimate inertial sensor calibration terms over a long spanning trajectory, from which a true north alignment can be extracted. The preintegral method is used to condense high rate inertial data, but allow for post integration bias correction. Results show that the proposed alignment scheme is feasible, and would for gyrocompassing alignment relaxed sensor performance constraints.

A FEM model for microwave absorption and heat transfer in a microwave combination washer dryer

Applications for the use of microwaves reach far and wide in various industries. Commonly known is the use of microwaves to heat foodstuffs. Research and development conducted in the development of a microwave combination washer dryer required the development of a system model, allowing for the development of a control system. Considered in this work is the propagation of microwaves within the system along with the heat transfer throughout the system. This paper presents a FEM model based on the geometry of the prototype combination washer dryer. Model results are validated by practical measurements using the combination washer dryer laboratory prototype. The model is at the time of writing undergoing final validation, with preliminary results indicating that the model fairly accurately represents the system. The simulation and practical system were compared by calculating the average of the absolute value of the difference between the data sets. Three of the five temperature points logged have an average absolute temperature difference of less then 0.7°C and the final two points logged have an average absolute difference of 1.2°C and 2.5°C respectively.

Genetic Algorithm based PID Tuning for Optimal Power Control of a Three-shaft Brayton Cycle based Power Conversion Unit

Abstract This paper considers the development of a PID control strategy to optimally control the power output of a High Temperature Gas-cooled Reactor (HTGR) power plant. A specific type of HTGR called the Pebble Bed Modular Reactor (PBMR) that utilises a closed recuperative Brayton cycle with helium as working fluid is considered. The power control of this kind of plant is significantly different from conventional steam cycle nuclear power plants. A distinguishing feature that complicates the control is the use of three separate shafts for different compressor/turbine or turbine/generator pairs. In addition the power output cannot be directly controlled by means of an upstream valve that regulates the flow through the power turbine, as is the case with conventional steam cycles. This paper addresses these challenges by means of a control strategy consisting of four PID control loops. The controller gains are optimised by means of a Genetic Algorithm (GA) that uses real-valued genes and the ITAE performance measure as a cost function. The control strategy is implemented and evaluated on a linear Simulink® model of the PBMR Power Conversion Unit (PCU). Results are presented illustrating the performance of the GA optimised PID control strategy.