Classical cueing, and other “static” filters, benefit from simplicity and popularity in industry. However, workspace constraints are not incorporated in these approaches — there is no mechanism preventing workspace constraint violations.

On the other hand, Model Predictive Control (MPC) is an effective solution to the workspace and cueing problem. Indeed, the cueing problem can be thought of as a constrained double integration, a linear model which fits well into the MPC paradigm.

Disadvantages of MPC are that it is difficult to tune and requires expert knowledge to implement correctly. Maintaining consistent cueing “character” across different scenarios and vehicles is harder compared with the classical approach. In the author's experience, many elite drivers prioritise “consistency” over “cueing quality”.

It is therefore desirable to augment Classical Washout Filters (CWF's) with workspace constraint functionality. An approach for doing so is given here.

In the marketing material for motion simulations, most, if not all, emphasis is placed on physical aspects: workspace size in terms of displacement, velocity and acceleration, latency and so on. Though important, these attributes are only as useful as the motion cueing system is able to exploit. In practice, poorly parametrised Motion Cueing Algorithms (MCA's), (unexpected) driver changes, lack of a dedicated (often part-time) cueing engineer and a lack of pre-session MCA testing severely compromises simulator outcomes. In light of this, ProCue ™ was conceived. Drawing on over a decade of simulation experience acquired at McLaren, Toyota Gazoo Racing, dSPACE, Bosch and others, ProCue helps to ensure deployment of the best MCA, with optimal parametrisation, for given simulator(s), scenario(s) and driver(s). Procue may even help you quantify the theoretical benefits of a new simulator prior to purchase. Moreover, given the innumerable permutations of the above, robust experiment management and trace-ability is urgently needed in the motion cueing domain-ProCue frees resource for what really matters: vehicle development.

Canonically, accelerations of a vehicle in a (fixed) reference frame are translated to washout, this introduces unwanted cross-axis distortion that scales with simulator capability. In this paper, a new variant of classical cueing is introduced that nearly eliminates cross-axis distortion entirely whilst enhancing washout behavior.

The washout or recentering of a motion simulator platform to its neutral or nominal position is guaranteed by the numerical properties of classical washout filters. Ideally, the sensation of the platform recentering should be imperceptible to the driver at all times-formally that the resulting translational accelerations and angular velocities fall below some perception thresholds. Exceeding these can cause an unwanted sensation (mis-cue) whose prevention is not guaranteed by linear washout filters-rather, the risk is minimised by careful tuning. Nonetheless, even a single miscue can break immersion or accelerate the onset of motion sickness. The need to avoid miscues may even lead an operator to compromise overall motion. In this paper, the classical washout filter for the acceleration case is refactored as the sum of a 'transient' and 'recentering' signal. It is shown the latter is equivalent to the closed loop Proportional Derivative (PD) control of a double integrating system. Since the saturated PD control of a double integrator system is known to be globally asymptotically stable, saturation may be applied to the 'recentering' signal so it does not exceed some perception threshold. The limitations of this approach are discussed.

The washout or recentering of a motion simulator platform to its neutral or nominal position is guaranteed by the numerical properties of classical washout filters. Ideally, the sensation of the platform recentering should be imperceptible to the driver at all times-formally that the resulting translational accelerations and angular velocities fall below some perception thresholds. Exceeding these can cause an unwanted sensation (mis-cue) whose prevention is not guaranteed by linear washout filters-rather, the risk is minimised by careful tuning. Nonetheless, even a single miscue can break immersion or accelerate the onset of motion sickness. The need to avoid miscues may even lead an operator to compromise overall motion. In this paper, the classical washout filter for the acceleration case is refactored as the sum of a 'transient' and 'recentering' signal. It is shown the latter is equivalent to the closed loop Proportional Derivative (PD) control of a double integrating system. Since the saturated PD control of a double integrator system is known to be globally asymptotically stable, saturation may be applied to the 'recentering' signal so it does not exceed some perception threshold. The limitations of this approach are discussed.

This paper proposes a new differential evolution (DE) algorithm for unconstrained continuous optimisation problems, termed JADE, that uses a small or ‘micro’ ( ) population. The main contribution of the proposed DE is a new mutation operator, ‘current-by-rand-to-pbest.’ With a population size less than 10, JADE is able to solve some classical multimodal benchmark problems of 30 and 100 dimensions as reliably as some state-of-the-art DE algorithms using conventionally sized populations. The algorithm also compares favourably to other small population DE variants and classical DE.

Presently, the uncertainties associated with controlling domestic heating system are managed using rule of thumb or heuristic rule-based controllers. The problems associated with this are: lack of bespokeness and optimality of the control to each unique building, difficulty in comparing technologies due to inconsistent control quality (lack of generality) and the expense of developing controllers for new technologies. In this work, the problem of heating system control is generalised with the intent of developing a generic-optimal controller — one that can control any set of heat sources in any building optimally, alleviating the aforementioned problems. A hybrid intelligent system design methodology is applied in order to develop the (model predictive) controller resulting in two sub-tasks. First, acquiring a model of each heating system — identification must be carried out on-line. Second, delivering optimal control using the model, given constraints. The first is tackled by applying Echo State Networks (ESN’s), whose benefits are that they have universal approximation ability, on-line learning is a recursive linear regression problem (for which the solution even in low precision environments is well understood), that on-line learning can be easily achieved using real feedbacks (network stability is relatively easy to attain) and that they can be easily scaled to systems of varying complexity. The second is tackled by using a global, derivative-free optimiser — meaning that the controller may tackle mixed integer problems and incorporate arbitrary output constraints expressed as penalty functions. A theoretical third problem arises due to the interaction of the learning and optimisation components of the controller. A methodology for tackling this is given. When applied to a simulated monovalent heating system in an unoccupied house (in the absence of user disturbances) consistent control can be achieved. The effective rejection of user disturbances is an outstanding problem and is briefly discussed..

Video games, especially driving ones, are becoming more accurate. So much so that it is now even possible to transition from e-sports to actual motor racing. Such models are not entirely accurate however. They prioritise stability in order to accommodate even the most unhinged driving, not to mention the janky input profile of a keyboard or controller.

Motion simulators operate in limited space. This necessitates routinely recentralising or "washing out" the cockpit in order to have room for the next manoeuvre. Ideally, this recentering is carried out at a small enough acceleration that it is imperceptible to the driver - too high and the risk of motion sickness greatly increases.