Improving Closed-Loop Signal Shaping of Flexible Systems with Smith Predictor and Quantitative Feedback

Input shaping is a technique used to move flexible systems from point to point rapidly by suppressing the residual vibration at the destination. The vibration suppression is obtained from the principle of destruction of impulse responses. The input shaper, when placed before the flexible system inside the control loop, proves to deliver several benefits. However, this so-called closed-loop signal shaping has one major disadvantage that it adds time delays to the closed-loop system. Being a transcendental function, the time delays cause difficulty in analysis and design of the feedback controller. In most cases, the time delays also limit the maximum achievable bandwidth. In this paper, for the very first time, Smith predictors were applied to the closed-loop signal shaping to remove the time delay from the loop. It was shown in simulation result that the detrimental effect of the time delays was completely removed in the case of perfect plant model. The quantitative feedback control was used in the study to quantify the amount of achievable bandwidth and to suppress vibrations from the plant-input disturbance

Modelling of Human Control and Performance Evaluation using Artificial Neural Network and Brainwave

Conventionally, a human has to learn to operate a machine by himself / herself. Human Adaptive Mechatronics (HAM) aims to investigate a machine that has the capability to learn its operator skills in order to provide assistance and guidance appropriately. Therefore, the understanding of human behaviour during the human-machine interaction (HMI) from the machine’s side is essential. The focus of this research is to propose a model of human-machine control strategy and performance evaluation from the machine’s point of view. Various HAM simulation scenarios are developed for the investigations of the HMI. The first case study that utilises the classic pendulum-driven capsule system reveals that a human can learn to control the unfamiliar system and summarise the control strategy as a set of rules. Further investigation of the case study is conducted with nine participants to explore the performance differences and control characteristics among them. High performers tend to control the pendulum at high frequency in the right portion of the angle range while the low performers perform inconsistent control behaviour. This control information is used to develop a human-machine control model by adopting an Artificial Neural Network (ANN) and 10-time- 10-fold cross-validation. Two models of capsule direction and position predictions are obtained with 88.3% and 79.1% accuracies, respectively. An Electroencephalogram (EEG) headset is integrated into the platform for monitoring brain activity during HMI. A number of preliminary studies reveal that the brain has a specific response pattern to particular stimuli compared to normal brainwaves. A novel human-machine performance evaluation based on the EEG brainwaves is developed by utilising a classical target hitting task as a case study of HMI. Six models are obtained for the evaluation of the corresponding performance aspects including the Fitts index of performance. The averaged evaluation accuracy of the models is 72.35%. However, the accuracy drops to 65.81% when the models are applied to unseen data. In general, it can be claimed that the accuracy is satisfactory since it is very challenging to evaluate the HMI performance based only on the EEG brainwave activity