Vibration suppression in multi-body systems by means of disturbance filter design methods

This paper addresses the problem of interaction in mechanical multi-body systems and shows that subsystem interaction can be considerably minimized while increasing performance if an efficient disturbance model is used. In order to illustrate the advantage of the proposed intelligent disturbance filter, two linear model based techniques are considered: IMC and the model based predictive (MPC) approach. As an illustrative example, multivariable mass-spring-damper and quarter car systems are presented. An adaptation mechanism is introduced to account for linear parameter varying LPV conditions. In this paper we show that, even if the IMC control strategy was not designed for MIMO systems, if a proper filter is used, IMC can successfully deal with disturbance rejection in a multivariable system, and the results obtained are comparable with those obtained by a MIMO predictive control approach. The results suggest that both methods perform equally well, with similar numerical complexity and implementation effort

Nonlinear predictive control applied to steam/water loop in large scale ships

In steam/water loop for large scale ships, there are mainly five sub-loops posing different dynamics in the complete process. When optimization is involved, it is necessary to select different prediction horizons for each loop. In this work, the effect of prediction horizon for Multiple-Input Multiple-Output (MIMO) system is studied. Firstly, Nonlinear Extended Prediction Self-Adaptive Controller (NEPSAC) is designed for the steam/water loop system. Secondly, different prediction horizons are simulated within the NEPSAC algorithm. Based on simulation results, we conclude that specific tuning of prediction horizons based on loop’s dynamic outperforms the case when a trade-off is made and a single valued prediction horizon is used for all the loops

Benchmarking Dynamic Balancing Controllers for Humanoid Robots

This paper presents a comparison study of three control design approaches for humanoid balancing based on the Center of Mass (CoM) stabilization and body posture adjustment. The comparison was carried out under controlled circumstances allowing other researchers to replicate and compare our results with their own. The feedback control from state space design is based on simple models and provides sufficient robustness to control complex and high Degrees of Freedom (DoFs) systems, such as humanoids. The implemented strategies allow compliant behavior of the robot in reaction to impulsive or periodical disturbances, resulting in a smooth and human-like response while considering constraints. In this respect, we implemented two balancing strategies to compensate for the CoM deviation. The first one uses the robot’s capture point as a stability principle and the second one uses the Force/Torque sensors at the ankles to define a CoM reference that stabilizes the robot. In addition, was implemented a third strategy based on upper body orientation to absorb external disturbances and counterbalance them. Even though the balancing strategies are implemented independently, they can be merged to further increase balancing performance. The proposed strategies were previously applied on different humanoid bipedal platforms, however, their performance could not be properly benchmarked before. With this concern, this paper focuses on benchmarking in controlled scenarios to help the community in comparing different balance techniques. The key performance indicators (KPIs) used in our comparison are the CoM deviation, the settling time, the maximum measured orientation, passive gait measure, measured ankles torques, and reconstructed Center of Pressure (CoP). The benchmarking experiments were carried out in simulations and using the facility at Istituto Italiano di Tecnologia on the REEM-C humanoid robot provided by PAL robotics inside the EU H2020 project EUROBENCH framework

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Adaptive control and identification for on-line drug infusion in anaesthesia.

Anaesthesia is that part of the medical science profession which ensures that the patient’s body is insensitive to pain and possibly other stimuli during surgical operations. It includes muscle relaxation (paralysis) and unconsciousness, both conditions being crucial for the operating surgeon. Maintaining a steady level of muscle relaxation as well as an acceptable depth of anaesthesia (unconsciousness), while keeping the dosage of administered drugs which induce those effects at a minimum level, have successfully been achieved using automatic control. Fixed gain controllers such as P, PI, and PID strategies can perform well when used in clinical therapy and under certain conditions but on the other hand can lead to poor performances because of the large variability between subjects. This is the reason which led to the consideration of adaptive control techniques which seemed to overcome such problems. Two control strategies falling into the above scheme and including the two newly developed techniques, i.e Proportional-Integral-Plus (PIP) control algorithm, and Generalized Predictive Control algorithm (GPC), are considered under extensive simulation studies using the muscle relaxation process associated with two drugs known as Pancuronium-Bromide and Atracurium. Both models exhibit severe non-linearities as well as time-varying dynamics and delays. Only the strategy corresponding to the GPC algorithm is retained for implementation on a 380Z disk-based microcomputer system, while the muscle relaxation process corresponding to either drugs is simulated on a VIDAC 336 analogue computer. The sensitivity of the algorithm is investigated when patient-to-patient parameter variability is evoked. The study is seen to provide the necessary basis for future clinical implementation of the scheme. Following the satisfactory results obtained under such a real-time environment, the self-adaptive GPC algorithm has been successfully applied in theatre to control Atracurium infusion on humans during surgery. This success later motivated further research work in which simultaneous control of muscle relaxation and anaesthesia (unconsciousness) was achieved. A good multivariable model has been derived and controlled via the multivariable version of the SISO GPC algorithm. The results obtained are very encouraging