Simulation plays a pivotal role in providing safely reproducible scenarios to evaluate the ever-advancing domain of computer science and robotics. It was an essential part of the pandemic when no access to physical spaces was available. The advent of AI-powered platforms in conjunction with enhanced graphics, physics and other sensory engines attracts a new breed of interdisciplinary researchers to enter the robotic field, most notably from computer science, engineering and social sciences. Integration of ROS as a uniform middleware to deploy achieved outcomes in real practice provides an opportunity to move one step closer to the sim-to-real experiences that enables researchers to test ideas beyond the close laboratory spaces. There is a lack of comprehensive evaluation of ROS-enabled simulators, and the integration of advanced AI techniques for realistic scenario replication. This paper addresses this challenge by evaluating ROS-enabled simulators in the design and implementation of AI techniques through an in-depth systematic literature review (SLR). This SLR is guided by the research and commercial market demands, employing Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) frameworks with a major focus on Wheeled Mobile Robots (WMRs). We also highlight the increasing importance of game engines like Unity and Unreal in future of robotic simulations, especially under modelling close to real experiences. By comparing simulation platform features and capabilities, this paper offers guidance to developers and researchers, enabling them to select the most suitable platform for their projects efficiently that contradicts the commonly in use “one size fits all” approach. Finally, based on the thorough insights from the review, we identify and suggest some key future research directions in AI-enhanced realistic robotic simulations.

Background: In this paper, the method of designing a noninvasive device for eliminating hand tremors in Parkinson's patients is presented. The designed device measures the tremors of the patient's hand and implements the tremor control accordingly. Since Parkinson's disease reduces patients’ abilities to perform daily activities, this device is designed as an electronic spoon. The inertial measurement units are used to measure hand tremors. Method: The signals got from motion sensors are passed through Butterworth's second order low pass filters to attenuate signals amplitude at frequencies higher than the human hand's natural frequency. The signals are sent to a proposed Proportional Integral (PI) fuzzy controller as a set point signal, and appropriate control signals are applied to two actuators installed orthogonal. Besides motion sensors, a microcontroller is installed inside the spoon handle that implements a PI fuzzy controller and provides control signals for two high speed servo motors installed perpendicularly. Results: As such, the spoon can minimize the tremor effect. In this system, no damper or mass is added to the hand, and the patients are not required to wear an orthosis. The contribution of this paper is twofold. First, we use sensor data fusion to increase measurement accuracy. In this paper, we use accelerometer and gyroscope sensors. Second, we proposed a robust PI fuzzy controller to compensate for the uncertainties and reduce the tremor. Conclusion: The test results show that the hand tremor of Parkinson's patients during eating is reduced up to 75% using this method.

Actuator faults are inevitable in small reverse osmosis desalination plants. It may cause energy losses and reduce the quality of the freshwater, which may endanger human life. This paper focuses on the integrated fault detection and fault-tolerant control approach. The primary motivation of this paper is to propose a novel integrated fault detection and fault-tolerant control approach. The actuator fault is estimated using the concept of parity space approach. Then the system model is updated in the fault-tolerant control block using the information of the estimated fault parameter. Moreover, the proposed approach uses the receding-horizon predictive control-bounded data uncertainties controller, which is the robust and stable variant of generalized predictive control. The remaining uncertainty caused by the model and observer is compensated by this controller. The structure of a small reverse osmosis desalination plant is deployed. In this plant, the permeate flow rate and conductivity are controlled by a retentate valve and a bypass valve, which add a small amount of inlet to the outlet. The performances of three predictive model controllers are evaluated, and a comparison is made between their computational costs, stability, and robustness. The plant is considered to be linear time-invariant and subject to model uncertainties, measurement noise, and actuator fault in the retentate valve as efficiency dropping. The results reveal the robustness of the proposed approach concerning noise and matched uncertainties as well as its accommodation to actuator fault up to 90%.

In this study, we propose a modified model predictive control (MPC) strategy for managing the thermal load in buildings, aimed at creating a fine-tuned balance between indoor thermal comfort and electricity cost reduction. Here, the multi-zone building’s state-space model is employed to dynamically manage energy consumption while preserving occupant comfort. The key contributions of this work include the development of a novel economic MPC strategy tailored for multi-zone heating, ventilation, and air conditioning (HVAC) systems, integrating thermal energy storage to optimise energy usage and occupant comfort. Additionally, we introduce an enhanced multi-objective optimisation framework that transforms the conflicting objectives of energy efficiency and occupant comfort into a single-objective problem for improved computational efficiency. The control strategy also incorporates dynamic electricity pricing, enabling cost-effective operation by shifting energy consumption to lower-cost periods. The proposed control method reduces fluctuations in indoor air temperature, extending the operational life of HVAC system actuators. Beyond reducing costs and consumption, this approach alleviates energy production strain and peak demand on the smart grid. The optimisation process incorporates user-defined temperature preferences for each zone, ensuring tailored comfort conditions. Simulation results show that this method maintains indoor air temperature within the desired comfort range, outperforming traditional methods prone to fluctuations. Furthermore, the proposed MPC strategy effectively shifts the peak load to periods of lower electricity prices, achieving an 18.58% reduction in overall energy costs.