Collaborative robots cooperate with humans to assist them in undertaking simple-to-complex tasks in several fields, including industry, education, agriculture, healthcare services, security, and space exploration. These robots play a vital role in the revolution of Industry 4.0, which defines new standards of manufacturing and the organization of products in the industry. Incorporating collaborative robots in the workspace improves efficiency, but it also introduces several safety risks. Effective safety measures then become indispensable to ensure safe and robust interaction. This paper presents the review of low-level control methodologies of a collaborative robot to assess the current status of human–robot collaboration over the last decade. First, we discuss the classification of human–robot collaboration, architectures of systems and the complex requirements on control strategies. The most commonly used control approaches were presented and discussed. Several methods of control, reported in industrial applications, are elaborated upon with a prime focus on HR-collaborative assembly operations. Since the physical HRC is a critical control problem for the co-manipulation task, this article identifies key control challenges such as the prediction of human intentions, safety, and human-caused disturbances in motion synchronization; the proposed solutions were analyzed afterwards. The discussion at the end of the paper summarizes the features of the control systems that should be incorporated within the systematic framework to allow the execution of a robotic task from global task planning to low-level control implementation for safe and robust interactions.

Biosolids storage areas are a significant contributor to wastewater treatment plant (WWTPs) odour emissions which can cause sensorial impact to surrounding communities. Most odour impact regulations are based on odour concentration (COD) measurements determined by dynamic olfactometry. Understanding the relationship between odorants concentrations and COD in the biosolids emission is important to identify how the measurement and monitoring can be conducted using analytical rather than sensorial techniques. Some of the odorants are unknown in biosolid emissions, increasing the uncertainty in predicting COD. In this study, emissions from 56 biosolid samples collected from two WWTPs located in Sydney, Australia, were analysed by analytical and sensorial methods, including olfactory detection port (ODP) and dynamic olfactometry. Concentrations of 25 odorants and two ordinal variables represented odour events detected by ODP assessors were linked to COD values. Bayesian Model Averaging and Variable Selection with Bayesian Adaptive Sampling were applied to model the relation between COD and odorants concentrations. Results indicate the usability of the probabilistic methods and nonlinear transformations in modelling the odour concentrations based on odorants concentrations from biosolids emission and the accuracy of a small dataset.

Fractional-order control system design can be used for systems with non-local dynamics involving long-term memory effects. However, implementation of a fractional-order controller in industrial systems is complicated, because of the excessive demand for computational power. The following paper presents the step-by-step design procedure, parameter tuning, and experimental evaluation of the fractional order proportional-integral-derivative (FOPID) controller. The control algorithm is based on the Continued Fraction Expansion approximation of the fractional-order operators. It is implemented on a standard industrial Programmable Logic Controller. The FOPID control system is verified and evaluated in terms of efficiency and robustness using a new laboratory benchmark of a temperature control in the pipeline. The proposed solution shows increased efficiency in terms of robustness compared to the standard PID closed-loop control.