Roberto Strada

Pneumatic actuators are widely used components for industrial automation, primarily due to their high power-to-volume ratio, high speed, simplicity of use, and the ease with which they can satisfy applications’ motion requirements. The accurate evaluation of the energy consumption in pneumatic systems is a topic of increasing importance due to contemporary attention to sustainability. Actuators of the same size have different pneumatic energy needs depending on the specific function they must accomplish, in terms of displacement, force, operational speed, and motion time. The dedicated literature focuses either on the best practices for the configuration of the circuit or on the pneumatic energy consumption in specific sections of the system; when electrical energy consumption is treated, only steady-state conditions are considered. Starting from this state of the art, this paper proposes a new general procedure for the assessment of the electrical energy required to perform specific functions in industrial machinery that relies on pneumatic actuators. The procedure is based on the evaluation of the air mass consumption of the actuator considering its actual application requirements, either through experimental measurements performed on the machinery or through a numerical simulation of the system. Regarding the numerical approach, an experimentally validated dynamic multi-domain model of the pneumatic system is proposed to also properly analyse the relevant transient behaviour. Starting from the evaluation of the air mass consumption, the procedure outlines how to calculate the corresponding electrical energy requirements, considering the application requirements and the operating point of the compressor. To this end, the procedure also includes a method to tailor the ISO 1217 standard to the actual working conditions of the compressor. The procedure, which enables the accurate mapping of the requirements placed by the application on the pneumatic actuator to the ultimately required electrical energy inputs, is validated through several simulations of real cyclic application cases. The results in terms of electrical energy consumption are compared to those obtained with the traditional method based on the steady-state behaviour of the system; the comparison shows that the proposed procedure evaluates the energy consumption with greater accuracy in applications characterized by highly dynamic work cycles.

Energy efficiency and sustainability are core issues in the modern design and management of industrial machinery and plants. These concerns are reflected and reinforced by the Sustainable Development Goal 9 of the United Nations (SDG9), “Industry, innovation and infrastructure”, which enshrines efficiency and optimized energy use as key features of sustainable production systems. As the engineering of industrial machinery reorients itself towards energy sustainability, attention is naturally shifting to actuators, since these components unavoidably waste part of the considerable amount of energy they absorb to execute their functions. Hydraulic actuation systems, while uniquely suited to heavy-duty applications, are particularly affected by poor energy conversion efficiency, in part due to their intrinsic properties but also because of outdated yet still common industrial practices. Consequently, for this actuation technology, there are wide margins for improvement in terms of energy waste reduction and increased environmental sustainability. This paper, therefore, investigates new applications for a management and control method conceived by the authors to drastically and systematically reduce the energy consumption of hydraulic actuators. The method is easily retrofittable to existing plants, being based on the unconventional and non-invasive deployment of a continuous-control electrohydraulic valve (CCEV) to control the supply pressure, whose required value is estimated according to the instantaneous load demands. Through the simulation of several industrial processes characterized by process parameters of varying orders of magnitude, this paper demonstrates that this innovative use of a CCEV for supply pressure regulation is an effective and widely applicable solution for energy savings and CO₂ footprint reduction in production systems that rely on hydraulic servo axes.

Several types of self-balancing vehicles have been successfully developed and commercialized in the past two decades, both as manned vehicles and as autonomous mobile robots. At the same time, due to their characteristic instability and underactuation, a large body of research has been devoted to their control. However, despite this practical and theoretical interest, the current publicly available literature does not cover their systematic design and development. In particular, overall processes that lead to a finished vehicle starting from a set of requirements and specifications have not been examined in the literature. Within this context, this paper contributes a comprehensive mechatronic, dynamics-based procedure for the design of this class of vehicles; to promote clarity of exposition, the procedure is systematically presented using Model-Based Systems Engineering tools and principles. In particular, the proposed design method is developed and formalized starting from an original description of the vehicle, which is treated as a complex system composed of several interconnected multi-domain components that exchange power and logical flows through suitable interfaces. A key focus of this work is the analysis of these exchanges, with the goal of defining a minimal set of quantities that should be necessarily considered to properly design the vehicle. As a salient result, the design process is organized in a logical sequence of steps, each having well-defined inputs and outputs. The procedure is also graphically outlined using standardized formalisms. The design method is shown to cover all the mechanical, electrical, actuation, measurement and control components of the system, and to allow the unified treatment of a large variety of different vehicle variants. The procedure is then applied to a specific case study, with the goal of developing the detailed design of a full-scale vehicle. The main strengths of the proposed approach are then widely highlighted and discussed.

High-speed pick-and-place industrial applications often use parallel kinematic robots due to their high stiffness and dynamic performance; furthermore, the latter not only depends on the mechanical characteristics of the robots but also on the control algorithm. The literature shows several theoretical contributions to such controllers, mainly tested at the simulation level or on simple proof-of-concept laboratory equipment that execute low-speed and simple trajectories. This paper presents an experimental investigation of the dynamic performance of an industrial high-speed 4-DOF 5R parallel robot designed for pick-and-place applications on moving objects. The inverse dynamics control in the task space is used as a control algorithm. The results show the contribution of all the components of the control algorithm to the motor torque, and the inverse dynamics controller performances are discussed also in comparison to those achievable with simpler PD or PID controllers in a joint space. Moreover, the paper shows the controller synthesis from a modern mechatronic point of view, and the effectiveness of the proposed solution for the tracking of complex high-speed trajectories in an industrial application.

The ability to predict the maximal performance of an industrial robot executing non-deterministic tasks can improve process productivity through time-based planning and scheduling strategies. These strategies require the configuration and the comparison of a large number of tasks in real time for making a decision; therefore, an efficient task execution time estimation method is required. In this work, we propose the use of neural network models to approximate the task time function of a generic multi-DOF robot; the models are trained using data obtained from sophisticated motion planning algorithms that optimize the shape of the trajectory and the executed motion law, taking into account the kinematic and dynamic model of the robot. For scheduling purposes, we propose to evaluate only the neural network models, thus confining the online use of the motion planning software to the full definition of the actually scheduled task. The proposed neural network model presents a uniform interface and an implementation procedure that is easily adaptable to generic robots and tasks. The paper’s results show that the models are accurate and more efficient than the full planning pipeline, having evaluation times compatible with real-time process optimization.