In the last two decades an intense shift from advanced mechatronic systems to Cyber-Physical Systems (CPS) is taking place. CPS will play an important role in the field of precision agriculture and it is expected to improve productivity in order to feed the world and prevent starvation. In order to expedite and accelerate the realization of CPS in the field of precision agriculture it is necessary to develop methods, tools, hardware and software components based upon transdisciplinary approaches, along with validation of the principles via prototypes and test beds. In this context this paper presents a precision agricultural management integrated system architecture based on CPS design technology

The paper proposes a novel data-driven approximation kinematic (DAK) model to estimate the shape and opening level of a PneuNets soft gripper in relation to the applied pressure signal. The model offers suitable capabilities for implementing in real-time applications involving soft grasping planning and size recognition of fragile objects with different sizes and shapes. The proposed DAK model estimates the free bending behavior of a PneuNets actuator (soft gripper finger) based on a set of approximation functions derived from experimental data and an equivalent serial mechanism that mimics the shape of the actuator. The model was tested for a commercial PneuNets actuator with decreasing chamber height, produced by SoftGripping Co. (Hamburg, Germany). The model validation is accomplished through a set of experiments, where the shape and elementary displacements were measured using a digital image processing technique. The experimental data and the estimated data from the DAK model were compared and analyzed, respectively. The proposed approach has applicability in sensorless/self-sensing bending control algorithms of PneuNets actuators and in soft grasping applications where the robotic system must estimate the opening level of the gripper in order to be able to accomplish its task.

The objective of this paper is to develop a simulation tool for analysing the thermodynamic behaviour of multi-room residential buildings using multi-physics description software Simscape. The developed model is intended to be use for testing and optimization experiments in the design phase of building heating control systems. The proposed approach is tested by implementing in Matlab/Simulink the model of a building with 4 rooms. The obtained results and the advantages of the proposed method are analyzed and presented in the paper

The paper proposes a novel approach for shape sensing of hyper-redundant robots based on an AHRS IMU sensor network embedded into the structure of the robot. The proposed approach uses the data from the sensor network to directly calculate the kinematic parameters of the robot in modules operational space reducing thus the computational time and facilitating implementation of advanced real-time feedback system for shape sensing. In the paper the method is applied for shape sensing and pose estimation of an articulated joint-based hyper-redundant robot with identical 2-DoF modules serially connected. Using a testing method based on HIL techniques the authors validate the computed kinematic model and the computed shape of the robot prototype. A second testing method is used to validate the end effector pose using an external sensory system. The experimental results obtained demonstrate the feasibility of using this type of sensor network and the effectiveness of the proposed shape sensing approach for hyper-redundant robots.

Model based predictive control is a class of computer algorithms that explicitly use a process model to predict future plant outputs and compute an appropriate control action through on-line optimization of a cost objective over a future horizon, subject to various constraints. The cost function is defined in terms of the tracking error (the difference between the predicted output and set-point). Using this scheme, many different MBPC algorithms have been proposed in the literature. This paper presents an adaptive-predictive control algorithm, which uses on-line simulation and rule-based control. The algorithm is applied to an electrode position system of an electric arc furnace. Electric arc furnaces are commonly used in steelmaking and in smelting of nonferrous metals. To obtain the electric arc, usually there are used three graphite electrodes. The power level depends by the positions of the electrodes. As a result, the realization of a competitive control system is very important because it led to reduction of the energy consumption, pollution, and increases the safety of the process.