Abstract
In this work, the feasibility of applying a Sliding Mode Predictive Controller (SMPC) to improve greenhouse inside air temperature control is addressed in terms of energy consumption, disturbance handling and set point tracking accuracy. Major research issues addressed concern the SMPC robustness study in greenhouse control, as well as to evaluate if the levels of performance and energy consumptions are acceptable when compared with the traditional generalized predictive controller. Besides the external disturbances related to weather conditions throughout the considered period, such as solar radiation and temperature variations, internal effects of irrigation system and external air flow entering the greenhouse must be taken into account. Simulations based on real data, carried out for a period of 4months, suggest that the strategy herein described not only appropriately rejects these disturbances, but also keeps the manipulated variables (heating and cooling) within feasible practical limits, with low levels of energy consumption, motivating its refinement for real application. SMPC results are presented and compared with the ones obtained with the generalized predictive controller. Both controllers are subject to actuator constraints and employ the Quadratic Programming for optimization. Copyright (c) 2015 John Wiley & Sons, Ltd.

Abstract
This paper presents the state-of-the-art in terms of automation and control for protected cultivation in greenhouses. Aspects such as modeling, instrumentation, energy optimization and applied robotics are considered, aiming at not only to identify latest research topics, but also to foster continuous improvement in key cutting-edge problems. © Springer International Publishing Switzerland 2017.

Abstract
Precision agriculture is gaining an increasing interest in the current farming paradigm. This new production concept relies on the use of information technology (IT) to provide a control and supervising structure that can lead to better management policies. In this framework, imaging techniques that provide visual information over the farming area play an important role in production status monitoring. As such, accurate representation of the gathered production images is amajor concern, especially if those images are used in detection and classification tasks. Real scenes, observed in natural environment, present high dynamic ranges that cannot be represented by the common LDR (Low Dynamic Range) devices. However, this issue can be handled by High Dynamic Range (HDR) images since they have the ability to store luminance information similarly to the human visual system. In order to prove their advantage in image processing, a comparative analysis between LDR and HDR images, for fruits detection and counting, was carried out. The obtained results show that the use of HDR images improves the detection performance to more than 30% when compared to LDR.

Abstract
This paper addresses a strategy to improve disturbance rejection for the Sliding Mode Controller designed in a Smith Predictor scheme (SMC-SP), with its parameters tuned through the bio-inspired search algorithm—Particle Swarm Optimization (PSO). Conventional SMC-SP is commonly based on tuning equations derived from step response identification, when First Order Plus Dead Time models (FOPDT) are considered and therefore controller parameters are previously set. Online PSO tuning based on minimization of the Integral of Time Absolute Error (ITAE) can provide faster recovery from external disturbances without significant increase of energy consumption, and the Sliding Mode feature deals with possible model mismatch. Simulation results for time delayed systems corroborating these benefits are presented. © Springer International Publishing Switzerland 2017.

Abstract
Posicast feedback control systems are very sensitive to model uncertainty. This paper proposes the use of Particle Swarm Optimization (PSO) to auto-tune two-degrees of freedom control systems. The system considers as a pre-filter a half-cycle Posicast command shaper and a PID controller in the feedback loop. A model reference technique is proposed to track differences among model and system to be controlled, feeding a decision block which will trigger an auto-tuning optimization mechanism. Preliminary simulation results are presented showing the proposed technique effectiveness to deal with prescribed plant uncertainties.

Abstract
The use of rigid robot manipulators with good performance in industrial applications demands a proper robust and optimized control technique. Several works have proven the efficient use of metaheuristics optimization algorithms to work with complex problems in the robotic area. In this work, it is proposed the use of Grey Wolf Optimizer (GWO) with chaotic basis to optimize the parameters of a robust Higher Order Sliding Modes (HOSM) controller for the position control in joint space of a rigid robot manipulator. A total of seven test cases were considered varying the chosen chaotic map, face to the original GWO and the general repeatability of such algorithm is improved using chaotic versions. Also, two cost functions were tested within the HOSM optimization. Simulation results suggest that both algorithm and cost function formulations influence the chaotic map choice. In fact, the chattering problem, presented by HOSM controllers, is reduced when the cost function attempts to minimize the total variation of the control signal.