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This paper presents a model predictive control (MPC)-based algorithm for rollover prevention of an autonomous electric road sweeper (AERS). For AERS, the basic function of autonomous driving is a path- and velocity-tracking control needed to make a vehicle follow given path and velocity profiles. On the other, the AERS adopts an articulated frame steering (AFS) mechanism which can make cornering behavior agile. Moreover, the tread of the AERS is narrow, and the height of the mass center is high. As a result, it is prone to roll over. For this reason, it is necessary to design a controller for path and velocity tracking and rollover prevention in order to improve maneuverability and roll safety of the AERS. A kinematic model was adopted as a vehicle one for the AERS. With the vehicle model, reference states of position and velocity were determined that are needed to make the AERS track the reference path and prevent rollover. With the vehicle model and reference states, an MPC-based motion controller was designed to optimize articulation angle and velocity commands. The load-transfer ratio (LTR) was used to measure a rollover propensity. To evaluate the proposed algorithm, a simulation was conducted for the U-turn scenario. Simulation results show that the proposed algorithm improves path tracking and prevents the rollover of the AERS.

This work presents an innovative control architecture, which takes its ideas from the theory of adaptive control techniques and the theory of statistical learning at the same time. Taking inspiration from the architecture of a classical neural network with several hidden levels, the principle is to divide the architecture of the adaptive controller into three different levels. Each level implements an algorithm based on learning from data and therefore we can talk about learning concepts. Each level has a different task: the first to learn the required reference to the control loop; the second to learn the coefficients of the state representation of a model of the system to be controlled; and finally, the third to learn the coefficients of the state representation of the actual controller. The design of the control system is reported from both a rigorous and an operational point of view. As an application example, the proposed control technique is applied on a second-order non-linear system. We consider a servo-drive based on a brushless DC (BLDC) motor, whose dynamic model considers all the non-linear effects related to the electromechanical nature of the electric machine itself, and also an accurate model of the switching power converter. The reported example shows the capability of the control algorithm to ensure trajectory tracking while allowing for disturbance rejection with different disturbance signal amplitude. The implementation complexity analysis of the new controller is also proposed, showing its low overhead vs. basic control solutions.

AbstractCompanies are forced by rising energy costs to seize control of their energy consumption to maintain contestability. There-fore, transparency over the companies’ energy flux along the production process is required. Energy data management software is helpful, but cost-intensive. Hence, especially small and medium sized enterprises (SME) spare this investment. In this paper, the requirements for an energy controlling infrastructure in SME are elaborated, followed by a deduced software-architecture which supports the respective controlling structure. Further, the prototypical realization of the corresponding tool “Green-Cockpit” will be presented. The free of cost, open source and web-based tool is designed to help companies monitor, interpret, analyze, plan and report their energy consumption. The Green Cockpit tool outperforms other energy management software at management disciplines with its ability to not only analyze energy consumption, but plan and control it additionally.

This paper presents a careful study related to a more efficient energy management between lithium battery (with rated voltage of 24V) and Ultracapacitors (UC) for Hybrid Electric Vehicle (HEV) applications. This association is due to the present trend in the field, knowing that the major drawback of the HEV is the autonomy problem. Thus, using the Hybrid energy source (such as UC + battery) and with a good energy management improves the HEV performances. In this paper, battery and Ultracapacitors (10 cells of 2.7V in series) are coupled to DC-bus using two buck-boost converters. The main contribution of this paper is focused on DC-bus voltage and currents control strategy based on polynomial (RST) controller. Through some simulations in MATLAB/Simulink software and experimental results, the authors present an improved energy management for HEV.

This paper studies a closed loop linear control system. The sensor computes a state estimate and sends it to the controller/actuator in the receiver block over a randomly fading packet dropping link. The receiver sends an ACK/NACK packet to the transmitter over a link. It is assumed that the transmission energy per packet at the sensor depletes a battery of limited capacity, replenished by an energy harvester. The objective is to design an optimal energy allocation policy and an optimal control policy so that a finite horizon LQG control cost is minimized. It is shown that in case the receiver to sensor feedback channel is free of errors, a separation principle holds. Hence, the optimal LQG controller is linear, the Kalman filter is optimal and the optimal energy allocation policy is obtained via solving a backward dynamic programming equation. In case the feedback channel is erroneous, the separation principle does not hold. In this case, we propose a suboptimal policy where the controller still uses a linear control, and the transmitter minimizes an expected sum of the trace of an “estimated” receiver state estimation error covariance matrix. Simulations are used to illustrate the relative performance of the proposed algorithms and various heuristic algorithms for both the perfect and imperfect feedback cases. It is seen that the dynamic programming based policies outperform the simple heuristic policies by a margin.

This paper focuses on the specific growth rate estimation problem in a Polyhydroxybutyrate bioplastic production process by industrial fermentation. The kinetics of the process are unknown and there are uncertainties in the model parameters and inputs. During the first hours of the growth phase of the process, biomass concentration can be measured online by an optical density sensor, but as cell density increases this method becomes ineffective and biomass measurement is lost. An asymptotic observer is developed to estimate the growth rate for the case without biomass measurement based on corrections made by a pH control loop. Furthermore, an exponential observer based on the biomass measurement is developed to estimate the growth rate during the first hours, which gives the initial condition to the asymptotic observer. Error bounds and robustness to uncertainties in the models and in the inputs are found. The estimation is independent of the kinetic models of the microorganism. The characteristic features of the observer are illustrated by numerical simulations and validated by experimental results.