A well-organized platoon control may have advantages in terms of increasing highway capacity and decreasing fuel consumption and emissions. The paper proposes the design of the velocity of platoon. In the velocity design several factors must be taken into consideration such as fuel consumption, road inclinations, emissions and traveling time. Since the vehicles in a heterogenous platoon have different dynamic abilities, emission parameters, and fuel characteristics, the design task leads to a multi-criteria optimization problem. The design of the platoon control is based on the robust Hinf control method. The method is illustrated through simulation examples.

 This paper deals with state estimation of Powered Two Wheeled (PTW) vehicle and robust reconstruction of related unknown inputs. For this purpose, we consider a Proportional two Integral (P2I) observer. Based on LMI technique, we recover vehicle states and part of its dynamics (rider action) within steering angle disturbances and system uncertainties.

To prove the effectiveness and the robustness of the proposed observer, two different scenarios are considered: first we observe the system with disturbances without system's uncertainties. The second case is more general as the observer synthesis is conducted in the presence of steering angle disturbances/system's uncertainties. Both the state and the rider action, on the handlebar, are recovered in the two cases.

 Object classification is a widely researched area in the field of computer vision. Lately there has been a lot of attention to appearance based models for representing objects. The most important feature of classifying objects such as pedestrians, vehicles, etc. in traffic scenes is that we have motion information available to us. The motion information presents itself in the form of temporal cues such as velocity and also as spatio-temporal cues such as optical flow, DHOG (Chen et al.), etc. We propose a novel spatio-temporal feature based on covariance descriptors known as DCOV which captures complementary information to the DHOG feature. We present a combined classifier based on properties of tracked objects along with the DHOG and the DCOV features. We show based on experiments on the PETS 2001 dataset and two video sequences of bicycle and pedestrian traffic that the proposed classifier provides competent performance for distinguishing pedestrians, vehicles and bicyclists. Our method is also adaptive and benefits from the availability of more data for training. The classifier is also developed with real-time feasibility in mind.

  This paper deals with the design of a temperature controller for a greenhouse using a wood pellet ]heating system. This heating system is carbon neutral; however it has three undesirable characteristics in control: 1) an On/Off control signal, 2) a longer time constant and 3) constraints on operation. The last property, in particular, is essential for achieving better fuel consumption and for achieving a longer lifetime of the heater. However the conventional PWM control also gives good control performance, the properties 1) and 2) block its effectiveness. In addition, the property 3) leads to performance degradation in the designed controller. To compensate for these effects, in this paper the model predictive control is applied to achieve the temperature control. The required specification is to keep the temperature error within 2 degrees Celsius in peak-to-peak with sufficient robustness for the parameter change in greenhouse. Experimental results in greenhouse give the effectiveness of proposed controller. Moreover the idea for computational load relaxation is also discussed.

 This paper presents a constrained Model Predictive Control (MPC) strategy enriched with soft-control techniques as neural networks and fuzzy logic, to incorporate self-tuning capabilities and reliability aspects for drinking water transport network management. The system is a multilevel controller with three hierarchical layers: neural level, fuzzy level and MPC level. Results in the Barcelona Water Network have shown that the quasi-explicit nature of the proposed predictive controller leads to improve the computational time, especially when the complexity of the problem structure can vary while tuning the receding horizons.

 This paper considers optimal control of a system of nonholonomic robots. Control effort and deviation from a desired formation are minimized for the system of robots traveling between specified initial and final configurations with a bifurcation parameter which is the relative weighting assigned to the control effort versus the formation constraint. The main contribution of this paper is an extension of our previous work which considered the nature of multiple solutions to an optimal control problem for a distributed system of multiple holonomic agents. An important aspect of the previous work was that symmetries in the holonomic system guaranteed symmetries in the bifurcations of the solutions to the optimization problem. In the current work, a system of relatively simple nonholonomic robots break the symmetry in the system, which results in symmetry-breaking in the bifurcations.