Smart cities are complex, socio-technological systems built as a strongly connected System of Systems, whose functioning is driven by human–machine interactions and whose ultimate goals are the well-being of their inhabitants. Consequently, controlling a smart city is an objective that may be achieved by using a specific framework that integrates algorithmic control, intelligent control, cognitive control and especially human reasoning and communication. Among the many functions of a smart city, intelligent transportation is one of the most important, with specific restrictions and a high level of dynamics. This paper focuses on the application of a neuro-inspired control framework for urban traffic as a component of a complex system. It is a proof of concept for a systemic integrative approach to the global problem of smart city management and integrates a previously designed urban traffic control architecture (for the city of Bucharest) with the actual purpose of ensuring its proactivity by means of traffic flow prediction. Analyses of requirements and methods for prediction are performed in order to determine the best way for fulfilling the perception function of the architecture with respect to the traffic control problem definition. A parametric method and an AI-based method are discussed in order to predict the traffic flow, both in the short and long term, based on real data. A brief comparative analysis of the prediction performances is also presented.

The identification (and control) of nonlinear systems is one of the most important and actual research directions. Moreover, many systems are multivariable. Different from linear system identification (where only a few classes of models are available), in the case of nonlinear systems, the class set of models is quite diverse. One of the most appealing nonlinear models belongs to the nonlinear ARMAX (NARMAX) class. This article focusses on the identification of such a model, which can be compared with other models (such as nonlinear ARX (NARX) and linear ARMAX) in an application based on the didactical installation ASTANK2. The mathematical foundation of NARMAX models and their identification method are described at length within this article. One of the most interesting parts is concerned with the identification of optimal models not only in terms of numerical parameters but also as structure. A metaheuristic (namely, the Cuckoo Search Algorithm) is employed with the aim of finding the optimal structural indices based on a special cost function, referred to as fitness. In the end, the performances of all three models (NARMAX, NARX, and ARMAX) are compared after the identification of the ASTANK2 installation.

Smart control technologies that can manage the complexity of urban traffic while also reducing response times for emergency vehicles are necessary. This article proposes AETM (Adaptive and Equitable Traffic Management), an adaptive and equitable traffic management system that integrates contextual methods for handling emergencies with traffic light control based on reinforcement learning. The system uses Q-learning to optimize traffic light phases under normal traffic conditions and integrates a dedicated emergency vehicle module, which includes detection, dynamic rerouting and contextual preemption functions. The system adaptively optimizes traffic light phases under normal traffic conditions and integrates a specialized module for emergency vehicles, which ensures their detection, dynamic rerouting and contextual preemption. The priority level is evaluated through an auxiliary fuzzy mechanism, based on interpretable rules, which takes into account local conditions without influencing the learning process. The performance of the framework is evaluated in a microscopic simulation environment by comparing classical control, adaptive control, and the full AETM configuration. The results highlight significant reductions in travel times and stops for emergency vehicles while maintaining overall traffic stability.

In the modern optimization context, this paper introduces an optimal PID-based control strategy for a two-tank installation, namely ASTANK2. The process model was identified by using raw and spline smoothed measured data, respectively. Two PID controller configurations, a standard (regular) one (PID-R) and a non-standard one (PID-N), were considered for each type of model, resulting in four regulators. The optimal tuning parameters of each regulator were obtained by a searching approach relying on a combination of two metaheuristics. Firstly, an improved version of the Hill Climbing algorithm was employed to comprehensively explore the searching space, aiming to find fairly accurate tuning parameters. Secondly, an improved version of the Firefly Algorithm was proposed to intensively refine the search around the previously found optimal parameters. A comparative analysis between the four controllers was achieved in terms of performance and robustness. The simulation results showed that all optimal controllers yielded good performance in the presence of exogenous stochastic noise (bounded error tracking, setpoint tracking, reduced overshoot, short settling time). Robustness analysis is extensive and illustrates that the PID-R controllers are more robust to model uncertainties, whilst PID-N controllers are more robust to tracking staircase type references.