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The problem of asymptotic stabilization for a class of nonholonomic systems is studied and solved by means of a hybrid control law which makes use of a (deterministic) finite state machine. It is shown that, by using a simple switching control scheme, the origin is a globally asymptotically stable equilibrium in the sense of Lyapunov. The control law can take into account the presence of input saturation. Simulation results are reported showing the performance of the proposed control scheme.

The development of a hybrid control scheme for freeway systems is the objective of the paper. A finite number of models is defined, each relevant to a specific traffic condition. The system state variables are the differences between the traffic density and the traffic mean speed in each section and their desired values. A receding–horizon (RH) regulator is defined for each model being the control variables the traffic volumes at on–ramps. A hybrid control scheme is proposed, which is composed of two control levels. The first control level consists of a finite class of models and RH control functions. The second level acts as a supervisor that chooses the best model and control law to be applied to the plant according to the present system state and possible external events. The application of the proposed control scheme makes it possible to guarantee some suitable stability properties of the origin as an equilibrium point of the system considered at the first control level.

The paper deals with an adaptive observer methodology for estimating the parameters of an unknown sinusoidal signal from a measurement perturbed by structured and unstructured uncertainties. The proposed technique makes it possible to handle measurement signals affected by structured uncertainties like, for example, bias and drifts which are typically present in applications. The stability of the estimator with respect to bounded additive disturbances is addressed by Input-to-State Stability arguments. The effectiveness of the proposed technique is shown through numerical simulations where comparisons with some recently proposed algorithms are also provided.

A multiplexing structure is considered, where TDM frames are used to carry both isochronous, circuit-switched, and asynchronous, packet-switched traffic. Control functions are sought, whose task is that of deciding the allocation of the frame capacity between the two traffic types. The problem is defined in the context of Markov decision processes, and multilayer feedforward neural networks are used to approximate the optimal control laws. A backpropagation algorithm is described, which exploits the finiteness of the system's state. The procedure is conceived in the framework of repetitive (receding horizon) control schemes. Numerical results are presented, as well as comparisons with the control laws obtained by applying a dynamic programming algorithm.

An approximation procedure termed "extended Ritz method" is presented for the solution of functional optimization problems. The properties of powerful nonlinear approximators, such as neural networks, are exploited to face highly nonlinear optimization problems in high-dimensional settings, with the possibility of avoiding the so-called "curse of dimensionality." As an example, a nonlinear control problem involving several tens of state variables is faced.

The present paper concerns the design of approximate off-line model predictive control laws for nonlinear discrete-time systems subject to hard constraints on state and input variables. The possibility to obtain an approximate receding horizon control law by performing off-line optimization, leads to a dramatic reduction of the real-time computational complexity with respect to on-line algorithms, and allows the application of the developed control technique to plants with fast dynamics, that require small sampling periods. The main feature of the proposed approximation scheme consists in the possibility to cope with possibly discontinuous state-feedback control laws, while guaranteeing the fulfillment of hard constraints on state and input variables despite the perturbations due to the use of an approximate controller. Finally, the resulting closed-loop system is shown to be input-to-state-stable with respect to the approximation-induced perturbations.

In this work, the off-line approximation of state- feedback nonlinear model predictive control laws by means of smooth functions of the state is addressed. The idea is to investigate how the approximation errors affect the stability of the closed-loop system, in order to derive suitable bounds which have to be fulfilled by the approximating function. This analysis allows to conveniently set up the characteristic parameters of some techniques such as Neural Networks which can be used to implement the control law, in order to render the system Input-to-State Practically Stable while satisfying, in addition, hard constraints on the trajectories; both the amount of data storage and the computational time result strongly reduced with respect to Nearest Neighbor or Set Membership approaches, which have been recently proposed to obtain effective off-line approximation of nonlinear MPC. The provided simulations confirm the validity of the method.