Jorge L. Piovesan

Randomized Receding Horizon Navigation

The note combines (weak) control Lyapunov function-based nonlinear receding horizon control, with randomized optimization. This approach is applied to the problem of robot navigation in the presence of state and input constraints. It is shown that under certain conditions, relaxing the definiteness requirements on the terminal cost function allows one to select control inputs through a Monte-Carlo optimization scheme in a way that preserves the stability and convergence properties of the closed loop system. While the particular randomized optimization scheme used here can be substituted for the nonlinear optimal control method of choice, the introduction of randomization in receding horizon optimization is anticipated to offer additional trade-offs between performance and computation speed compared to the fixed-overhead nonlinear optimal control strategies typically employed.

CLF-Based Control Design for Unknown Multiinput Nonlinear Systems With Good Transient Performance

This technical note tackles the problem of constructing state-feedback stabilizers that guarantee good transient closed-loop performance when applied to general unknown nonlinear multiinput state-feedback stabilizable systems. An adaptive Control Lyapunov Function-based control scheme is proposed in order to address this problem. Mathematical analysis establishes that the proposed control scheme guarantees good closed-loop transient performance outside the regions where the system is uncontrollable, provided the controlled system admits a controllability-like property.

Practical issues in networked control systems

In this paper we expose experimental issues faced in a closed-loop networked control system. We also propose some compensation actions, and evaluate their performance for different experimental setups, focusing specifically on time delays

Randomized model predictive control for robot navigation

The paper suggests a new approach to navigation of mobile robots, based on nonlinear model predictive control and using a navigation function as a control Lyapunov function. In this approach, the nonlinear optimal control problem is treated using randomized algorithms. The advantage of the proposed combination of navigation functions for robot motion planning with randomized algorithms within an MPC framework, is that the control design offers stability by design, is platform independent, and allows the designer to trade-off performance for (computation) speed, according to the application requirements.

Discrete Asymptotic Abstractions of Hybrid Systems

In this paper we introduce the notion of finite time mode abstraction to relate a hybrid automaton to a timed automaton that preserves the stability and reachability properties of the former. The abstraction procedure discards the continuous dynamics of each mode in the hybrid automaton completely, keeping only the information about the maximum time in which the continuous state makes a discrete jump. This information is used to construct a timed automaton, based on the original hybrid automaton, and to prove that the stability and reachability properties of the original system are retained in the abstract timed automaton. In the process of abstracting a hybrid to a timed automaton we introduce a new notion of hybrid distance metric, which provides information about both the number of discrete transitions that a system would have to make to go from one hybrid state to another, and the distance between the continuous parts of such hybrid states

Finite abstractions for hybrid systems with stable continuous dynamics

This paper outlines an abstraction process in which a particular class of hybrid automata with continuous dynamics that have parameterized positive limit sets, are being abstracted into finite transition systems. The limit sets with their corresponding attraction regions define pre- and post-conditions for the continuous dynamics, and determine the transitions in the discrete abstraction. An observable (weak) bisimulation equivalence is established between the two models. The abstraction process described can find application in verification, as well as in planning and symbolic control.

Preliminary results on interconnected hybrid systems

We present a new framework for describing multi-agent systems with hybrid individual dynamics where the interaction between agents occurs at both the continuous and discrete levels. We formally define these multi-agent systems as interconnected hybrid systems and then recast fundamental hybrid concepts such as a hybrid metric, hybrid execution, and reachability in this new interconnected hybrid systems framework. We then prove a necessary condition for the existence of the interconnected hybrid executions. This work extends results in [10], [16].

Statistical learning controller for the energy management in a fuel cell electric vehicle

This paper considers a high efficiency energy management control strategy for a hybrid fuel cell vehicle. The proposed switching architecture consists of a bank of neural network based controllers designed using statistical learning theory. The use of different power sources and the presence of different constraints make the power management problem highly nonlinear. Probabilistic and statistical learning methods are used to design the weights of a neural network and the switching strategy is used to implement different controllers designed on the considered operative conditions. The proposed controller increases the efficiency of the whole system and reduces the fuel consumption during a given path. Numerical results are obtained using the model of a real hybrid car, ¿smile¿ developed by FAAM, using a stack of fuel cells as the primary power source in addition to ultracapacitors and a lithium battery pack. The results are compared with those of a single neural network based controller and the performance is shown to be satisfactory in terms of fuel consumption and the efficiency of the whole system.