Continuous soft robots are becoming more and more widespread in applications, due to their increased safety and flexibility in critical applications. The possibility of having soft robots that are able to change their stiffness in selected parts can help in situations where higher forces need to be applied. This paper describes a theoretical framework for learning the desired stiffness characte...

In this paper, we consider the efficient and reliable solution of distributed optimal control problems governed by parametrized elliptic partial differential equations. The reduced basis method is used as a low-dimensional surrogate model to solve the optimal control problem. To this end, we introduce reduced basis spaces not only for the state and adjoint variable but also for the distributed ...

This contribution focuses on the important stability issue of ABR traac control with explicit-rate schemes. We develop a novel design approach with three advantages. First, the ABR rate is only adapted to the low-frequency variation of the underlying VBR traac, which eeectively prevents network congestion with much improved control stability. Second, a linear dynamic model is presented which ma...

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. A new adapt...

The subject of this paper is the design and implementation of a robust dynamic feedback controller, based on the dynamic model of the four-wheel skidsteering RobuFAST A robot, undergoing high-speed turns. The control inputs are respectively the linear velocity and the yaw angle. The main objective of this paper is to formulate a sliding mode controller, robust enough to obviate the knowledge of...

We address the task of inferring the future actions of people from noisy visual input. We denote this task activity forecasting. To achieve accurate activity forecasting, our approach models the effect of the physical environment on the choice of human actions. This is accomplished by the use of state-of-the-art semantic scene understanding combined with ideas from optimal control theory. Our u...

We consider an Ito stochastic differential equation with delay, driven by brownian motion, whose solution, by an appropriate reformulation, defines a Markov process X with values in a space of continuous functions C, with generator L. We then consider a backward stochastic differential equation depending on X , with unknown processes (Y, Z), and we study properties of the resulting system, in p...

The proposed control method is based on Hirschorn's Algorithm extended with Silverman's structure algorithm, and facilitates easy determination of the control law using the relationship, between the output and the input, which is obtained by differentiating the output. This method is the extension of the linear model matching control system for which the control law was proposed by Wolovich. Th...

Since the number of compromised computers, or botnet, continues to grow, the cyber security problem has become increasingly important and challenging to both academic researchers and industry practitioners. A respect to combat botnet propagation is to understand the attacker’s behaviors based on the whole operation of a system that can be modeled with population models used in epidemiological s...

This paper presents a non-linear optimal control strategy for the operational management of water distribution networks (WDNs) including both flow and hydraulic head/pressure constraints. The optimal operation of WDNs should guarantee water supply with suitable pressures at all the demand nodes in the network. The challenge for non-linear model predictive control in this context is to compute c...