No abstract

No abstract

The deÞning characteristic of a networked control system (NCS) is having a feedback loop that passes through a local area computer network. Our two-step design approach includes using standard control methodologies and choosing the network protocol and bandwidth in order to ensure important closed-loop properties are preserved when a computer network is inserted into the feedback loop. For sufficiently high data rates, global exponential stability is preserved. Simulations are included to demonstrate the theoretical result.

In many applications of multi-agent systems (MAS), a set of leader agents acts as a control input to the remaining follower agents. In this paper, we introduce an analytical approach to selecting leader agents in order to minimize the total mean-square error of the follower agent states from their desired value in steady-state in the presence of noisy communication links. We show that the problem of choosing leaders in order to minimize this error can be solved using supermodular optimization techniques, leading to efficient algorithms that are within a provable bound of the optimum. We formulate two leader selection problems within our framework, namely the problem of choosing a fixed number of leaders to minimize the error, as well as the problem of choosing the minimum number of leaders to achieve a tolerated level of error. We study both leader selection criteria for different scenarios, including MAS with static topologies, topologies experiencing random link or node failures, switching topologies, and topologies that vary arbitrarily in time due to node mobility. In addition to providing provable bounds for all these cases, simulation results demonstrate that our approach outperforms other leader selection methods, such as node degree-based and random selection methods, and provides comparable performance to current state of the art algorithms.

In a leader-follower multi-agent system (MAS), the leader agents act as control inputs and influence the states of the remaining follower agents. The rate at which the follower agents converge to their desired states, as well as the errors in the follower agent states prior to convergence, are determined by the choice of leader agents. In this paper, we study leader selection in order to minimize convergence errors experienced by the follower agents, which we define as a norm of the distance between the follower agents' intermediate states and the convex hull of the leader agent states. By introducing a novel connection to random walks on the network graph, we show that the convergence error has an inherent supermodular structure as a function of the leader set. Supermodularity enables development of efficient discrete optimization algorithms that directly approximate the optimal leader set, provide provable performance guarantees, and do not rely on continuous relaxations. We formulate two leader selection problems within the supermodular optimization framework, namely, the problem of selecting a fixed number of leader agents in order to minimize the convergence error, as well as the problem of selecting the minimum-size set of leader agents to achieve a given bound on the convergence error. We introduce algorithms for approximating the optimal solution to both problems in static networks, dynamic networks with known topology distributions, and dynamic networks with unknown and unpredictable

In this article we steer wheeled nonholonomic systems that can be represented in a so-called chained form. Sufficient conditions for converting a multiple-input system with nonholonomic velocity constraints into a multiple-chain, single-generator chained form via state feedback and a coordinate transformation are presented along with sinusoidal and polynomial control algorithms to steer such systems. Our example is the three-input nonholonomic system of a fire truck, or tiller truck. In this three-axle system, the control inputs are the steering velocities of both the first and third (or tiller) axles and the forward driving velocity of the truck. Simulation results are given for parallel parking, left hand turning, right hand turning, and changing lanes. Comparison is made to the same vehicle without tiller steering.

In this paper we address the problem of physical node capture attacks in wireless sensor networks and provide a control theoretic framework to model physical node capture, cloned node detection and revocation of compromised nodes. By combining probabilistic analysis of logical key graphs and linear control theory, we derive a dynamical model that efficiently describes network behavior under attack. Using LQR and LQG optimal control theory tools, we develop a network response strategy, which guarantees secure network connectivity and stability under attack. Detailed simulations are presented to validate the methodology.

No abstract