178,269 research outputs found
Minimum Violation Control Synthesis on Cyber-Physical Systems under Attacks
Cyber-physical systems are conducting increasingly complex tasks, which are
often modeled using formal languages such as temporal logic. The system's
ability to perform the required tasks can be curtailed by malicious adversaries
that mount intelligent attacks. At present, however, synthesis in the presence
of such attacks has received limited research attention. In particular, the
problem of synthesizing a controller when the required specifications cannot be
satisfied completely due to adversarial attacks has not been studied. In this
paper, we focus on the minimum violation control synthesis problem under linear
temporal logic constraints of a stochastic finite state discrete-time system
with the presence of an adversary. A minimum violation control strategy is one
that satisfies the most important tasks defined by the user while violating the
less important ones. We model the interaction between the controller and
adversary using a concurrent Stackelberg game and present a nonlinear
programming problem to formulate and solve for the optimal control policy. To
reduce the computation effort, we develop a heuristic algorithm that solves the
problem efficiently and demonstrate our proposed approach using a numerical
case study
Solution of Linear Programming Problems using a Neural Network with Non-Linear Feedback
This paper presents a recurrent neural circuit for solving linear programming problems. The objective is to minimize a linear cost function subject to linear constraints. The proposed circuit employs non-linear feedback, in the form of unipolar comparators, to introduce transcendental terms in the energy function ensuring fast convergence to the solution. The proof of validity of the energy function is also provided. The hardware complexity of the proposed circuit compares favorably with other proposed circuits for the same task. PSPICE simulation results are presented for a chosen optimization problem and are found to agree with the algebraic solution. Hardware test results for a 2âvariable problem further serve to strengthen the proposed theory
An Efficient Policy Iteration Algorithm for Dynamic Programming Equations
We present an accelerated algorithm for the solution of static
Hamilton-Jacobi-Bellman equations related to optimal control problems. Our
scheme is based on a classic policy iteration procedure, which is known to have
superlinear convergence in many relevant cases provided the initial guess is
sufficiently close to the solution. In many cases, this limitation degenerates
into a behavior similar to a value iteration method, with an increased
computation time. The new scheme circumvents this problem by combining the
advantages of both algorithms with an efficient coupling. The method starts
with a value iteration phase and then switches to a policy iteration procedure
when a certain error threshold is reached. A delicate point is to determine
this threshold in order to avoid cumbersome computation with the value
iteration and, at the same time, to be reasonably sure that the policy
iteration method will finally converge to the optimal solution. We analyze the
methods and efficient coupling in a number of examples in dimension two, three
and four illustrating its properties
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