Optimal codesign of industrial networked control systems with state‐dependent correlated fading channels

Abstract

Summary This paper examines a codesign problem in industrial networked control systems (NCS) whereby physical systems are controlled over wireless fading channels. The considered wireless channels are assumed to be stochastically dependent on the physical states of moving machineries in the industrial working space. In this paper, the moving machineries are modeled as Markov decision processes whereas the characteristics of the correlated fading channels are modeled as a binary random process whose probability measure depends on both the physical states of moving machineries and the transmission power of communication channels. Under such a state‐dependent fading channel model, sufficient conditions to ensure the stochastic safety of the NCS are first derived. Using the derived safety conditions, a codesign problem is then formulated as a constrained joint optimization problem that seeks for optimal control and transmission power policies which simultaneously minimize an infinite time cost on both communication resource and control effort. This paper shows that such optimal policies can be obtained in a computationally efficient manner using convex programming methods. Simulation results of an autonomous forklift truck and a networked DC motor system are presented to illustrate the advantage and efficacy of the proposed codesign framework for industrial NCS.

Co-design of Optimal Transmission Power and Controller for Networked Control Systems Under State-dependent Markovian Channels

“…Existing results may thus be recovered from our MATI bounds by setting the parameter λ to be independent of the channel conditions. Second, the MATI results in this paper extend our prior works in [11], [40] by considering a less conservative assumption on the system structure and a more general SD-MC model. Theorem 4.5: Suppose conditions ( 17) and ( 18) hold for the stochastic hybrid system in ( 5)- (6).…”

“…The MATI bounds shown in (17) are functions of parameters ξ and L defined in Assumption 4.1, and λ that depends on the parameters of the SD-MC model in (7). The proposed MATI bounds differ from the existing results in [11], [33], [40], [41] in two aspects. First, the MATI bounds in (17) generalizes the results in [33], [41] as they take into account the impacts that the stochastic communication channel has on the MATI.…”

“…As a matter of fact, one may show that a system is ASAS if it is exponentially stable in expectation, i.e. there exists an exponential function (10) holds [11], [40].…”

“…Following the proof of Theorem 9 in [11], one can shows that ESE implies ASAS by using the Borel-Cantelli Lemma. Please refer to [11], [40] for more details of the proof.…”

Predictive control compensation for networked control system with time-delay

“…Some random control theories are used to compensate for the time-delay. These compensation strategies include robust control, 14,15 neural network, [16][17][18] fuzzy control, 19,20 Markov jump system method, 21,22 state feedback control, 23,24 switching control method, 25 stochastic optimal control, 26,27 and other time-delay compensation methods. These compensation methods have achieved some progress.…”