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To deal with the uncertainty problems by using Dempster-Shafer (D-S) evidence theory, the reliability values of the various propositions usually need to be transformed into the basic probabilities, which facilitates a correct decision-making. However, some probability transformation algorithms may not make full use of the prior information and bring about risks of the wrong decision due to the inaccurate probability transformation. Thus, this paper proposes an algorithm of the probability transformation based on the Assignable Certainty, in which different transformation rules are respectively presented to handle two cases of all existence/partial existence of single proposition. Numerical experiments demonstrate that the proposed algorithm is more objective and accelerates the convergence process. It not only improves the accuracy of decision-making, but also reduces the risk of decision-making.

Multi-sensor systems are able to obtain various measurement data, but their accuracy and reliability are difficult to be guaranteed, thus the decision-makings using these data are likely contrary to the facts. In view of this, an approach to multi-sensor decision fusion based on improved Jousselme evidence distance is proposed in the framework of D-S evidence theory. By rationally dividing the similarity Jaccard coefficient matrix, the evidences about conflicted sensor node are described accurately and their weights are reallocated by correction. This facilitates the final decision fusion. Numerical experimental results demonstrate that the proposed decision fusion approach based on the improved Jousselme distance achieves better performance than some existed approaches and largely reduces the uncertainty of the fused decision. To sum up, our approach not only recognizes the evidence about conflicted sensor node rapidly, but also has less risk of decision-makings.

This paper is concerned with local stabilization for unstable bilinear systems with input saturation. Given a prespecified polytope \(\mathcal{P}\) of the state space containing the zero equilibrium point, a linear state feedback is designed to guarantee that the closed-loop system is asymptotically stable in \(\mathcal{P}\) and \(\mathcal{P}\) is enclosed in the domain of attraction of the zero equilibrium point. Such sufficient conditions are derived via linear matrix inequalities (LMIs). Finally, an example illustrates the effectiveness of the proposed method.

We consider the robust stabilization problem for discrete-time Takagi—Sugeno (T—S) fuzzy systems with time-varied delays subjected to input saturation. We design static and dynamic anti-windup fuzzy controllers to ensure the convergence of all admissible initial states within the domain of attraction. Based on the project lemma and classical sector conditions, the conditions for the existence of solutions to this problem are obtained and expressed in terms of a set of linear matrix inequalities. Finally, a numerical example is provided to illustrate the effectiveness of the proposed design approach.