The issue of event-triggered guaranteed cost control (GCC) for networked T-S fuzzy switched systems in the sense of finite-time is addressed in this article. A unified event-based augmented fuzzy switched systems under the denial of service (DoS) attacks is formulated, which leads to the system switching more complex. Using a controller-mode Lyapunov functional approach, sufficient conditions are developed to guarantee the finite-time boundedness (FTB) of the system. Then, a criterion is also proposed to obtain the triggering parameters and controller gains simultaneously. Finally, simulation examples demonstrated the validity of the approach.
This paper presents a novel algorithm for industrial robot trajectory planning based on the NURBS(Non-Uniform Rational B-Spline) curve and Slerp interpolation aiming at the problems that the trajectory of a six-axis industrial robot is not smooth enough in the operation process, the posture planning process is non-uniform, and the six-axis industrial robot starts and stops frequently. Firstly, aiming at the first problem, the trajectory planning algorithm based on the NURBS curve is presented to improve the smoothness of the trajectory curve. Combined with Slerp posture planning based on quaternion description, which realizes the uniform change of posture on the robot’s end-effector. Secondly, aiming at the second problem, the S-velocity planning algorithm is presented in the interpolation interval of the robot, which realizes the operation process of complex curves continuously, and improves the operation quality. Finally, this paper uses Bernoulli’s lemniscate as the incentive trajectory, and the contrast experiment of trajectory planning between two incentive profiles is designed, which are the NURBS curve and the five-order polynomial curve. The result of the experiment indicates that the planning algorithm proposed in this paper could effectively improve the smoothness of trajectory in a Cartesian workspace, decrease the impact and tremulous in a Cartesian workspace, and effectively improve the performance of the robot working process. The results drawn from this paper lay a certain foundation for the future high-precision control of industrial robots.
The bionic joints composed of pneumatic muscles (PMs) can simulate the motion of biological joints. However, the PMs themselves have non-linear characteristics such as hysteresis and creep, which make it difficult to achieve high-precision trajectory tracking control of PM-driven robots. In order to effectively suppress the adverse effects of nonlinearity on control performance and improve the dynamic performance of PM-driven legged robot, this study designs a double closed-loop control structure based on neural network. First, according to the motion model of the bionic joint, a mapping model between PM contraction force and joint torque is proposed. Second, a control strategy is designed for the inner loop of PM contraction force and the outer loop of bionic joint angle. In the inner control loop, a feedforward neuron Proportional-Integral-Derivative controller is designed based on the PM three-element model. In the control outer loop, a sliding mode robust controller with local model approximation is designed by using the radial basis function neural network approximation capability. Finally, it is verified by simulation and physical experiments that the designed control strategy is suitable for humanoid motion control of antagonistic PM joints, and it can satisfy the requirements of reliability, flexibility, and bionics during human-robot collaboration.
In this study, a new semi-Markov process (SMP)-based model is devised to evaluate the IEEE 802.11p enhanced distributed channel access (EDCA) broadcast performance for vehicular safety communication. Differing from the existing SMP analytical models, the proposed model takes the virtual collision among various prioritized access categories (ACs) inside each vehicle into consideration. Moreover, in contrast to the Markov chain-based models, our model is simpler but with approximate accuracy. Concretely, we first capture the behavior of each AC’s backoff entity using SMP. Then, the parameters of interest in the vehicular ad hoc network (VANET) such as packet transmission probability, conditional collision probability, and saturation throughput are derived. Finally, via MATLAB simulations, we demonstrate that the newly developed model achieves comparable accuracy in calculating these output parameters while its complexity and computation time is around one-tenth of that of the Markov chain-based models. Therefore, the proposed model is more suitable for real-time performance analysis of IEEE 802.11p EDCA safety communication in a freeway scenario.
This paper integrates the classical resource-constrained project scheduling problem with step deterioration and accordingly presents a solution-based tabu search (SB-TS) algorithm to solve it with challenging computations. First, we choose successor list size as the priority rule to generate the initial solution of the presented methods. Next, the corresponding solution decoding and calculation ways are defined to update the related configuration at each iteration. Compared with the state-of-the-art methods, in terms of the known datasets, the proposed SB-TS finds the optimal solution for each instance in J30 and shows strongly competitive performance in J60, J90, and J120. Then, on newly generated datasets, where the step deterioration is attached to, the comparison experimental data state that the SB-TS with high-quality solution is superior to the move-based tabu search (MB-TS) algorithm. In addition, two key components are investigated to emphasize their attributions to the proposed algorithm.
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