This paper is concerned with bipartite consensus tracking for multi-agent systems with unknown disturbances. A barrier function-based adaptive sliding-mode control (SMC) approach is proposed such that the bipartite steady-state error is converged to a predefined region of zero in finite time. Specifically, based on an error auxiliary taking neighboring antagonistic interactions into account, an SMC law is designed with an adaptive gain. The gain can switch to a positive semi-definite barrier function to ensure that the error auxiliary is constrained to a predefined neighborhood of zero, which in turn guarantees practical bipartite consensus tracking. A distinguished feature of the proposed controller is its independence on the bound of disturbances, while the input chattering phenomenon is alleviated. Finally, a numerical example is provided to verify the effectiveness of the proposed controller.

This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables. The focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data packets. For this purpose, a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical probabilities. Then, an asynchronous fuzzy sampled-data controller, featuring distinct premise variables from the active suspension system, is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of membership. Furthermore, novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous H₂ and H_∞ performance requirements. Finally, the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.