In this article, the event-triggered fixed-time tracking control is investigated for uncertain strict-feedback nonlinear systems involving state constraints. By employing the universal transformed function (UTF) and coordinate transformation techniques into backstepping design procedure, the proposed control scheme ensures that all states are constrained within the time-varying asymmetric boundaries, and meanwhile, the undesired feasibility condition existing in other constrained controllers can be removed elegantly. Different from the existing static event-triggered mechanism, a dynamic event-triggered mechanism (DETM) is devised via constructing a novel dynamic function, so that the communication burden from the controller to actuator is further alleviated. Furthermore, with the aid of adaptive neural network (NN) technique and generalized first-order filter, together with Lyapunov theory, it is proved that the states of closed-loop system converge to small regions around zero with fixed-time convergence rate. The simulation results confirm the benefits of developed scheme.
Summary Model predictive control (MPC) is capable to deal with multiconstraint systems in real control processes; however, the heavy computation makes it difficult to implement. In this paper, a dual‐mode control strategy based on event‐triggered MPC (ETMPC) and state‐feedback control for continuous linear time‐invariant systems including control input constraints and bounded disturbances is developed. First, the deviation between the actual state trajectory and the optimal state trajectory is computed to set an event‐triggered mechanism and reduce the computational load of MPC. Next, the dual‐mode control strategy is designed to stabilize the system. Both recursive feasibility and stability of the strategy are guaranteed by constructing a feasible control sequence and deducing the relationship of parameters, especially the inter‐event time and the upper bound of the disturbances. Finally, the theoretical results are supported by numerical simulation. In addition, the effects of the parameters are discussed by simulation, which gives guidance to balance computational load and control performance.
This paper focuses on the problem of collision-free lane change for autonomous vehicles under dynamic obstacle road scenarios and proposes a new trajectory planning algorithm for autonomous vehicles. With the goals of dynamics feasibility, comfort, and safety in mind, we develop a B-spline-based trajectory planning method capable of generating trajectories which satisfy all the desired objectives. Specifically, (i) the maximum velocity, the maximum acceleration and the maximum curvature of the trajectory are taken into account explicitly to ensure the comfort of the ride and the traceability of the planned path; (ii) MINVO basis is used to obtain outer polygon representations with minimum area of each interval of the host vehicles' trajectories. These polygon representations are separated by designed artificial hyperplanes to ensure collision avoidance; and (iii) the objective function is designed to guarantee both feasibility and comfort with the given trajectory by incorporating a jerk term and a deviation from the goal point for the penalty. Finally, simulation experiments on both the ample and aggressive scenarios are conducted to verify the effectiveness of this proposed trajectory planning algorithm.
In this paper, a distributed model predictive control (DMPC) algorithm for a platoon of heterogeneous vehicles is proposed. The leading vehicle is allowed to be driven by a non-zero and timevarying input, rather than travelling at a constant velocity. Except for individual state and input constraints for each vehicle, all vehicles are coupled via state-coupled inter-vehicular spacing constraints and state-coupled cost functions, which maintain the unidimensional platoon formation with satisfactory transient performance. Each vehicle communicates with its neighboring vehicles, and may not know the leading vehicle’s kinetic status information. The control input of each following vehicle is computed by a local optimization problem established by each vehicle’s local information and the assumed state information from its neighbors. By designing distributed terminal control laws for following vehicles, dividing each state-coupled set into several specific subsets, and then forcing each following vehicle to optimize its state constrained in the assigned subsets, the coupled constraints and cost functions can be decoupled, and thus a distributed and parallel computing method can be adopted to compute the control inputs of all following vehicles. Based on the tailored terminal equality constraints together with the tailored terminal control laws, the recursive feasibility of MPC optimization problems is achieved at all time steps and the asymptotic stability of each vehicle is also guaranteed. The effectiveness of the proposed DMPC method is demonstrated in simulation, and the advantage of the proposed DMPC dealing with the leading vehicle’s non-zero, inaccessible, and time-varying input is highlighted by a comparison simulation for heterogeneous vehicle platoon with a continuously changing leading vehicle velocity.
Model predictive control (MPC) is one of the most popular approaches for vehicle trajectory tracking problem, since it provides optimal strategy by predicting its future behaviors, and at the same time ensures robustness. However, MPC requires a large amount of computing resources for optimization at each step. This results in poor performance of the algorithm. In this paper, a novel workflow-based MPC approach is proposed to accelerate the traditional MPC algorithm. First, a trajectory tracking method using MPC based on alternating direction method of multipliers (ADMM) algorithm is developed for online optimization. Then, we seperate the algorithm into multiple smaller computational tasks and provide an approach on establishing the workflow of MPC. Finally, it is shown that the workflow-based method improves the accuracy of trajectory tracking significantly and achieves the finer-grained discretization of continuous systems. The computation time is reduced by at most 62.89 $ \%$ .
In data-driven predictive cloud control tasks, the privacy of data stored and used in cloud services could be leaked to malicious attackers or curious eavesdroppers. Homomorphic encryption technique could be used to protect data privacy while allowing computation. However, extra errors are introduced by the homomorphic encryption extension to ensure the privacy-preserving properties, and the real number truncation also brings uncertainty. Also, process and measure noise existed in system input and output may bring disturbance. In this work, a data-driven predictive cloud controller is developed based on homomorphic encryption to protect the cloud data privacy. Besides, a disturbance observer is introduced to estimate and compensate the encrypted control signal sequence computed in the cloud. The privacy of data is guaranteed by encryption and experiment results show the effect of our cloud-edge cooperative design.
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