Currently, most assumptions in nonholonomic mobile robot controllers indicate that the robot velocity can become significantly large and that the robot actuators are able to generate the necessary level of torque input. Based on the sliding mode control theory, this paper develops a new framework to handle the control problem of a wheeled robot dynamics model with constrained velocity and torque. Through rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed controller guarantees asymptotic convergence of tracking or stabilization errors and boundedness of closed-loop signals. The advantages of the developed controller include the ability to simultaneously achieve tracking and stabilization control of nonholonomic mobile robots and the ability to ensure that the prescribed velocity and torque constraints are not breached by simply tuning the design parameters a priori, even in the presence of uncertain disturbances.

In this paper, we investigate the modeling and control of networked discrete-event systems (DESs), where a supervisor is connected to the plant via an observation channel and the control commands issued by the supervisor are delivered to the actuator of the plant via a control channel. Communication delays exist in both the observation channel and the control channel. First, a novel modeling framework for the supervisory control of DESs subject to observation delays and control delays is presented. The framework explicitly models the interaction process between the plant and the supervisor over the communication channels. Compared with the previous work, a more accurate “dynamics” of the closed-loop system is specified. Under this framework, we further discuss how to estimate the states of the closed-loop system in the presence of observation delays and control delays. Based on the state estimation, we synthesize an optimal supervisor on the fly to maximize the controlled behaviors while preventing the system from leaving the desired behaviors under communication delays. We compare the proposed supervisor with the supervisor proposed in the literature and show that the proposed supervisor is more permissive. As an application, we show how the proposed approach can be applied to manage vehicles in a signal intersection. Finally, we show how to extend the proposed framework to model a system whose actuators and sensors are distributed at different sites.

In industrial applications, many systems present serious productivity problems due to limited resources. Generally, the dynamics of resource allocation are inherently discrete-event driven, such as the buffer allocation in production line systems. In this paper, we develop a discrete-event mathematical model for resource allocation optimization. In this work, we consider two crucial optimization objectives, e.g., deadlock-free and efficiency, that originate from the customer’s actual requirements. The main aim is to develop a resource allocation scheme for fulfilling the production process (without deadlock) while ensuring that the cost of the process is minimized. As a case study, we consider the vehicle scheduling problem in a signal-free intersection. The intersection is divided into several disjoint spatial traffic resources, and vehicles need to occupy different traffic resources for passing through the intersection. Thus, the traffic control problem at the signal-free intersection is transformed into a scheduling problem with limited resource constraints. An online control approach is developed to schedule vehicles to go through the intersection safely and efficiently by optimizing the resource allocation order. Simulation results demonstrate the efficiency and robustness of the proposed model and optimization approach.

In this paper, a novel adaptive control scheme is proposed for the path-following problem of a nonholonomic mobile robot with uncertain dynamics based on barrier functions. To optimize communication resources, we integrate an event-triggered mechanism that avoids Zeno behavior and ensures that the tracking error of the closed-loop system converges to a small neighborhood around zero within a fixed time, while consistently satisfying predefined transient performance requirements. Extensive simulation studies demonstrate the effectiveness of the proposed approach and validate the theoretical results.

In this paper, we investigate the consensus control problem of Euler–Lagrange systems which can be used to describe the motion of various mechanical systems such as manipulators and quadcopters. We focus on consensus control strategies, which are important for achieving coordinated behavior in multi-agent systems. The paper considers the key challenges posed by random communication delays and packet losses that are increasingly common in networked control systems. In addition, it is assumed that each system receives information from neighboring agents intermittently. Addressing these challenges is critical to ensure the reliability and efficiency of such systems in real-world applications. Communication delay is time-varying and can be very large, but should be smaller than some bounded constant. To decrease the frequency of control input updates, we implement an event-triggered scheme that regulates the controller’s updates for each agent. Specifically, it does not update control inputs at traditional fixed intervals, but responds to predefined conditions and introduces a dynamic consensus item to handle information irregularities caused by communication delays and intermittent information exchange. The consensus can be achieved if the communication graph of agents contains a spanning tree with the desired velocity as the root node. That is, all Euler–Lagrange systems need to obtain the desired velocity, directly or indirectly (via neighbors), to reach consensus. We establish that the Zeno behavior can be avoided, ensuring a positive minimum duration between successive event-triggered instances. Finally, we provide simulation results to show the performance of our proposed algorithm.