In this work, we develop an analytical framework that integrates opinion dynamics with a recommendation system. By incorporating elements such as collaborative filtering, we provide a precise characterization of how recommendation systems shape interpersonal interactions and influence opinion formation. Moreover, the property of the coevolution of both opinion dynamics and recommendation systems is also shown. Specifically, the convergence of this coevolutionary system is theoretically proved, and the mechanisms behind filter bubble formation are elucidated. Our analysis of the maximum number of opinion clusters shows how recommendation system parameters affect opinion grouping and polarization. Additionally, we incorporate the influence of propagators into our model and propose a reinforcement learning-based solution. The analysis and the propagation solution are demonstrated in simulation using the Yelp data set.

Opinion dynamics models that elucidate the evolution and formation of opinions conventionally focus on pairwise interactions within graphs, often overlooking the complex higher-order interactions that arise in real-world social networks, such as online meetings and group chats. In this article, a continuous-time dynamical system is developed to study opinion-forming processes over higher-order networks associated with undirected hypergraphs. The proposed model introduces a novel diffusion-like interaction function to characterize interactions of different orders over hypergraphs. The convergence and stability of the dynamical systems are further examined in both the presence and absence of stubborn individuals. Building on traditional opinion dynamics models and integrating weak-tie theory, we emphasize the critical role of higher-order interactions in shaping individual opinions, enhancing network communication efficiency, and mitigating opinion polarization. Finally, all theoretical results are extensively investigated and empirically validated through numerical experiments on both synthetic and real-world network datasets.

Since collision can change the velocity in a very short time, a collision-inclusive trajectory planning algorithm for unmanned aerial vehicle (UAV) can utilize collision to get a fast and energy-efficient trajectory in complex environments. We proposed a lightweight collision-inclusive trajectory planner, which can be integrated into a UAV system easily. The trajectory segments that need to be optimized are recognized by the curvature of the collision-free trajectory. After getting the pre-collision information by forward integration of sampled control, the optimal collision position and time will be generated by the collision-inclusive optimizer in 40ms. The experiments verify the effectiveness and efficiency of our method.

This paper proposes a novel distributed coverage controller for a multi-agent system with constant-speed unicycle robots (CSUR). The work is motivated by the limitation of the conventional method that does not ensure the satisfaction of hard state-and input-dependent constraints and leads to feasibility issues for multi-CSUR systems. In this paper, we solve these problems by designing a novel coverage cost function and a saturated gradient-search-based control law. Theoretical proofs are provided to guarantee that the CSURs ultimately move to the optimal coverage configuration without moving out of the covered domain. The controller is implemented in a distributed manner based on a novel communication standard among the agents. A series of simulation studies are conducted to validate the correctness of our theory by showing the efficacy of the proposed coverage controller in different initial conditions and with various control parameters. A comparison study in simulation reveals the advantage of the proposed method over the conventional method in terms of avoiding infeasibility. The experimental study verifies the applicability of the method to real robots. The development procedure of the method from theoretical analysis to experimental validation provides a novel framework for multi-agent system coordinate control with complex dynamics.Note to Practitioners—This paper gives a novel method to effectively cover a polygonal area using multiple constant-speed unicycle robots (CSUR) like wheeled robots and fixed-wing unmanned aerial vehicles (fUAV). Compared to the conventional approaches, our method allows these robots to cover a target region using circular orbits without departing the covered region. Also, the method satisfies common control saturation constraints in practice and can be implemented in a reliable distributed scheme. While the efficacy and correctness of the proposed method are rigorously proved using control theory, we also provide necessary interpretive elucidations to explain its underlying mechanism and selection rationale. The method is validated to be effective for wheeled robots in experimental studies, although it can also be applied to fUAVs in theory.

In this article, an incremental model predictive controller is proposed for the velocity-controlled robot manipulator. First, the time-delay estimation (TDE) technique is used to approximate the unknown discrepancy between the command and the real joint velocity due to velocity dynamics, and the equation of motion in the incremental form is obtained. Then, on the basis of the resulting equation of motion and taking into account joint position and velocity constraints, the incremental model predictive controller is developed by formulating a constrained optimal control problem (OCP). The constrained OCP is cast to a quadratic programming (QP) problem, making it possible to employ computationally efficient QP solvers to solve the constrained OCP. As a result, real-time control is guaranteed. Finally, experiments on a robot manipulator are implemented to verify the effectiveness of the developed incremental model predictive controller.

Observer-based methods are widely used to estimate the disturbances of different dynamic systems. However, a drawback of the conventional disturbance observers is that they all assume persistent excitation (PE) of the systems. As a result, they may lead to poor estimation precision when PE is not ensured, for instance, when the disturbance gain of the system is close to the singularity. In this paper, we propose a novel disturbance observer based on concurrent learning (CL) with time-variant history stacks, which ensures high estimation precision even in PE-free cases. The disturbance observer is designed in both continuous and discrete time. The estimation errors of the proposed method are proved to converge to a bounded set using the Lyapunov method. A history-sample-selection procedure is proposed to reduce the estimation error caused by the accumulation of old history samples. A simulation study on epidemic control shows that the proposed method produces higher estimation precision than the conventional disturbance observer when PE is not satisfied. This justifies the correctness of the proposed CL-based disturbance observer and verifies its applicability to solving practical problems.

This article proposes an off-policy risk-sensitive reinforcement learning (RL)-based control framework to jointly optimize the task performance and constraint satisfaction in a disturbed environment. The risk-aware value function, constructed using the pseudo control and risk-sensitive input and state penalty terms, is introduced to convert the original constrained robust stabilization problem into an equivalent unconstrained optimal control problem. Then, an off-policy RL algorithm is developed to learn the approximate solution to the risk-aware value function. During the learning process, the associated approximate optimal control policy is able to satisfy both input and state constraints under disturbances. By replaying experience data to the off-policy weight update law of the critic neural network, the weight convergence is guaranteed. Moreover, online and offline algorithms are developed to serve as principled ways to record informative experience data to achieve a sufficient excitation required for the weight convergence. The proofs of system stability and weight convergence are provided. The Simulation results reveal the validity of the proposed control framework.

This article investigates the tracking problem of an uncertain$n$-link robot manipulator with guaranteed safety and performance. To tackle parametric uncertainties, the torque filtering-augmented concurrent learning (CL) method is introduced for online identification of the unknown system without requirements of joints acceleration. By using CL, the parameter convergence is guaranteed by exploiting the current and historical data simultaneously. This technique enjoys practicability compared with common methods that need to incorporate external noises to satisfy the persistence of excitation condition for the parameter convergence. Based on the estimated model, we design a barrier Lyapunov function (BLF)-based adaptive control law by the backstepping technique and Lyapunov analysis. By ensuring the boundness of the BLF, the system output and the tracking error are proved to lie in the safety set and performance set, respectively. Numerical simulation results and experiment tests validate the proposed strategy.

Piecewise affine (PWA) systems are attractive models that can represent various hybrid systems with local affine subsystems and polyhedral regions due to their universal approximation properties. The identification problem of PWA systems amounts to estimating the number of subsystems, parameters of each subsystem, and the corresponding polyhedral partitions via state-input vectors. In this paper, we propose a novel approach to address the online identification problem of continuous-time PWA systems in state-space form. Specifically, an online active mode recognition algorithm and a generalized integral concurrent learning identifier are presented to acquire the number of subsystems, the switching sequence, and the parameter of each subsystem. In addition, we develop the optimization problem for the polyhedral partition estimation, which is solved by using the estimated switching sequence and subsystem parameters. The effectiveness of the proposed identification approach is demonstrated via simulation results.

This paper focuses on the performance tracking issue of complex industrial processes in double layer architecture. First, the nonlinear plants in the device layer are modeled by using Takagi-Sugeno (T-S) fuzzy technique, and are controlled by local proportional integral (PI) controller with the H∞performance guaranteed. Then, the outputs and inputs of local plants are sampled and transited to the operation layer to form the economic performance index (EPI), which is used to represent the performance of the tracking of economic objective. Furthermore, the setpoints, which are dynamically changing, are calculated via a compensator based on the error between the objective and the EPI at each step of the operation layer. Finally, the effectiveness of the proposed method is demonstrated by a nonlinear continuous stirred tank reactor (CSTR) model.

Complex industrial processes are controlled by the local regulation controllers at the field level, and the setpoints for the regulation are usually made by manual decomposition of the overall economic objective according to the operators' experience. If a precise static process model can be built, real-time optimization (RTO) can be used to generate the setpoints. Nevertheless, since the aforementioned control structure is actually open-loop, the desired economic objective of the whole processes may not be tracked when disturbances exist. Aiming at solving this problem, a novel network based model predictive control method (MPC) for setpoints compensation is proposed in this paper. Firstly, a multivariable proportional integral (PI) controller is designed to perform the local regulation control. Secondly, a stochastic packet dropout model is adopted to characterize the measurement and human-in-the-loop delay effect. Then, a model predictive controller considering the random dropout effect is developed to compensate the setpoints dynamically according to the changing conditions of the processes, such that the prescribed performance objective can be obtained. Finally, a flotation process model is employed to demonstrate the effectiveness of the proposed method.