IET Control Theory and Applications最新文献

Inspired by advancements in swarm autonomous vehicles and intelligent control systems, this research addresses the issue of frequency synchronization and phase tracking in oscillator networks. A novel distributed consensus protocol and a reinforcement learning algorithm for a multi-agent network with a leader–follower topology, considering stability conditions, are developed. The critic-based neuro-fuzzy learning (CBNFL) method aims to achieve consensus and minimize local tracking errors. Additionally, an explicit synchronization condition for the network using the Lyapunov theorem is derived. Each vehicle tracks its reference phase and frequency. Employing a fuzzy critic to evaluate the current state and generate a stress signal for the controller, the method prompts adaptive parameter adjustments to minimize this signal. The proposed design's versatility and adaptability to various networks demonstrate robustness against dynamic vehicle properties and network parameter uncertainties, ensuring consistent controller performance. This approach exhibits high scalability, accommodating numerous autonomous agents. To validate the proposed learning method's efficacy, numerical simulations are conducted on a network of five oscillators. The outcomes of implementing CBNFL compared with a conventional PI controller underscore the CBNFL method's superior performance and robustness in maintaining network stability and achieving synchronization.

This study presents a learning-based iterative model predictive control (MPC) scheme for unknown (Lipschitz continuous) nonlinear dynamical systems. The proposed method begins by learning the unknown part of the controlled system using a Gaussian process (GP), which helps derive multi-step reachable sets that are guaranteed to encompass the actual system states. At each time step in each iteration, the MPC controller calculates a sequence of control inputs that robustly satisfy state and control constraints, as well as terminal constraints based on the GP-based reachable sets. Then only the first control input is applied to the system. After the iteration, the initial state is reset, and the same procedure is executed with the MPC optimization problem defined by the updated terminal set and cost. As iteration goes on, improvement of the control performance is expected since more data is obtained and the environment is progressively explored. The proposed method provides properties such as recursive feasibility and input to state stability of the goal region under certain assumptions. Moreover, bound on the performance cost in each iteration associated with the implementation of the proposed MPC scheme is also analyzed. The results of the simulation study show that the proposed control scheme can iteratively improve the control performance.

This paper concerns the optimality problem of distributed linear quadratic control in a linear stochastic multi-agent system (MAS). The main challenge stems from MAS network topology that limits access to information from non-neighbouring agents, imposing structural constraints on the control input space. A distributed control-estimation synthesis is proposed which circumvents this issue by integrating distributed estimation for each agent into distributed control law. Based on the agents' state estimate information, the distributed control law allows each agent to interact with non-neighbouring agents, thereby relaxing the structural constraint. Then, the primal optimal distributed control problem is recast to the joint distributed control-estimation problem whose solution can be obtained through the iterative optimization procedure. The stability of the proposed method is verified and the practical effectiveness is supported by numerical simulations and real-world experiments with multi-quadrotor formation flight.

The complexity of real-world systems poses challenges to model-based control, sparking significant interest in model-free control methods. By depending exclusively on the system's input–output data, the proposed method eliminates the need to construct intricate internal system models. The implementation is straightforward, can satisfy bounded control inputs, and allows for arbitrary adjustment of the actuator's execution frequency. The proposed method establishes an iterative mechanism under the constraint of bounded control inputs. It guarantees the algorithm's convergence by ensuring the continuous narrowing of the control interval. Furthermore, the update conditions within the iterative strategy can adapt to extremely low and continuously adjusting actuator execution frequencies. The bounded stability of the control method is proven using the continuity definition of functions. Its effectiveness and feasibility are validated through simulation and experimental verification.

In this article, the problem of the prescribed-time formation control for the heterogeneous multi-agent systems (MASs) under actuator faults and external disturbances with sampling-data settings is discussed. A sufficient condition for the MASs to globally converge to a bounded neighborhood within a prescribed time is given, which can ensure the formation control performance of the MASs with actuator faults. Further, an event-triggered communication mechanism based on the sampled data is designed to reduce the communication burden. Such a triggering mechanism allows for adjusting the triggering interval to a certain extent while ensuring that the Zeno phenomenon is excluded for each agent. To mitigate the impact of the actuator faults and external disturbances on the system formation control, an actuator fault estimator is designed along with a prescribed-time state observer to estimate the state of each agent. Then an adaptive control strategy is developed so that the MASs can achieve the desired formation within a prescribed time under the actuator faults and external disturbances. Simulation results are performed to validate the effectiveness of the proposed control strategy.

Formation coordination can improve the overall performance of unmanned aerial vehicle (UAV) systems, so it is important to realize coordinated formation control. To solve the problems of ineffective internal collision avoidance and communication distance maintenance in existing formation control methods, a UAV formation control algorithm based on an improved artificial potential field and consensus is proposed. First, an improved artificial potential field obstacle avoidance method is used to solve the problems of collision avoidance and communication distance maintenance within the formation by setting a communication risk zone and a collision risk zone and by defining the dynamic adjustment parameters of the inter-UAV virtual force to improve the traditional artificial potential field method. Second, a time integrating factor is introduced, and an improved consensus-based formation control method is employed to overcome the problem of the artificial potential field method falling into the local optimum easily. Simulation experiments were designed to analyse the trends of the UAV formation motion trajectory, inter-UAV relative distance, attitude, and formation time. The analysis results show that the method can make the UAV formation consistent with the flight trajectory of the intended formation, solving the problems of collision avoidance and communication distance maintenance within the UAV formation.

This article proposes an extreme learning machine (ELM)-based super-twisting integral terminal sliding mode control (STITSMC) for speed regulation of a permanent magnet synchronous motor (PMSM). First, the PMSM is modeled in a non-cascade control structure for fast system response and uncertainty compensation in the speed and torque loops. Second, the STITSMC is designed with integral actions in both the sliding surface and the reaching law to reduce chattering. Third, the ELM is constructed to compensate for the system lumped disturbance, and relax the disturbance upper bound required by the controller which further reduces the chattering. Fourth, the stability of the whole control system is proved based on the Lyapunov method and the finite time convergence regions are derived for both the reaching and the sliding phases. Finally, the comparative simulations and experiments are conducted to show the superiority of the proposed control.

This survey provides an overview of combining federated learning (FL) and control to enhance adaptability, scalability, generalization, and privacy in (nonlinear) control applications. Traditional control methods rely on controller design models, but real-world scenarios often require online model retuning or learning. FL offers a distributed approach to model training, enabling collaborative learning across distributed devices while preserving data privacy. By keeping data localized, FL mitigates concerns regarding privacy and security while reducing network bandwidth requirements for communication. This survey summarizes the state-of-the-art concepts and ideas of combining FL and control. The methodical benefits are further discussed, culminating in a detailed overview of expected applications, from dynamical system modelling over controller design, focusing on adaptive control, to knowledge transfer in multi-agent decision-making systems.

This article proposes an online universal self-learning control (USLC) algorithm based on a physical performance policy-optimization neural network, which aims to solve the problem of universal self-learning optimal control laws for nonlinear systems with various uncertain dynamics. As a key system characterization, this algorithm predicts the discrepancy between the optimal and current control laws by evaluating overall performance in each iterative learning cycle, leveraging an offline-trained universal policy network. This approach is universal, as it does not rely on an exact system model and can adaptively control performance preferences across various tasks by customizing the physical performance cost weights. Using the established control law-performance surface and contraction Lyapunov function, the necessary assumptions and proofs for the stable convergence of the system within a three-dimensional manifold space are provided. To demonstrate the universality of USLC, simulation experiments are conducted on two different systems: a low-order circuit system and a high-order variable-span aircraft attitude control system. The stable control achieved under varying initial values and boundary conditions in each system illustrates the effectiveness of the proposed method. Finally, the limitations of this study are discussed.

This article studies the feedback stabilization problem of an impulsive switched linear system whose feedback loop is closed over a digital network. Particularly, the combined effects of mode switches, impulses, quantization, network delay and external disturbances on the stability of that system are investigated. By extending the previous delay-free and impulse-free methods of reachable-set approximation and propagation, some novel communication and control policies are designed to stabilize the concerned switched system with both network delay and impulses. In order to save the occupied network bandwidth, some event-triggered control policies are proposed. To handle the effects of the mode switches and the impulse, we design event-triggering conditions for both the case with no switch and no impulse on an inter-event interval and the case with one switch or one impulse on an inter-event interval. Note that the occurrence of a mode switch or an impulse leads to the switch of the event-triggering conditions. Under the event-triggered control policies, a stabilizing bit rate condition is derived. It is proven that even under impulses and network delay, the event-triggered control policies can stabilize the switched system at a lower bit rate than earlier works based on conventional time-triggered control policies.