International Journal of Control Automation and Systems最新文献

In this paper, a novel data-driven control algorithm based on model-free adaptive control is presented, addressing general discrete-time single-input single-output nonlinear systems, approximated by an equivalent dynamic linearization model using pseudo-partial derivatives. The closed loop stability is proved, showing that the tracking error asymptotically vanishes. Moreover, the proposed approach has been applied to a 5 MW wind turbine, considering as control target the efficiency optimization issue when the turbine operates under medium wind speed conditions. Validation of the technique has been performed, testing the overall control system by simulation using the tool FAST developed by the National Renewable Energy Laboratory (NREL).

This article presents an improved data-driven adaptive control structure to address the problem of input and output saturation in unknown nonlinear systems with multiple inputs and multiple outputs. In the suggested structure, a virtual model of the controlled system is initially built utilizing a multi-layered group method of data handling neural network. The control signal is then applied to this virtual model to predict the output before being applied to the system. If the predicted output is saturated, the control signals are readjusted to prevent saturation and are then applied to the system. By using this proposed structure, the performance of model-free adaptive control against input/output saturation phenomena is improved and the occurrence of saturation is prevented. Based on Lyapunov’s theory, the stability of the suggested structure is proven. The controller has been applied to an interconnected three-tank system and a subway train which results clearly illustrate the advantages of the suggested method over the traditional form of model-free adaptive control design.

This paper studies how to design a control scheme for a complex dynamical network (CDN) such that the state of nodes and links can track on any given reference signals respectively, under the view that the CDN is coupled by the nodes and links. Since the dynamic behavior of the links reflects the changes in network topology(NT), the weights of the links are regarded as state variables of the NT. In addition, since the state of the links is not always avaluable in practical engineering applications, in order to address this problem, this paper provides an asymptotical state observer that uses its observation values to estimate the links state. Based on this, this paper proposes a new control scheme which designs controllers in the nodes and links respectively, to realize the asymptotical tracking control of the nodes and links. In order to understand the NT tracking target, an illustrative example is that the star topology can be chosen as the NT tracking target of communication transmission network for the centralized management. Finally, the validity of the theoretical results is verified by a numerical experiment that applies the control scheme to a helicopter model.

This study presents an investigation into trajectory planning for a 5-degree-of-freedom pneumatic robot using the firefly metaheuristic algorithm (FMA). The methodology employed uses FMA and is based on the application of 5th-degree B-splines for interpolating intermediate points, aiming to obtain trajectories that consider the lower actuator forces associated with the dynamics of the manipulator under study (objective function). Kinematic and dynamic constraints determined by the operational characteristics of pneumatic servo-positioners were proposed, such as friction modeling, calculation of equivalent mass, velocity, acceleration etc. The results demonstrates that the trajectories generated by the proposed method and by 7th-degree optimized trajectory presented in literature resulted in similar average error and maximum error when applied in the pneumatic robot, indicating that the other issues, such as friction modeling, robot vibration, among others, need to be investigated to improve robot under investigation control.

The output regulation (OR) problem for linear heterogeneous networked multi-agent systems (NMAS) with communication constraints is investigated in this paper. A cost function is given to measure the coordinated control between NMAS. The objective of this paper is to design controllers such that not only the cost function is optimized, the asymptotic tracking and disturbance rejection are achieved but also the communication constraints are actively compensated. It is assumed that only a part of agents can receive the information of the exosystem, which can generate the reference signal and the disturbance signal. First, distributed observers are proposed to estimate the states of the subsystems and the state of the exosystem, which can guarantee the exponentially convergence of the estimation errors. Then, an observer-based predictive controller is proposed using feedforward method and networked predictive control method. With the help of OR technique, it indicates that the proposed predictive control scheme can deal with the OR problem. Finally, a numerical example is presented to illustrate our results.

A snake robot should be robust to actuator failures because it has more actuators than required for locomotion. However, controlling a snake robot is problematic if some of the joints become passive because of problems in power supply lines. In a recent study, we proposed a controller to achieve path-following in cases where one of the joints is passive. However, the controller applies only to cases with a single passive joint. With two or more passive joints, it cannot be applied even if sufficient active joints remain. This paper considers the path-following control of a snake robot with passive joints by extending our previous study (the single passive joint case). The validity of the method is verified by numerical simulation.

The advancement of foundation models, such as large language models (LLMs), vision-language models (VLMs), diffusion models, and robotics foundation models (RFMs), has become a new paradigm in robotics by offering innovative approaches to the long-standing challenge of building robot autonomy. These models enable the development of robotic agents that can independently understand and reason about semantic contexts, plan actions, physically interact with surroundings, and adapt to new environments and untrained tasks. This paper presents a comprehensive and systematic survey of recent advancements in applying foundation models to robot perception, planning, and control. It introduces the key concepts and terminology associated with foundation models, providing a clear understanding for researchers in robotics and control engineering. The relevant studies are categorized based on how foundation models are utilized in various elements of robotic autonomy, focusing on 1) perception and situational awareness: object detection and classification, semantic understanding, mapping, and navigation; 2) decision making and task planning: mission understanding, task decomposition and coordination, planning with symbolic and learning-based approaches, plan validation and correction, and LLM-robot interaction; 3) motion planning and control: motion planning, control command and reward generation, and trajectory generation and optimization with diffusion models. Furthermore, the survey covers essential environmental setups, including real-world and simulation datasets and platforms used in training and validating these models. It concludes with a discussion on current challenges such as robustness, explainability, data scarcity, and real-time performance, and highlights promising future directions, including retrieval augmented generation, on-device foundation models, and explainability. This survey aims to systematically summarize the latest research trends in applying foundation models to robotics, bridging the gap between the state-of-the-art in artificial intelligence and robotics. By sharing knowledge and resources, this survey is expected to foster the introduction of a new research paradigm for building generalized and autonomous robots.

In this paper, a wave-to-wire global optimal control framework for a networked wave-energy converter array (WECA) power system is presented. For the proposed framework, a distributed global optimal control method considering maximum wave energy harvesting and optimal power dispatch (OPD) is developed in the WECA system for precise power output. First, the WECA including G-type and S-type nodes is modeled, and the corresponding model predictive control (MPC) and primary control strategy are developed to maximally harvest the wave energy and to accurately track the dispatched reference power. Second, a distributed OPD method integrating convex optimization and consensus is proposed to actively regulate each energy storage (ES) unit and to manage the power system to satisfy the specified power output reference. Finally, the effectiveness of the proposed control method is investigated and analyzed by semi-physical hardware-in-loop simulation tests. The results of the experiments demonstrate that the power system output matches with the specified power demand, and indicate reliable performance.

With the increasing complexity of the robot grasping environment, it puts forward higher requirements on the grasping strategy of manipulators. However, grasping cluttered multi-class objects is a challenging task because the objects are stacked and occluded from each other, and it is often difficult for robots to find a suitable grasping position. Deep reinforcement learning with DQN algorithm has been used to study the pushing and grasping strategy to manipulate cluttered multi-class objects. However, there exists long learning time and low success rate. To solve this problem, we adopt two fully convolutional networks (FCN) to map the color and depth maps to actions: pushing and grasping. These two networks are trained by an improved soft actor-critic algorithm that includes auto entropy regularization, regularized objective function and clipped double Q learning. Pushing and grasping synergies has been learned with the dense reward feedback. The simulation experiment demonstrate that the learning process converges quickly and stable with a grasp success rate up to 83.3%. We further demonstrate the generalization ability and well performance of our models when novel objects appear in the scenes that the robot has never grasped before. Finally, real-world experiments with trained models from simulations are conducted to test the grasping performance on manually arranged scenes.

This paper investigates the N-coalition game, which covers the generalized Nash equilibrium problems (GNEPs) and distributed optimization problems. To seek the Nash equilibrium of the N-coalition games, we firstly exploit a centralized algorithm. Under the full information, the algorithm can exponentially converge to the Nash equilibrium of the N-coalition games. Then, based on the extremum seeking (ES) approach, we extend the centralized algorithm to distributed counterpart. Different from the centralized algorithm, the explicit cost functions are not available in the distributed algorithm. The players not only can converge to a small neighborhood of the Nash equilibrium, but do not communicate with the other coalitions. Furthermore, in order to reduce the communication burden in intra-coalition, an event-triggered based distributed algorithm is proposed. By the algorithm, the players only communicate with their neighbors in intra-coalition at event-triggered time constants, and also can converge to a neighborhood of the Nash equilibrium of the N-coalition games. Finally, an example about Nash-Cournot game is given to illustrate the effectiveness of our algorithms.