International Journal of Control Automation and Systems最新文献

A non-minimum phase system has unstable zero internal dynamics. Even though the system’s output has stabilised, the state variables of the internal dynamics continue to grow indefinitely. Uncertain parameters in the internal dynamics make their behaviour even more unpredictable. In this article, we solve the tracking problem for a bilinear control system with unstable internal dynamics and uncertain parameters using adaptive backstepping. The bilinear control system is transformed using input-output feedback linearisation to normal form. The unstable internal dynamics containing uncertain parameters are first stabilised using the external dynamics as a virtual control. The external dynamics are then stabilised using other state variables in the external dynamics; the final system uses the actual control function. Numerical simulations are performed to demonstrate the proposed control’s technical implementation and performance. A robustness test is conducted analytically and numerically to understand the control function’s tolerance to uncertain parameters. The simulation results show that the control function successfully solves tracking problems in non-minimum phase systems. Using the integral absolute error criterion, we also determine the range of uncertain parameter values for which the control function works satisfactorily.

An adaptive control method based on immersion and invariance (I&I) is presented in a class of nonlinear systems with time-varying uncertain parameters. A parameter estimation law based on reference models using I&I is designed to accelerate the convergence of estimated parameters to the true value, enabling the closed-loop system to reach the predefined target system on the manifold more quickly and reducing the energy consumption of the system. The inherent integrability obstacles in I&I are overcome by using dynamic scaling techniques, reducing the complexity of controller design. Stability analysis of the closed-loop system demonstrates that the proposed control method can achieve asymptotic stability control of the target system, and verified the robustness of the closed-loop system in the face of external disturbances. Finally, simulations of attitude tracking control demonstrate the effectiveness and superiority of the proposed method.

Time-delay is an unavoidable factor in analyzing and designing any networked system since in most scenarios, it decreases the performance or even destabilizes the system. In this paper, we study the newly proposed matrix-scaled consensus (MSC) algorithms under the presence of communication time delays. Both networks of single- and double-integrator agents are considered, and for each scenario, a corresponding delay margin will be given. The analysis hinges on the Nyquist stability criteria and the algebraic solution of quasi-polynomials associated with the MSC models. Numerical examples are provided to demonstrate the correctness of theoretical results.

In this article, some synchronization issues for delayed fractional-order fuzzy neural networks (FOFNNs) including uncertain parameters are discussed. To begin with, the complete synchronization (CS) criterion is derived through the adaptive controller. This control strategy can effectively reduce control costs. Next, applying the pinning controller, the CS condition is inferred by controlling partial nodes. Meanwhile, considering the advantages of adaptive and pinning control, an adaptive pinning controller is established to reach CS. Finally, we present some numerical examples to demonstrate the derived criteria.

This work considers the innovation-based attacks with side information under the energy constraint in cyber-physical systems where Kullback-Leibler (K-L) divergence is used as the stealthiness metric. Moreover, the attacker requires to decide when to launch attacks over a finite time horizon since the energy limitation. To cause the largest degradation to the estimation performance, the attack strategy and schedule require to be designed synergistically under the constraints. The terminal error (TE) and average error (AE) are respectively taken as attack performance indices. Then, the optimal attack policies for the TE and AE are obtained by solving a constrained optimization problem and a 0–1 programming problem. Finally, simulation examples are employed to demonstrate the results.

This paper addresses the dynamic output feedback leader-following consensus control for a class of high-order stochastic multi-agent systems characterized by unknown time-varying delays. The agents are modeled as a class of stochastic strict feedback nonlinear systems with unknown time-varying delays. Additionally, the states of the agents are unknown for control design. To address these challenges, observers are designed firstly to estimate the unknown states of each agent. Subsequently, a distributed observer-based output feedback consensus protocol, relying solely on the outputs of neighboring agents, is introduced. It is shown that the followers can effectively track the leader’s output with a 1st-moment exponential rate. The effectiveness of the proposed control scheme is validated through simulation examples.

In the face of challenges encountered during femur fracture surgery, such as the high rates of malalignment and X-ray exposure to operating personnel, robot-assisted surgery has emerged as an alternative to conventional state-of-the-art surgical methods. This paper introduces the development of a leader-follower robot-assisted system for femur fracture surgery, called Robossis. Robossis comprises a 7-DOF haptic controller and a 6-DOF surgical robot. A control architecture is developed to address the kinematic mismatch and the motion transfer between the haptic controller and the Robossis surgical robot. A motion control pipeline is designed to address the motion transfer and evaluated through experimental testing. The analysis illustrates that the Robossis surgical robot can adhere to the desired trajectory from the haptic controller with an average translational error of 0.32 mm and a rotational error of 0.07°. Additionally, a haptic rendering pipeline is developed to resolve the kinematic mismatch by constraining the haptic controller’s (user’s hand) movement within the permissible joint limits of the Robossis surgical robot. Lastly, in a cadaveric lab test, the Robossis system was tested during a mock femur fracture surgery. The result shows that the Robossis system can provide an intuitive solution for surgeons to perform femur fracture surgery.

This paper introduces an adaptive hybrid approach to address the forward kinematics problem of a Hexa parallel robot (HPR), known for its challenge in obtaining a unique closed-form analytic solution. In the initial stage, we construct an artificial neural network (ANN) model to rapidly generate a preliminary result, effectively narrowing the search space. Subsequently, bacterial foraging optimization (BFO) is adapted to refine the result by focusing on exploration within the reduced search space. Adaptive functions adjust BFO parameters based on the error level in the preliminary result, enhancing algorithm performance. Software is developed to demonstrate the practical application of this method. Experimental results within the robot workspace indicate a significant reduction in calculation errors compared to using only the ANN model.

The retargeting human motions to those of a humanoid robot is a difficult task that involves using complex humanoid models and intensive geometric calculations, while also requiring high joint recognition accuracy. Herein, we propose a new motion retargeting framework for whole-body motions using a monocular camera composed of three modules per frame: 1) the extraction of 3D human joint coordinates from a package from pose AI, 2) the calculation of human joint angles by fitting the humanoid model to the skeleton model attained from the pose AI using a global optimization method, and 3) the transmission of the estimated joint angles as a pose command to a full-scale humanoid robot. The results suggest that the proposed framework can reproduce human-like motion sequences while reflecting certain limitations in the robot’s joint angles due to intentionally set hardware limitations and constraints. Using the proposed method, the robot can directly mimic human motion at the joint angle level based solely on images taken by an RGB camera or video files. These findings suggest that it would be useful to construct big data consisting of joint angle vectors for various human poses and joint angle trajectories for human motions, so that—as one example application in the near future—a robot butler could refer to these big data when performing various motions at a person’s home or in the office.

As the range of applications of robots expanded, they began to be used for complex assemblies such as screw fastening and pin assembly. Most specialized screw fastening tools are sensitive to external influences and require additional instruments to prevent screw dislodgement when working in unstructured environments. To address this challenge, we propose a screw fastening gripper (SFG) capable of screw fastening and small pin gripping with a single power source. The proposed SFG is divided into a fastening part for screw fastening and a gripper for grasping, with power distributed via a magnetic gear. It is designed to temporarily separate the gripper and fastener using the magnetic gear’s features, so it can be used to fasten screws of various lengths if necessary. Through gripping force measurements and screw fastening experiments, the proposed SFG showed sufficient performance in grasping and screw fastening.