Quantitative Predictive Monitoring and Control for Safe Human-Machine Interaction

In this paper, the stabilization of a continuous time-delayed system is considered. To control the bifurcation and chaos in a time-delayed system, a parameter perturbation control and a hybrid control are proposed. Then, to ensure the asymptotic stability of the system in the presence of unexpected system parameter changes, the adaptive control idea is introduced, i.e., the perturbation control parameter and the hybrid control parameter are automatically tuned according to the adaptation laws, respectively. The adaptation algorithms are constructed based on the Lyapunov-Krasovskii stability theorem. The adaptive parameter perturbation control and the adaptive hybrid control methods improve the corresponding constant control methods. They have the advantages of increased stability, adaptability to the changes of the system parameters, control cost saving, and simplicity. Numerical simulations for a well-known chaotic time-delayed system are performed to demonstrate the feasibility and superiority of the proposed control methods. A comparison of the two adaptive control methods is also made in an experimental study.

A parallel wire driven mechanism uses flexible wires instead of heavy rigid links. In this paper, we propose an adaptive position control method for the fully constrained parallel wire-driven systems that use the minimum number of wires under zero-gravity conditions. Unfortunately, conventional methods of adaptive control cannot be used directly because of the nonlinearity of an internal force term. To overcome such an impossibility, we incorporate a new idea into the conventional method to separate the internal force term linearly. Not only does this adaptive control method ensure precise positioning using external sensors; it enhances the robustness for uncertainty of the Jacobian matrix, which results from the error of actuator installation. First, we explain the linearization of the internal force term. Next, the adaptive control method for the parallel wire driven system using the uncertain Jacobian matrix is proposed; then we prove the motion convergence to desired points and discuss its robustness based on Lyapunov stability analysis. Finally, the usefulness of the proposed control scheme is demonstrated through both experiments and simulation

This paper focuses on designing an adaptive radial basis function neural network U+0028 RBFNN U+0029 control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively. The uniform ultimate boundedness of the closed U+002D loop system is guaranteed by the proposed controller. A coupled motor drives U+0028 CMD U+0029 system, which satisfies the structure of nonlinear system, is taken for simulation to confirm the effectiveness of the method. Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.

Trajectory tracking of tail-sitter aircraft by ℓ₁ adaptive fault tolerant control

Distributed energy resources (DE) or distributed generators (DG) with power electronics interfaces and logic control using local measurements are capable of providing reactive power related ancillary system services. In particular, local voltage regulation has drawn much attention in regards to power system reliability and voltage stability, especially from past major cascading outages. This paper addresses the challenges of controlling DEs to regulate local voltage in distribution systems. An adaptive voltage control method has been proposed to dynamically modify control parameters to respond to system changes. Theoretical analysis shows that there exists a corresponding formulation of the dynamic control parameters; hence the adaptive control method is theoretically solid. Both simulation and field experiment test results at the Distributed Energy Communications and Controls (DECC) Laboratory confirm that this method is capable of satisfying the fast response requirement for operational use without causing oscillation, inefficiency, or system equipment interference. Since this method has a high tolerance to real-time data shortage and is widely adaptive to variable power system operational situations, it is quite suitable for broad utility application.

It is very difficult to realize the networked tracking control of a controlled process with unclear mechanism. An adaptive networked tracking control method that we proposed can solve such a problem. The method is a data-based control scheme started from the input-output data of the controlled process. We have realized it in the NetCon system. Firstly, the T-S fuzzy model of the controlled object is established according to input-output data by using fuzzy cluster modeling technology. Then the fuzzy prediction model is obtained by transforming it into the fuzzy singleton model through equivalent transformation. A series of control actions are derived by inverting the fuzzy singleton model according to the reversibility condition. These predicted values can compensate the data dropout and time delay in the network communication channels. The influence of external disturbance and uncertainty on system performance is eliminated by using internal model structure and adaptive control method. Finally, experiment results in NetCon system show the effectiveness of the proposed method.

Adaptive control methods are widely used for the control of both linear and nonlinear dynamical systems. Among the adaptive control approaches, Model Reference Adaptive Control (MRAC) method has a systematic design feature in which response of a stable reference model is targeted to be followed by the dynamical system. In general, Linear Time Invariant (LTI) Reference Models are considered in the MRAC design and the adaptation rule is formulated accordingly. In this paper, the linear reference model is altered with a stable nonlinear one and the adaptation rule is re-formulated to cope with the nonlinearity. The stable nonlinear reference model is generated by using the State Dependent Riccati Equation (SDRE) approach and the adaptation rule is also updated by using the same SDRE idea. The effects of altering the linear reference model to the nonlinear counterpart are shown in the MRAC formulations. In addition to the new MRAC formulations, the proposed MRAC algorithm is applied to a 3 DoF helicopter test bed to reveal the advantages of the nonlinear reference model.

With the development of economic science and technology, the development of computer vision has undergone rapid changes, and various products relying on computer vision are also more and more, such as smart home, robot technology, and so on. At present, robot technology has become a very important part of the development of human science and technology, and in the field of industrial robots, the most rapid development is the robot with robot arm adaptive motion. It is very necessary to study the adaptive motion control of the manipulator based on machine learning. The robot with the adaptive motion of the manipulator can carry out logistics express sorting, operate in the doors and windows outside the building, and pick fruits in the orchard, which can ensure the effective implementation of hard work. Therefore, this paper proposes a mechanical adaptive control method based on a neural network. According to the motion model of the manipulator, the RBF neural network model is used to judge the stability of the system according to the Lyapunov function. The related algorithms of machine learning and multi-degree of freedom manipulator are studied and improved. The RBF neural network model approximates the unknown function infinitely and then establishes the complex motion model. Aiming at the adaptive neural network of a manipulator, a network adaptive terminal control method is proposed. Firstly, a stable manipulator motion system is designed by using a neural network, and then the terminal synovial controller is designed by using backstepping control technology. The stability of the method is proved by using the approximation virtual control technology of the neural network. The adaptive control is realized by using the learning and self-adaptability of the neural network; thus, the stability analysis of the closed-loop system is realized.

This paper proposes a new method of adaptive inverse control based on support vector machine–fuzzy rules acquisition system (SVM-FRAS) for the gas tungsten arc welding (GTAW) process. In this control mechanism, an identifier is established based on SVM-FRAS, and an inverse controller based on SVM-FRAS is designed. The proposed adaptive inverse control method can automatically extract control rules from the process data. Comprehensibility is one of the required characteristics for a complex GTAW process control. We use the proposed SVM-FRAS-based adaptive inverse control method to obtain the rule-based process and the control model of the aluminum alloy pulse GTAW process. Based on the simulation experiments for GTAW process, the SVM-FRAS adaptive inverse control method is found to be effective.

This article investigates the trajectory tracking problem of quadrotor formation with collision avoiding. The safety distributed control strategy is introduced for quaternion-based multiple quadrotors formation to avoid collision. Then, based on super-twisting and adaptive control method, a multiple adaptive finite time super-twisting control method (MAFTSTC) is proposed, which rarely relies on the information of the formation model. The purpose of the robust controller designed in the position loop and the attitude loop is to ensure that quadrotor formation tracks the desired trajectory and maintains formation configuration in finite time. The closed-loop system stability of the novel control method is verified through Lyapunov theory. Finally, compared with traditional finite time convergence (FTC) method and non-robustness control (NRC) method, the simulation results illustrate the effectiveness of the proposed control method.

We present a general framework for risk semantics on Signal Temporal Logic (STL) specifications for stochastic dynamical systems using axiomatic risk theory. We show that under our recursive risk semantics, risk constraints on STL formulas can be expressed in terms of risk constraints on atomic predicates. We then show how this allows a (stochastic) STL risk constraint to be transformed into a risk-tightened deterministic STL constraint on a related deterministic nominal system, enabling the application of existing STL methods. For affine predicate functions and a (coherent) Distributionally Robust Value at Risk measure, we show how risk constraints on atomic predicates can be reformulated as tightened deterministic affine constraints. We demonstrate the framework using a Model Predictive Control (MPC) design with an STL risk constraint.

Due to the increase in wind power accessing power system in recent years, the low-frequency oscillation of power system is more complicated. Therefore, a new adaptive switching control method combined with a nonlinear system is proposed for the complex operating conditions of the grid-connected power system. Firstly, this method decomposes the low-frequency oscillation control problem of the power system into two parts, which are the linear system control and the nonlinear system control. Then, in the linear system control part, the iterative identification method is used to control this part. In the nonlinear system control part, the nonlinear term is decomposed into a combination of the previous beating measurable and the nonlinear estimation increment. Secondly, the nonlinear system is divided into three different operating conditions by the nonlinear estimation increment. Thus, four different adaptive controllers are obtained. Then, the designed controller is switched according to the controlled object with four controllers by the switching function, which not only ensures the stability of the closed-loop system, but also improves the control performance of the controller under different operating conditions. Finally, the feasibility and effectiveness of this method are verified, and the controller can fit the control efficiency of the real power system well.

This paper proposes an adaptive non-linear distortion noise control method for low-priority systems under spectrum sharing environments. The proposed method adaptively controls distortion noise power of a transmitter to improve reception quality of shared bands with transmission power control. The distortion power is reduced to meet a required transmission quality. In this paper, the spectrum property and BER performance with the method are evaluated by computer simulations. The results confirm that the proposed method can effectively reduce the distortion power and adaptively control transmission SNRs according to the required transmission quality.

The paper proposes an adaptive control method for a water chiller unit in a HVAC system. Based on the minimum variance evaluation, the adaptive control method was used to realize better control of the water chiller unit. To verify the performance of the adaptive control method, the proposed method was compared with an a conventional PID controller, the simulation results showed that adaptive control method had superior control performance to that of the conventional PID controller.

Plug-in fuel cell electric buses (PFCEBs) have the potential to effectively reduce energy usage through efficient energy management. However, when PFCEBs operate on non-fixed bus routes, noise factors such as driving cycle, stochastic vehicle mass, and driving distance make it challenging to achieve optimal fuel economy. To tackle this issue, this study introduces an adaptive energy management control method for PFCEBs operating on non-fixed bus routes, considering the aforementioned factors. Firstly, an adaptive energy management control method based on the algorithm of Pontryagin’s Minimum Principle (PMP) is proposed to be integrated into the energy management system, allowing for real-world adaptive control. Secondly, a Design for Six Sigma (DFSS) methodology is proposed to address the noise disturbance problem caused by the driving cycle, stochastic vehicle mass, and driving distance. The main objective of DFSS is to find the “flat” zone of the design space (constituted by co-state and normalized distance), whilst minimizing the mean hydrogen consumption and its standard deviation. Validation results from Monte Carlo Simulation (MCS) demonstrate the effectiveness and applicability of the DFSS methodology in the energy management design for PFCEBs operating on non-fixed bus routes. Furthermore, simulation results indicate that the proposed robust co-state design method can achieve fuel economy comparable to that of an off-line PMP control strategy. In comparison to the rule-based strategy, the fuel economy improves by an average of 18.01%, 20.06%, and 18.91% for bus route 1, 2, and 3, respectively.