Search

Simulating biological intelligence has been proved to be an effective way to design intelligent robots, and simultaneously can solve the problems existing in machine learning methods. For creatures, their motor skills achieving is the first stage of learning. By combining two important cognitive elements: orientation and curiosity, this article proposes a new neurobiologically-inspired sensorimotor developmental learning method for the mobile robot. In this method, curiosity promotes robot’s exploration of the environment, while orientation enhances robot’s exploitation knowledge of the environment. The orientation cognitive algorithm is designed based on Skinner’s operant conditioning theory, and its rationality is proved. The balance of exploration and exploitation, which is a key problem for all the cognitive learning method, is solved in this method. The developmental learning process can avoid fixed sensorimotor mapping space problem, and help reduce learning waste as well as computing waste. All of the developmental learning method’s characters are finally verified via simulations on a virtual mobile robot.

In this paper, the problem of tracking control for uncertain singularly perturbed systems is studied. Firstly, the uncertain singularly perturbed system and the uncertain external system are combined to form an augmented system, and the optimal tracking problem is transformed into a new standard linear quadratic optimization problem. Then, based on the minimum principle, the minimum value of quadratic performance index and the tracking optimal controller of the system are obtained. For the controller with a feasible approximate solution of the generalized Riccati equation, the design method of the controller can be obtained in the form of linear matrix inequality (LMI). Finally, a numerical example is given to demonstrate the viability and rightness of the proposed conclusion.

In this paper, leader-follower formation control is studied to solve the trajectory tracking problem of multiple UAVs. The upper formation controller and the bottom trajectory tracking controller are designed based on active disturbance rejection control(ADRC). Virtual control variables are set in the design of the bottom controller to solve the under-actuated problem of the quadrotor. The extended state observer(ESO) is used to estimate and compensate the total disturbance in the design of the upper and bottom controller, which solves the problem of strong coupling of the quadrotor and improves the anti-interference and robustness of the system. Then the effectiveness of the designed controllers is verified by numerical simulations. Finally, an experiment of the designed controllers is carried out on Tello quadrotor UAV. The experimental results show the feasibility of the designed controllers.

A data-driven fault tolerant control for singularly perturbed systems is presented in this paper. It is founded on the data-driven full state observer and Youla controller parameterization. Based on the multiplex observer residual generator and the identification of the parity subspace, an extended internal model control (EIMC) structure is proposed and then the design of the parameters is given accordingly. Since the system is disturbed by faults, the controller proposed in this paper can accurately track the fault parameters. At last a numerical example is given to illustrate the feasibility and effectiveness of the method.

With the development of science and technology and the expansion of intelligent manufacturing technology, there are more and more automatic production lines in China, but the overall structure of the automatic production line is complex and the R & D investment cycle is long, resulting in low production efficiency. From the point of view of automatic production line structure and data virtual simulation, this paper studies the simulation control debugging system of automatic production line driven by PLC data. According to PLC data, the developer can carry out virtual commissioning of automatic production line and verify the timing of production line.

Due to the stochastic of multi-agent systems, it is difficult to achieve strict consensus. In this paper, consensus in the sense of probability is achieved by reducing the output error entropy of multi-agent system. Sliding mode controller is the core to keep the system stability and probability density function(PDF) compensator is used to reduce the chattering effect of sliding mode and compensate the random part of the system. Radial basis function neural network combined with the minimum entropy criterion is used to model the PDF compensator, and the output error entropy of the system is minimized through the training of weights, so as to optimize the control effect. Finally, the simulation results verify the effectiveness of the method.

Model Predictive Control (MPC) has accomplished several important achievements after decades of growth. Through the perspective of model predictive control for time-delay systems, the system output may deteriorate when there is time-delay or even the system will be unstable. This paper presents the performance of model predictive control methods for time-delay systems with state delays and control delay. The characteristics of the methods used to deal with various types of time-delay systems are illustrated and the benefits, drawbacks and future directions for research are highlighted. Finally, an example is given to test that model predictive control is one of the most successful methods of tackling system control problems.

This paper investigates power and rate control for uncertain wireless communication networks with nonlinear channel fading and time-varying delay. A new mathematical model of power and rate control system is derived. To compensate for the effects of nonlinear channel fading, time-varying delay, and parameter uncertainty, a power and rate controller is designed based on adaptive control technique. The performance of the closed-loop system is analyzed by using Lyapunov-Krasovskii functional and linear matrix inequality. Simulation results verify the effectiveness of the proposed controller.

This paper addresses congestion control for networks. The main focus is the modeling and compensation of nonlinear disturbance in the network, which is seldom considered in the literature. A new congestion control method is presented to compensate for the effects of nonlinear disturbance, uncertainty, time-varying delay, and input constraint. A state feedback congestion controller is designed via model predictive control approach. The stability of the closed-loop system is analyzed by using Lyapunov-Krasovskii functional. The effectiveness and feasibility of the proposed controller are verified by simulation results.

Vehicle networking cruise system is a complex system with large inertia and nonlinearity. It is difficult to establish an accurate model of the system because of the complex and changeable road conditions. To deal with this problem, a fuzzy cruise control method is proposed in this paper. The cruise system consists of two fuzzy inputs and one fuzzy output. The fuzzy inputs are the speed of the vehicle in the fleet and the distance of the adjacent vehicles, the output is the acceleration of the auto-driving vehicle. Based on the experience, 25 rules are formulated. The information between vehicles transmitted by vehicle to vehicle (V2V) communication. Under the premise of ensuring safety, the optimal tracking is ensured to the maximum extent and the utilization rate of road is improved. This paper relies on MATLAB/Simulink to simulate the vehicle movement in the simulation environment, and verifies that the fuzzy cruise control can achieve good result in the vehicle networking cruise system.

In this paper, the problem of sub-optimal output tracking control of singularly perturbed systems is considered. The necessary and sufficient condition for the existence of sub-optimal output tracking controller is given in terms of matrix inequalities, such that the tracking system achieves the asymptotic tracking and the minimum value of quadratic performance index can be obtained. Furthermore, the controller design method is presented in the form of LMI for the case that the controller has an approximate solution of the generalized Riccati equation. Finally, one numerical example is given to illustrate the correctness and feasibility of the proposed results.

The combined control of power and rate is very important in the wireless communication network (WCN). Because the power and rate model of WCN is difficult to be established, this paper studies the power and rate control of WCN from the perspective of fuzzy control. Firstly, theoretical analysis and mathematical model based on quality service priority are established. On this basis, a fuzzy control algorithm is proposed, which can ensure the system output has good tracking performance. Finally, the validity of the algorithm is confirmed by MATLAB/Simulink simulation.

An active adaptive fault tolerant controller design method for a linear singularly perturbed system with actuator faults is presented. It is founded on the adaptive fault estimation and Youla controller parameterization. The controller is adapted with the estimated fault, working in such way that the reconfigurable parameterized matrix in the designed Youla controller. The theory of R-controllability and R-observability is used to deal with the small perturbation parameters in singularly perturbed systems. Since actuators suffer from faults, the proposed controller can track the fault parameters adaptively. Finally, a numerical example shows the effectiveness and feasibility of the proposed method.

In this paper, a new robust model predictive control (MPC) approach is proposed for Cooperative Adaptive Cruise Control (CACC) of heterogeneous connected vehicle systems. Firstly, considering the limited range of vehicle-to-vehicle (V2V) communication, a new state space model of CACC heterogeneous vehicle system is established, which includes uncertainties, input constraints, and time-varying delays. Then, a robust model predictive cruise controller is designed based on an optimization with linear matrix inequality (LMI), and the stability of the closed-loop system is analyzed by using the Lyapunov-Krasovskii approach. The simulation result shows that the proposed controller has good performance.

This paper focuses on dealing with the nonlinearities in network congestion control. The nonlinearities are reflected in time-varying perturbations in the state and delayed state with unknown bounding coefficients, which have not been considered in the existing congestion control methods. A new system model is established for networks with unmatched nonlinearities, uncertainties and time-varying delays, and a new state feedback congestion controller is designed based on adaptive control. Finally, the stability and the performance of the closed-loop system are validated via a new Lyapunov-Krasovskii functional and linear matrix inequality. The effectiveness and feasibility of the proposed congestion controller are verified through simulations.

With the development of modern industry and information science and technology, modern industrial processes become more and more complex, which brings many challenges for model-based controller design. In this case, data-driven control is a complementary approach to model-based control. This paper proposes an improved model free adaptive control method based on compact format dynamic linearization technique for a class of nonlinear systems. Its control law consists of a time-varying proportional term, a time-varying integral term, and a time-varying derivative term. As a result, compared with the original method where there is only a time-varying integral term, it can strongly improve the dynamical performance of control systems. The effectiveness of the proposed method is demonstrated through simulation results.

This paper addresses cruise control of heterogeneous connected human driven vehicle systems. Firstly, from the aspect of control theory, a new state space model of cruise control system is established, which includes time-varying delays caused by driver reaction delays, feedback delays and wireless vehicle-to-vehicle communication delays. Then, a robust cruise controller is designed based on the H∞ control approach with linear matrix inequality (LMI), and the stability of the closed-loop system is analyzed by using the Lyapunov-Krasovskii approach. Finally, the effectiveness of the proposed controller is verified through simulation.

This paper focuses on modeling and compensation of multiple time delays in Internet congestion control, which has not been addressed in the literatures. A new mathematical model of the congestion control system for Internet with multiple state and input time delays is established, and a robust predictive congestion controller is presented. The controller is designed based on active queue management (AQM) and linear matrix inequality (LMI). The performance of the closed-loop system is analyzed via Lyapunov-Krasovskii approach and the effectiveness of the proposed controller is verified through simulation.

With the increase of the scale and complexity of the industrial process, the requirements for process safety and reliability are further improved. In order to detect the equipment failure accurately and timely, a fault detection method based on continuous hidden Markov model (CHMM) is proposed. The principal component analysis (PCA) method is used to extract the characteristic data of the process variable data, and the dynamic data is effectively tracked by the variable moving window. A new implementation statistic is proposed based on the conditional probability as the quantization index of the fault detection and the real — time threshold. CHMM fault detection. The test results of this method to Tennessee-Eastman (TE) chemical process show that the fault detection effect based on CHMM is better than PCA-based fault detection, indicating that CHMM-based fault detection method can more accurately detect faults.

In cellular mobile wireless networks, nonlinear channel fading is always a critical factor which will scientifically deteriorate the Quality of Service (QoS) for the users. However, it is difficult to describe the nonlinear channel fading by precise mathematical model because of its complexity. So, the control of wireless networks becomes an especial tough work. In this paper, a power and rate control system model is established which considers general nonlinearity of the channel fading which is seldom considered in the literature. And a neural network controller is proposed. The effectiveness and feasibility of the proposed controller are verified by simulation results. The presented method yields superior performance with shorter transient time and higher signal-to-interference-plus-noise ratio (SINR) than traditional predictive control scheme.

This paper investigates the data-driven networked control problem for a class of nonlinear systems with random packet dropouts in the feedback and forward channels. To compensate for the two-channel packet dropouts, a new data-driven networked predictive control method is proposed by using a dynamic linearization approach. In the controller design, a general dynamic linearization data model, i.e., full form dynamic linearization model, is employed instead of the compact form dynamic linearization model to predict future system outputs. Then based on such a model, a sequence of future control increment predictions are generated and transmitted to the actuator through networks to compensate for the random packet dropouts in both channels. The proposed method merely depends on the input and output data of the controlled plant, without using any explicit or implicit information about its structure and dynamics. Finally, the effectiveness of the proposed method is demonstrated through simulation results.

Existing power and rate control methods for wireless communication networks aim to maximize the system capacity and the network throughput, and attempt to increase the quality-of-service (QoS), but cannot guarantee the QoS. This paper presents a new power and rate control method for wireless communication networks, which is based on the priority of QoS, so the QoS can be guaranteed. A dual closed-loop power and rate controller is designed. The transmission rate is controlled by the outer loop, the target transmission rate is obtained (thus the optimal target signal-to-inference ratio (SIR) can relatively be determined) based on the priority of QoS. According to the target SIR, the transmit power is controlled by the inner loop to make the actual SIR track the target one. Simulation results show that the proposed power and rate control method has good control performance.

Habituation is non-associative learning mechanism of biological neurons. This paper studied the simplified description of associative learning mechanism, and based on the classical M-P (McCulloch — Pitts) neuron model, put forward study neurons model with the ability of habituation learning, including habituation neurons. At the same time, in this paper, based on the simplified description of Learning neurons, the mathematical model of habituation neurons is designed, and habituation neurons are applied to deep convolution neural networks. It has been verified by experiment that habituation neurons have typical habituation learning ability, and can optimize the performance of convolution networks.

For the constant linear system and a given quadratic performance index, study the optimal guaranteed cost analysis and control problems based on the linear matrix inequality (LMI) method. Through some lemmas, the conditions of the guaranteed cost state feedback control are converted into a problem of LMI solvability. By solving the LMI, the guaranteed cost controller parameters can be obtained. In turn, give an optimization algorithm with LMI constraints, to obtain optimal guaranteed cost control law. The results can be implemented by the Matlab, and the curve of control and state can be simulated by the Simulink. Finally, two numerical examples are used to verify the feasibility and the correctness of the result.

A class of linear singularly perturbed system and the optimal problem of the upper bound of the perturbed parameter based on the genetic algorithm are considered. Firstly, the problem of the asymptotically stability is studied in the term of Lyapunov stability theory based on the Linear Matrix Inequality (LMI). Then, the standard problem of the upper perturbed parameter to be optimized is presented. Thirdly, the optimization algorithm for the upper bound of the perturbed parameter in the linear singularly perturbed system is given based on the genetic algorithm. Lastly, two numerical examples are provided to demonstrate the effectiveness and feasibility of the proposed methods.

This paper concerns multiple time delays in power and rate control for wireless communication networks with external disturbance. Based on the mathematical state-space model established for the wireless communication networks with state delay and input delay, a compensation algorithm for multiple time delays of general form model is researched and it can be applied into our model. An optimal tracking algorithm is given and the optimal control law can be obtained by solving the matrix Riccati algebraic equation. Finally, simulation results show that the proposed algorithm has good control performance.

In this paper, the active fault tolerant control problem of networked systems with sensor fault is studied. The network-induced delay treated as the round-trip time (RTT) delay and additive sensor fault are taken into consideration. A networked predictive active fault tolerant control (NPAFTC) scheme based on networked predictive control approach is proposed to eliminate the effect of the sensor fault and compensate for the network-induced delay at the same time. The network-induced delays of the two data transmission channels are described by the RTT delays, which can be extended to represent or include other communication constraints, such as packet disorder and loss. A new fault estimation method that can estimate the sensor fault and the system state simultaneously based on Kalman filter is presented. Then a predictive controller based state-feedback, which can generate a sequence of predictive control signals to actively compensate for the RTT delay, is designed. A sufficient condition is derived for the stability of the resulting closed-loop system by using the switched system theory. Finally, numerical simulation demonstrates the effectiveness of the proposed method.

This paper investigates power and rate control for wireless communication networks. A new system model with nonlinear channel fading is established, while it was not considered in existing power and rate control methods. After that, a power and rate control method is presented based on the model predictive control (MPC), and the stability and the performance of the closed-loop system are guaranteed via Lyapunov approach with linear matrix inequality (LMI). The effectiveness of the proposed controller is verified through simulation results.

With the serious pollution of urban industry, sewage treatment has become an urgent problem to be solved. However, there are many problems in the traditional sewage treatment process, resulting in waste of energy, secondary pollution, and so on. In order to overcome the lack of artificial dosing in ozone water treatment system, this paper presents a fuzzy adaptive PID control algorithm for ozone water treatment systems with large time-delay and strong nonlinear characteristic. The Smith predictor is used to compensate for the large time-delay in chemical reaction and detection instrument, which can improve the response speed. The fuzzy adaptive PID controller is designed to deal with the strong nonlinearity. Simulation results are given to verify the effectiveness of the controller.

This paper presents a observer-based fault estimation method for a class of singularly perturbed systems subjected to parameter uncertainties and time-delay in state and disturbance signal with finite energy. To solve the estimation problem involving actuator fault and sensor fault for the uncertain disturbed singularly perturbed systems with time-delay, the problem we studied is firstly transformed into a standard \begin{document}$ H_\infty $\end{document} control problem, in which the performance index \begin{document}$ \gamma $\end{document} represents the attenuation of finite energy disturbance. By adopting Lyapunov function with the \begin{document}$ \varepsilon $\end{document} -dependence, a sufficient condition can be derived which enables the designed observer to estimate different kinds of fault signals stably and accurately, and the result obtained by dealing with small perturbation parameter in this way is less conservative. A novel multi-objective optimization scheme is then proposed to optimal disturbance attenuation index \begin{document}$ \gamma $\end{document} and system stable upper bound \begin{document}$ \varepsilon^* $\end{document} , in this case, the designed observer can estimate the fault signals better in the presence of interference when the systems guarantee maximum stability bound. In the end, the validity and correctness of proposed scheme is verified by comparing the error between the estimated faults and the actual faults.

This paper is concerned with power and rate control for wireless networks. In the control theoretic framework, a new power and rate control system model is derived for uncertain wireless networks with time-varying delays in both state and control input. A robust power and rate controller is presented, which is designed based on the H ∞ control approach with linear matrix inequality (LMI). Simulation results verify the effectiveness of the proposed algorithm.

In this paper, the multi-objected optimal control problem for singularly perturbed systems based on passivity is considered. First, the problem of the asymptotically stability under passive performance constraint is studied in terms of Lyapunov stability theory. Second, a sufficient condition for the existence state feedback controller is presented, so that the resulting closed-loop system is asymptotically stable and passive. Furthermore, in light of the passive controller, the linear quadratic optimal control problem is constructed, in which the weight matrices of the performance index can be calculated. Finally, two numerical examples are provided to demonstrate the effectiveness and correctness of the proposed results.

This paper investigates the problem of active fault tolerant control for networked control systems with network delay, package loss, as well as actuator fault. Treating the aforementioned communication constraints as a unified definition of view, called the round-trip time (RTT) delay. An adaptive fault observer is devised in the sensor to evaluate the state and the actuator fault. According to the estimation of the state and the actuator fault, a networked predictive fault tolerant control (NPFTC) strategy is introduced to eliminate both the effect of RTT delay and the actuator fault actively. A control prediction generator is designed by using an state feedback control law with the fault compensation form to compensate for the actuator fault, and a network delay compensator is designed in the actuator to compensate for the RTT delay. Then a stability and acceptable performance of NCSs is obtained. Eventually, some system simulations are supply to demonstrated the availability of the presented approach.

In this paper, the problem of fault detection for a dual-motor driving servo system is investigated. The dual-motor driving servo system is modeled as a linear system with disturbances, and the actuator and sensor faults are both considered. Based on the system model, an observer-based fault detection scheme which consists of a residual generator and a residual evaluator is proposed. An optimal residual generator based on the H ∞ /H ∞ index is designed to guarantee a suitable trade-off between sensitivity to faults and robustness against disturbances, and then the residual evaluator including a norm-based residual evaluation function and a threshold is presented to detect the occurrence of faults. Simulation results illustrate the effectiveness and applicability of the proposed method.