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In this paper, an adaptive fixed time parameter estimation and synchronization control scheme is proposed for multiple robotic manipulators with unknown parameters and lumped disturbances. A mean coupling synchronization error is defined by averaging all the tracking errors of multiple robotic manipulators, and an adaptive fixed time synchronization control scheme is proposed to guarantee that both the tracking error and the synchronization error can simultaneously converge to zero within a fixed time, which means that the upper bound of the settling time is independent of the initial conditions. Moreover, an adaptive parameter estimation law is developed through constructing auxiliary filtered variables to achieve the accurate estimation of the unknown system parameters. Comparative simulations are provided to validate the effectiveness of the proposed scheme.

In this paper, an adaptive sliding mode control incorporating with a nonlinear disturbance observer is proposed for a class of second-order nonlinear systems with unknown parameters and matched lumped disturbance. A double hyperbolic reaching law with chattering-free characteristic is first constructed, and an adaptive sliding mode controller is designed to guarantee the satisfactory tracking performance and fast error convergence. The chattering problem is eliminated by means of the infinitely approaching equilibrium point instead of crossing it by employing two hyperbolic functions. In order to identify the unknown parameters accurately, an adaptive parametric update law is presented through constructing a set of auxiliary filtered variables. Then, a nonlinear disturbance observer is proposed to improve the tracking performance and compensate for the lumped disturbance including perturbations and uncertainties. The stability analysis is provided by the Lyapunov stability theory, and a numerical simulation on a mass-spring damper system is given to demonstrate the effectiveness of the proposed method.

This paper proposes an adaptive tracking control scheme for the uncertain model flexible joint manipulators with output constraints to achieve satisfactory performance. A time-varying arctangent barrier Lyapunov function (TABLF) is first designed to ensure the system output constraints are not violated. By combining backstepping, the negative feedback controller is designed to guarantee the errors remain within the allowable range of the constraints. The unknown dynamic observers are employed to approximate the uncertainties in the system, and a tracking differentiator (TD) is applied to obtain the differentiation of the virtual control laws in the back stepping design. Finally, the comparative simulations are given to illustrate the effectiveness of the proposed scheme.

Power management in wireless sensor networks (WSNs) is crucial due to the limited battery energy. Energy harvesting wireless sensor networks (EH-WSNs) can extract energy from environmental sources to break this limitation. Consistent with the nodes’ energy collection and workload, adaptive energy management strategies are used to improve energy utilization, thereby making the system perform efficiently and operate sustainably. Solar-powered sensor nodes in the network are grouped into clusters. Data collected by cluster members are sent to the cluster head and finally transmitted to the base station. The goal of the whole network is to maintain an energy neutrality state and to maximize the effective data throughput in the network. This paper proposes an adaptive power management method based on reinforcement learning (RL) algorithms for the solar-powered sensor networks. RL methods are applied for adjusting the transmission rate for each node to keep harvested energy more balanced among the whole clustered network. The Q-learning and the latest proximal policy optimization (PPO) methods are applied to maximize transmitting sensing data over the whole network. Experiments show that both methods are suitable for performing energy management by better adjusting the duty cycle of each node to achieve the overall application goal of maximizing information transmission.

In this paper, a speed tracking and synchronization control approach is proposed for a multimotor system based on fuzzy active disturbance rejection control (FADRC) and enhanced adjacent coupling scheme. By employing fuzzy logic rules to adjust the coefficients of the extended state observer (ESO), FADRC is presented to guarantee the speed tracking performance and enhance the system robustness against external disturbance and parametric variations. Moreover, an enhanced adjacent coupling synchronization control strategy is proposed to simplify the structure of the speed synchronization controller through introducing coupling coefficients into the conventional adjacent coupling approach. Based on the proposed synchronization control scheme, an adaptive integral sliding mode control (AISMC) is investigated such that the chattering problem in conventional sliding mode control can be weakened by designing an adaptive estimation law of the control gain. Comparative simulations are carried out to prove the superiorities of the proposed method.

This paper proposes a novel continuous reaching law for chattering-free sliding mode control by using two hyperbolic functions with similar changing rate and opposite amplitude characteristics. The first function is an inverse hyperbolic sine function, which can guarantee the fast convergence as the initial value of the sliding mode variable is far away from the equilibrium. When the sliding mode variable is approaching to zero, the second hyperbolic tangent function can ensure the sliding mode variable be infinitely close to zero rather than cross the zero. With the proposed reaching law, the fast convergence and chattering-free property can be both guaranteed, and the satisfactory convergence performance of the reaching phase is achieved with an approximation method. Moreover, the steady state error bound is analyzed in details when considering external disturbances. A simple example is provided to demonstrate the effectiveness of the proposed method.

In this paper, a finite-time neural funnel control (FTNFC) scheme is proposed for motor servo systems with unknown input constraint. To deal with the non-smooth input saturation constraint problem, a smooth non-affine function of the control input signal is employed to approximate the saturation constraint, which is further transformed into an affine form according to the mean-value theorem. A fast terminal sliding mode manifold is constructed by using a novel funnel error variable to force the tracking error falling into a prescribe boundary within a finite time. Then, a simple sigmoid neural network is utilized to approximate the unknown system nonlinearity including the saturation. Different from the prescribed performance control (PPC), the proposed finite-time neural funnel control avoids using the inverse transformed function in the controller design, and could guarantee the prescribed tracking performance without knowing the saturation bounds in prior. The effectiveness and superior performance of the proposed method are verified by comparative simulation results.

In this paper, a new fault-tolerant approach based on improved particle swarm optimization (IPSO) is proposed to automatically locate the transient voltage disturbance source (TVDS) for smart grid with distributed generation (DG) integration. We first analyze the influence of the DG integration on the TVDS direction-judgments. Two new credibility indexes, the monitoring-credibility and the partial-credibility, are defined to measure the reliability of direction-judgment result at each power quality monitor (PQM) with consideration of multiple factors, including DG integration, disturbance intensity and fluctuation characteristic. By using these credibility indexes and a newly defined search space, a heuristic searching approach, called IPSO, is then proposed to obtain the optimal solution of the TVDS location. Simulation study is carried out on the IEEE 34 node test feeder, and the results demonstrate that the proposed approach has significantly improved fault-tolerant capability with satisfatory convergence speed.

This paper proposes an adaptive tracking control scheme for voltage-driven robotic manipulators with output constraints to achieve satisfactory tracking performance. To overcome the design difficulty from output constraints, a tangent boundary Lyapunov function (tBLF) is first introduced to keep the errors remain within the allowable range of the constraints. Then, RBF neural networks are employed to approximate the uncertainties in the system, and a tracking differentiator (TD) is applied to obtain the differentiation of the virtual control laws in the back stepping design. Finally, the comparative simulations are given to illustrate the effectiveness of the proposed scheme.

In this paper, a full-order sliding mode control based on extended state observer (FSMC+ ESO) is proposed for high-order nonlinear system with unknown system states and uncertainties. The extended state observer (ESO) is employed to estimate both the unknown system states and uncertainties so that the restriction that the system states should be completely measurable is relaxed, and a full-order sliding mode controller is designed based on the ESO estimation to overcome the chattering problem existing in ordinary reduced-order sliding mode control. Simulation results show that the proposed method facilitates the practical application with respect to good tracking performance and chattering elimination.

In this paper, an adaptive synchronization control method is proposed for dual-manipulator systems to guarantee the satisfactory synchronization and tracking performance on the basis of effective finite time parameter estimation. The mean-coupling synchronization scheme is employed to obtain the mean-coupling error, and a fast finite time sliding mode surface and a auxiliary control variable are presented based on the position tracking error and mean-coupling error to facilitate the control design. Then, the fast terminal sliding mode synchronization controller is proposed to ensure that both the tracking error and the synchronization error can converge to zero in finite time. Moreover, an adaptive parameter estimation law is developed by the extracted parameter error information to accurately identify the unknown system parameters. Comparative simulations are provided to validate the effectiveness of the proposed method.

For power management in the energy harvesting wireless sensor networks (EH-WSNs), it is necessary to know in advance the collectable solar energy data of each node in the network. Our work aims to improve the accuracy of solar energy predictions. Therefore, several existing prediction algorithms in the literature are surveyed, and then this paper proposes a solar radiance prediction model based on a long short-term memory (LSTM) neural network in combination with the signal processing algorithm empirical mode decomposition (EMD). The EMD method is used to decompose the time sequence data into a series of relatively stable component sequences. For improving the prediction accuracy further by utilizing the current day solar radiation profile in one-hour-ahead predictions, similar solar radiation profile data were selected for training LSTM neural networks. Simulation results show that the hybrid model achieves better prediction performance than traditional prediction methods, such as the exponentially-weighted moving average (EWMA), weather conditioned moving average (WCMA), and only LSTM models.

An input-adaptive self-oscillating boost converter is proposed for wide-range ultralow voltage input LED driving with tight input regulation, low cost, high efficiency, and high reliability. It consists of a self-oscillation unit, a rectification unit, and an input-adaptive peak-current control unit with a blanking time control branch and an open-circuit protection branch. In this paper, its operation principle including an input-adaptive peak-current control scheme with a blanking time control strategy and a method to provide multiple protection functions is introduced. For experimental verification, a prototype boost converter was implemented by using several commonly used cheap components and devices. The steady-state and dynamic results obtained in the experiments show that it could start up successfully by itself, could operate with tight input regulation and high efficiency, and had short-circuit and open-circuit fault-tolerant characteristics.

In this paper, a composite integral terminal sliding mode surface is developed, and an adaptive synchronization control scheme is proposed accordingly for multiple robotic manipulators with actuator saturation. By combining the terminal sliding mode with integral terms, the presented scheme is able to reduce the static tracking errors and improve the synchronization performance. Moreover, the system uncertainties, external disturbances and the effect of input saturation are compensated by designing the adaptive update laws. The finite-time stability of the whole system is proved via the Lyapunov stability analysis. Finally, simulation results are given to show the effectiveness and superior performance of the proposed method.

In this paper, an adaptive on-line parameter identification law is investigated by the extracted parameter error information for the position servo system, and the improved exponential reaching law is employed to design the controller to enhance the tracking performance. The chattering problem is weakened by using the proposed method. Moreover, several adaptive parameters are adopted to suppress the effect of saturation when the limited input affects the system tracking performance. Finally, simulations are conducted to verify the effectiveness of the proposed method.

A finite-time tracking control scheme is proposed in this paper based on the terminal sliding mode principle for motor servo systems with unknown nonlinear dead-zone inputs. By using the differential mean value theorem, the dead-zone is represented as a time-varying system and thus the inverse compensation approach is avoided. Then, an indirect terminal sliding mode control (ITSMC) is developed to guarantee the finite-time convergence of the tracking error and to overcome the singularity problem in the traditional terminal sliding mode control. In the proposed controller design, the unknown nonlinearity of the system is approximated by a simple sigmoid neural network, and the approximation error is diminished by employing a robust term. Comparative experiments on a turntable servo system are conducted to show the superior performance of the proposed method.

In this article, an adaptive neural dynamic surface sliding mode control scheme is proposed for uncertain nonlinear systems with unknown input saturation. The non-smooth input saturation nonlinearity is firstly approximated by a smooth non-affine function, which can be further transformed into an affine form according to the mean value theorem. Then, one simple sigmoid neural network is employed to approximate the uncertain nonlinearity including the input saturation, and the approximation error is estimated using an adaptive learning law. Virtual controls are designed in each step by combing the dynamic surface control and integral sliding mode technique, and thus the problem of complexity explosion inherent in the conventional backstepping method is avoided. With the proposed control scheme, no prior knowledge is required on the bound of input saturation, and comparative simulations are given to illustrate the effectiveness and superior performance.

In this paper, a universal control scheme is investigated for a class of nonlinear systems with unknown parameters and nonlinearities like friction, dead-zone, etc. First of all, an adaptive finite-time update law is developed to estimate the unknown parameters by introducing the auxiliary filtered variables for the system states and regressor matrix. Then, a universal controller is proposed based on the finite-time parameter estimation to guarantee the globally asymptotic stability of the system. Finally, a motor servo control system with nonlinear friction is given as an example to show the effectiveness of the proposed method.

In this paper, an improved adjacent coupling control scheme combined with integral sliding mode control (ISMC) and active disturbance rejection control (ADRC) techniques is proposed for multiple motors synchronization. The whole synchronous system is divided into two parts. For the first part, a speed synchronization controller is designed based on an integral sliding mode control (ISMC), and an improved adjacent coupling scheme is developed by introducing coupling cofficients, which can enhance the synchronous performance of multiple motors. For the second part, an active disturbance rejection control (ADRC) technique is employed for the design of the speed tracking controller to guarantee the strong robustness and high precision. Simulation results show the effectiveness of the proposed control method.

This paper is concerned with the problem of H ∞ control for uncertain two-dimensional (2-D) state delay systems described by the Roesser model. Based on a summation inequality, a sufficient condition to have a delay-dependent H ∞ noise attenuation for this 2-D system is given in terms of linear matrix inequalities (LMIs). A delay-dependent optimal state feedback H ∞ controller is obtained by solving an LMI optimization problem. Finally, a simulation example of thermal processes is given to illustrate the effectiveness of the proposed results.

A loss-adaptive self-oscillating buck converter is proposed for high-efficiency and low-cost LED driving. It consists of a self-oscillating unit composed of bipolar junction transistors (BJTs)-type self-oscillating unit, a loss-adaptive BJT driving unit, and a low-loss peak-current sensing unit. In this paper, its operation principle including a loss-adaptive BJT-driving scheme and a low-loss peak-current sensing method is introduced. For experimental verification, a prototype LED driver was implemented with some cheap components and devices for a 24-V lighting system to drive up to six LEDs. The test results show that the prototype LED driver could successfully start up itself and operate highly efficiently in steady state. In order to improve the performance of the proposed buck converter, a useful pulse-width modulation (PWM) LED dimming function is discussed for the future study.

In this paper, the parameter identification and control problem are investigated for a mechanical servo system with LuGre friction. First of all, an intelligent glowworm swarm optimization (GSO) algorithm is developed to identify the friction parameters. Then, by using a finite-time parameter estimate law and nonlinear sliding mode technique, an adaptive nonlinear sliding mode control (NSMC) based on GSO is designed to speed up the parameter convergence and to decrease the overshoot and steady-state time in control process. Finally, comparative simulations are given to show that the proposed parameters identification technique and adaptive NSMC law are both effective with respect to fast convergence speed and high tracking accuracy.

In this paper, a backstepping funnel control (BFC) is proposed to achieve a prescribed tracking performance for robotic manipulator systems with unknown input dead zone. According to the differential mean value theorem, the dead zone inverse compensation approach is avoided by representing the dead zone as a linear time-varying system. Without constructing the complex barrier Lyapunov function, a new constraint variable is employed and the tracking error will be forced to fall into prescribe boundaries. A simple sigmoid neural network is utilized to approximate the system uncertainties and the calculation of derivative terms generated by the recursive steps of traditional backstepping control can be avoided. With the proposed scheme, no prior knowledge is required on the bound of input dead zone, and the convergence of the position tracking error is guaranteed via the Lyapunov synthesis. Comparative simulation examples are given to illustrate the effectiveness of the proposed method.

We consider the general decentralized stabilization problem of linear time-invariant, large-scale systems with delay interconnections. A sufficient condition for the stabilizability via a decentralized state feedback control law is derived, it is shown that this condition is equivalent to the existence of positive definite symmetric matrix solutions to a system of (parameters-dependent) Riccati equations at the subsystem level. Also, a construction for the decentralized stabilizing control law is given in terms of the solutions to the Riccati equations. A example is given to illustrate the proposed method, and a comparison with the existing methods is made.

The inverse kinematics of 6-DOF manipulator is one of the main concerns and difficult problem in robotics. This dissertation presents a new method for the inverse kinematics of 6-DOF manipulator based on dual quaternion which uses dual quaternion to respect both rotation and translation in a single transformation vector. So all the revolute joints of 6-DOF manipulator can be denoted by dual quaternion. The simulation was provided to verify the feasibility and precision of the proposed new method.

In the Permanent Magnet Synchronous Motor(PMSM) field-oriented control system, the information of rotor position and speed are necessary in order to achieve high performance. This method is based on sliding mode observer theory using Lyapunov stability criteria. The proposed estimation method is implemented using a stator flux/current and a modified back Electromotive Force(EMF) observer connected in cascade. The flux/current observer based on sliding mode techniques ensures stator currents convergence while the modified back EMF observer provides accurate rotor position estimation at a wide range of motor speeds. Also the variations of the parameters, such as resistance and inductance variations are analyzed. Simulative results show that the observer is robust to parameter variation and able to observe the position of rotor so as to achieve sensorless vector control of PMSM.

A full-order sliding mode control based on a fuzzy extended state observer is proposed to control the uncertain chaos in the permanent magnet synchronous motor. Through a simple coordinate transformation, the chaotic PMSM model is transformed into the Brunovsky canonical form, which is more suitable for the controller design. Based on the fuzzy control theory, a fuzzy extended state observer is developed to estimate the unknown states and uncertainties, and the restriction that all the system states should be completely measurable is avoided. Thereafter, a full-order sliding mode controller is designed to ensure the convergence of all system states without any chattering problem. Comparative simulations show the effectiveness and superior performance of the proposed control method.

This paper proposes a novel approach for AC servo system control of electrical injection molding machine. Based on the permanent-magnet synchronous motor (PMSM) vector control, position servo system is achieved using repetitive control (RC), and then the controller is designed in order to tracking the periodic reference. Not only the principle and arithmetic of RC are detailed, but also the mathematic model of PMSM and the design process are included in this paper. At last, the simulation is presented for the given periodic reference of position. And the effectiveness, briefness, and feasibility of the proposed approach are demonstrated by the results of simulation

The new approach of the constant-tension control of rolling machine is presented in this paper, On the premise of the close-loop control of the induction motor torque, the torque of the induction motor can adjust along with the change of the rolling machine diameter automatically, so the constant-tension control is realized. The rotor speed signal adopted in the system is derived from the indirect measurement based on induction motor theory. The approach employs recursive least squares (RLS) to estimate rotor speed in real time. The accuracy of estimated rotor speed is independent of rotor resistance's value. The results show that the system has good static performance. The effectiveness of the proposed strategy is approved.

The direct and convenient approaches for modeling in computer-aided design are studied. An algorithm of local deformation and quick algorithm of virtual sculpture for triangulate mesh are proposed. With our software system and Phantom, modeling with force feedback in virtual reality environment is realized. A convenient approach for stylists to design industrial products is provided.

This paper considers the problem of constructing a reliable tracking controller such that the output of resulting of closed-loop systems asymptotically tracking reference output. The uncertain linear time-delay system under consideration contains a more practical and general faults model of actuator. The main results are a sufficient condition for the existence of such a controller and the design method of this controller. A simulation example is given to illustrate the efficiency and necessity for reliable tracking control.

On the basis of artificial neural network theory, this paper has put forward a new kind of speed observer. The observer is good in robustness of motor parameter changes, high in the observation precision. The speed identification convergence is fast, especially keeping higher precision in low speed. At the same time, the paper puts forward a new method of dead-time compensation, applies the new observer and a new method to direct torque control (DTC) system, which have improved the performance of DTC in an all-round way. The simulation results show that this control system has good static property and dynamic performance.

Process knowledge discovery is a significant research field in enterprise management. Based on process knowledge complexity and specialty, a process knowledge discovery model is presented in this paper according to multi-agent technology for full life-orientation of the product which is a solid foundation for an enterprise seeking optimal process technology. At the same time, according to multi-agent technological characters, the whole mining process has become more intelligent.

A design method of stabilizing controller for a class of robot manipulator is proposed. The synthesis problem of the controller via memoryless state feedback is solved based on LMI. A numerical example illustrates the results.