Adaptive multi‐dimensional Taylor network tracking control for SISO uncertain stochastic non‐linear systems

The path planning and tracking control scheme for unmanned vehicle using multi-dimensional Taylor network (MTN) is proposed. First, the reference path is designed by the A-star algorithm, which is a common method used as path planning. Second, design the MTN controller based on PID controller to control the unmanned vehicle tracking the reference path; and the improved BP algorithm is used as the learning of MTN controller. Finally, the simulation experiment of unmanned vehicle is given to verify the effectiveness of the proposed scheme. The simulation results show that the proposed scheme has optimal path planning and good tracking control performance.

MTN optimal control of MIMO non-affine nonlinear time-varying discrete systems for tracking only by output feedback

This paper presents an adaptive multi-dimensional Taylor network (MTN) control approach for a class of nonlinear systems with unknown parameters. MTN is employed to identify unknown nonlinear characteristics existing in the system, and then a novel adaptive MTN tracking control method is proposed, via backstepping technique. In the controller design, double adaptive laws are designed and appropriate Lyapunov functions are chosen to overcome the difficulties caused by the unknown parameters. The designed controller can guarantee that all the variables in the closed-loop systems are bounded and the tracking error can be arbitrarily small. Finally, simulation results are presented to verify the effectiveness of the proposed approach.

In this paper, the output feedback adaptive multi-dimensional Taylor network (MTN) tracking control for a class of nonlinear systems with unmeasurable states is investigated. Firstly, a nonlinear state observer is designed to estimate the unmeasurable states, and then an adaptive MTN-based output-feedback control approach is developed via backstepping technique. Secondly, in view of the simple structure of MTN, the controller based on MTN has the advantages of simple structure and fast calculation speed. Thirdly, in order to avoid the “differential explosion” problem inherited from the backstepping design, dynamic surface control (DSC) technique is introduced in the process of controller design. The results demonstrate that this scheme guarantees the stability and tracking performance of the closed-loop system. Finally, simulation examples are given to reveal the viability of the proposed method.

In this paper, a method of tank gun stability control system based on multi-dimensional Taylor network optimal control(MTN) is proposed. As the fact that tank itself has a strong nonlinear characteristic, which will lead to the traditional effects such as PID control is not satisfactory. Multi-dimensional Taylor network controller, which only relies on the output of the system, it combines the new controller structure with the classical feedback system architecture. It is not only suitable for analyzing the stability of closed-loop system, but also for control of the nonlinear systems with the mechanism known or unknown models. The virtual prototype of tank is used as the controlled plant to test the actual control effect of the MTN. And simulation results have shown the effectiveness of the proposed method.

SummaryIn this article, the issues of asymmetric input saturation and asymmetric time‐varying state constraints are integrated into nonlinear systems for the first time, and a new adaptive tracking control strategy is proposed based on the multi‐dimensional Taylor network (MTN) method. Firstly, by introducing a continuous auxiliary function, the input saturation is transformed into a smooth model with bounded error. Secondly, MTNs are employed to estimate nonlinear functions, asymmetric barrier Lyapunov functions (ABLFs) are constructed to make all state variables meet asymmetric constraints, and then a novel control scheme with simple structure is developed via backstepping. Thirdly, according to the Lyapunov stability theory, it is proved that all signals in the closed‐loop systems are bounded, and all state variables do not violate the constraints. Finally, three examples show the effectiveness of the proposed scheme.

This paper investigates an adaptive finite‐time tracking control strategy for nonlinear systems subject to input quantization and actuator faults, integrating prescribed performance control (PPC) with finite‐time control (FTC). Firstly, the prescribed performance function is used to ensure that the tracking error of the system meets the prescribed performance standards. Secondly, by utilizing the characteristics of quantized nonlinear sectors and the structural model of actuator faults, and allowing the total number of faults to be infinite, a novel finite‐time adaptive estimation technique can be devised to handle the effects caused by actuator faults and quantized inputs. Based on this, an adaptive finite‐time tracking controller is designed via backstepping, multi‐dimensional Taylor network(MTN), quantized feedback, and finite‐time Lyapunov stability theory. The control strategy not only ensures that all signals in the closed‐loop system are semi‐globally practically finite‐time stable (SGPFS), but also that the tracking error of the system can meet the transient and steady‐state performance criteria within finite time. Finally, simulation results verify the effectiveness of the proposed control scheme.

This paper focuses on the problem of multi-dimensional Taylor network (MTN)-based adaptive tracking control for a class of nonlinear systems with input constraints. As preliminaries, the saturation is first presented by a smooth function, and then a novel MTN-backstepping-based adaptive control method is designed on the basis of the Lyapunov stability theory. It is shown that the proposed control method can guarantee that all the signals in the closed-loop system are bounded and the tracking error converges to an arbitrarily small neighborhood around the origin. Finally, one example is given to illustrate the effectiveness of the proposed design approach.

The randomness and nonlinearity of stochastic nonlinear systems increase computational complexity and impede their tracking performance. However, randomness and nonlinearity are inevitable in practical applications, and the existing methods can hardly achieve the desirable control effect, especially in real-time control. For this end, a new network control strategy based on multi-dimensional Taylor network (MTN), whose design depends only on the system output, is put forward to solve the optimal output-feedback tracking problem of the SISO stochastic nonlinear systems. The network structure of the MTN is given first, and its approximation properties are proven. Based on the quadratic cost function design learning algorithm, the tracking error is minimized to update the controller parameters, and the desired tracking performance is obtained. Using the Lyapunov stability theorem, it is proved that the corresponding closed-loop system is bounded in the sense of probability and it can be ensured that the output tracking error converges to a small residual set around the origin in the sense of probability. An example is provided to illustrate the effectiveness of the proposed design approach. Comparative simulation study reveals that the proposed solution promises desirable real-time dynamic performance.

This paper investigates the adaptive decentralized control problem for a class of large‐scale nonlinear systems with finite‐time output constraints. In order to ensure that the tracking errors are constrained within a predefined boundary in finite time, a novel adaptive barrier Lyapunov function (BLF) control method is proposed by combining the modified finite‐time performance function (FTPF) in the first step of backstepping process. Besides, the mean value theorem and regulating functions are employed to handle the difficulties caused by interconnection functions in large‐scale systems. Subsequently, with the approximation performance of multi‐dimensional Taylor network (MTN), a MTN‐based adaptive decentralized tracking control scheme is developed to guarantee that the tracking errors satisfy the prescribed performance and all signals of the closed‐loop systems are bounded. Finally, the stability theory analysis and simulation results demonstrate the effectiveness of the proposed method.

In this paper, we propose a discrete-time infinite horizon nonlinear model predictive control (QIH-NMPC) based on state-space recurrent multi-dimensional Taylor network (RMTN). The purpose of this paper is to construct a state-space RMTN to be used as internal predictive model for QIH-NMPC and train this network by backpropagation through time (BPTT) algorithm. The multi-dimensional Taylor network (MTN) differs from the existing neural network (NN) on its structure and dynamic performance. RMTN gains advantages over recurrent neural network (RNN) on its training efficiency and ease of use, thus it reduces the on-line nonlinear optimization burden and enhances the efficiency of computation. The stability of closed loop system is guaranteed via Lyapunov stability theory. Finally, a numeric example is given to illustrate the effectiveness of the proposed design approach.

This paper proposes the work on the back stepping controller for the class of Discrete-time nonlinear system under amplitude & rate saturation, dead-zone constraints. A robust adaptive neural network (NN) control is developed for a general class of uncertain single-input-single output (SISO) discrete time nonlinear system with unknown system dynamics and input nonlinear ties. Here ( ) function is employed to saturate the amplitude and rate of system input. For input nonlinear ties like saturation and dead zone, discrete time SISO nonlinear system in combination with back stepping and Lyapunov synthesis is proposed for adaptive NN design with guaranteed stability. To reduce the effect of dead-zone a dead-zone compensator or dead-zone inverse is designed. Chebyshev Neural Networks (CNNs) are used to approximate the unknown nonlinear functions in system dynamics. New weight update laws are derived to make this scheme adaptive and show the stability of this scheme. Finally simulation results are presented in this paper to show the effectiveness of proposed scheme.

In this paper, an adaptive robust control is investigated in order to deal with the unmatched and matched uncertainties in the manipulator dynamics and the actuator dynamics, respectively. Because these uncertainties usually include smooth and unsmooth functions, two adaptive mechanisms were investigated. First, an adaptive mechanism based on radial basis function neural network (RBFNN) was used to estimate the smooth functions. Based on the Taylor series expansion, adaptive laws derive for not only the weighting vector of the RBFNN, but also for the means and standard derivatives of the RBFs. The second one was the adaptive robust laws, which is designed to estimate the boundary of the unsmooth function. The robust gains will increase when the sliding variable leave the predefined region. Conversely, they will significantly decrease when the variable approaches the region. So, when these adaptive mechanisms are derived with the backstepping technique and sliding mode control, the proposed controller will compensate the uncertainties to improve the accuracy. In order to prove stability and robustness of the controlled system, the Lyapunov approach, based on backstepping technique, was used. Some simulation and experimental results of the proposed methodology in the electrohydraulic manipulator were presented and compared to other control to show the effectiveness of the proposed control.

This paper is devoted to addressing the issue of adaptive fuzzy control for single-input single-output (SISO) fractional-order nonlinear systems with uncertainty. In order to avoid the “explosion of terms” caused by solving the fractional derivative of virtual signal, a new fractional-order dynamic surface control (DSC) method is proposed for fractional-order systems. Based on backstepping technique and Lyapunov direct method for fractional derivatives, the stability of closed-loop system is proved, which can ensure that the tracking error can converge to a small region. Finally, a simulation example is provided to reveal the effectiveness of the proposed control scheme.

Cooperative Non-Orthogonal Multiple Access (CNOMA) is a superior solution to increase the reliability of the far user. This technique treated the near user as relay for the far user or cell edge user which required significant power consumption. Hence hybrid simultaneous wireless information and power transfer (HS) protocol is considered with CNOMA downlink transmission to energize the relay operation without draining the battery of near user. Transmit antenna selection scheme is also integrated with the considered system. So this paper investigates the dominant factor among the fraction of block time for energy harvesting or power-splitting ratio in case of outage probabilities and channel capacity for the far user of the considered system with Single Input Single Output (SISO) scheme and Multiple Input Single Output (MISO) scheme as well. The simulation results of the channel capacities of the far user are compared with analytical results respectively.