Model-following control of linear proper non-minimum-phase systems

The design of model-following control (MFC) foT (A, B, C, D) quadruple systems is considered. The main result is that a non-minimum-phase (NMP) plant can be controlled so that it will follow a prespecificd model in the sense of optima. The characteristics of the proposed control mechanism are: (1) the control design problem appears to be a two-point boundary-value problem (TPBVP); (2) this TPBVP can be easily solved by a modified Runge-Kutta method; (3) the existence of the control for a typical NMP plant is proved; (4) some poor properties of a NMP plant are also improved. Two examples are used to verify the control mechanism presented     

Active disturbance rejection and Lyapunov redesign approaches for robust boundary control of plate vibration

In this paper, two approaches, namely active disturbance rejection control (ADRC) and Lyapunov redesign, are utilised to stabilise the vibration of a boundary-controlled flexible rectangular plate in the presence of exogenous disturbances. Based on ADRC, an estimation/cancellation strategy is applied where disturbance is estimated online by an extended state observer (ESO) and cancelled by injecting the output of ESO into the feedback loop. By the Lyapunov redesign, on the other hand, the control law intended for a nominal system is redesigned by adding a (discontinuous) control component that makes the system robust to large uncertainties. Both control algorithms are designed directly based on partial differential equation model of the plate so that spillover instabilities that are a result of model truncation are avoided. The established control schemes are able to stabilise the plate vibration by actuating and sensing only along the plate boundary while accounting for the dynamical effects of Gaussian curvature integral, in-plane membrane force and actuator mass. The stability of each control approach is proven using Lyapunov analysis. The efficacy of each proposed control is illustrated by simulation results.     

Control of a 2 DoF robot using a Brain–Machine Interface

In this paper, a non-invasive spontaneous Brain–Machine Interface (BMI) is used to control the movement of a planar robot. To that end, two mental tasks are used to manage the visual interface that controls the robot. The robot used is a PupArm, a force-controlled planar robot designed by the nBio research group at the Miguel Hernández University of Elche (Spain). Two control strategies are compared: hierarchical and directional control. The experimental test (performed by four users) consists of reaching four targets. The errors and time used during the performance of the tests are compared in both control strategies (hierarchical and directional control). The advantages and disadvantages of each method are shown after the analysis of the results. The hierarchical control allows an accurate approaching to the goals but it is slower than using the directional control which, on the contrary, is less precise. The results show both strategies are useful to control this planar robot. In the future, by adding an extra device like a gripper, this BMI could be used in assistive applications such as grasping daily objects in a realistic environment. In order to compare the behavior of the system taking into account the opinion of the users, a NASA Tasks Load Index (TLX) questionnaire is filled out after two sessions are completed.     

A multivariable predictive fuzzy PID control system

In this paper, a novel multivariable predictive fuzzy-proportional-integral-derivative (F-PID) control system is developed by incorporating the fuzzy and PID control approaches into the predictive control framework. The developed control system has two main units referred as adaptation and application parts. The adaptation part consists of a F-PID controller and a fuzzy predictor. The incremental control actions are generated by the F-PID controller. The controller parameters are adjusted with the predictive control approach. The fuzzy predictor provides the multi-step ahead predictions of the plant outputs. Therefore, the F-PID controller parameters are adjusted by minimizing the errors between the predicted plant outputs and reference trajectories over the prediction horizon. The fuzzy predictor is trained with an on-line training procedure in order to adapt the changes in the plant dynamics and improve the prediction accuracy. The Levenberg–Marquardt (LM) optimization method with a trust region approach is used to adjust both the controller and predictor fuzzy systems parameters. In the application part, an identical F-PID controller of the adaptation part is used to control the actual plant. The adjusted parameter values are transferred to this identical controller at each time step. The performance of the proposed control system is tested for both single-input single-output (SISO) and multiple-input multiple-output (MIMO) nonlinear control problems. The adaptation, robustness to noise, disturbance rejection properties together with the tracking performances are examined in the simulations.     

Robust fuzzy neural network control for linear ceramic motor drivevia backstepping design technique

This study presents a robust fuzzy-neural-network (RFNN) control system for a linear ceramic motor (LCM) that is driven by an unipolar switching full-bridge voltage source inverter using LC resonant technique. The structure and operating principle of the LCM are introduced. Since the dynamic characteristics and motor parameters of the LCM are nonlinear and time varying, a RFNN control system is designed based on the hypothetical dynamic model to achieve high-precision position control via the backstepping design technique. In the RFNN control system a fuzzy neural network (FNN) controller is used to learn an ideal feedback linearization control law, and a robust controller is designed to compensate the shortcoming of the FNN controller. All adaptive learning algorithms in the RFNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed RFNN control system is verified by experimental results in the presence of uncertainties. In addition, the advantages of the proposed control system are indicated in comparison with the traditional integral-proportional (IP) position control system     

Model reference adaptive control algorithms for industrial robots

In this paper some problems concerning the control of multifunctional manipulators (industrial robots) with high speed continuous movements are investigated. Although deterministic approaches to the control of robots, whose model are highly interconnected and non-linear, are known alternative approaches based on the Model Reference Adaptive System (MRAS) method of control are possible and useful. In the paper it is proved that a generalized MRAS control assures the convergence to a suitable reference model for a class of processes: the manipulator is shown to belong to such a class. The paper is completed by some applications evaluated by simulation.     

MPC-based Co-design of Control and Routing for Wireless Sensor and Actuator Networks

A wireless sensor and actuator network (WSAN) is a class of networked control systems. In WSANs, sensors and actuators are located in a distributed way, and communicate to controllers through a wireless communication network such as a multi-hop network. In this paper, we propose a model predictive control (MPC) method for co-design of control and routing of WSANs. MPC is an optimal control strategy based on numerical optimization. The control input is calculated by solving the finite-time optimal control problem at each discrete time. In the proposed method, a WSAN is modeled by a switched linear system. In the finite-time optimal control problem, a control input and a mode corresponding to a communication path are optimized simultaneously. The proposed method is demonstrated by a numerical example.     

二自由度无人直升机的非线性自抗扰姿态控制

无人机高性能姿态控制的难题之一是无人机系统模型通常无法精确建立且受到复杂外部干扰的作用.针对这一难题,本文提出了二自由度无人直升机姿态控制的非线性自抗扰控制设计方法.该方法的主要思想是将系统内部的未建模动态和外部干扰等不确定性因素作为"总扰动",利用输入输出信息在线观测,并在反馈控制环节对其进行补偿.本文发展了非线性扩...     

Real-time torque control using discrete-time recurrent high-order neural networks

This paper presents a discrete-time direct current (DC) motor torque tracking controller, based on a recurrent high-order neural network to identify the plant model. In order to train the neural identifier, the extended Kalman filter (EKF) based training algorithm is used. The neural identifier is in series-parallel configuration that constitutes a well approximation method of the real plant by the neural identifier. Using the neural identifier structure that is in the nonlinear controllable form, the block control (BC) combined with sliding modes (SM) control techniques in discrete-time are applied. The BC technique is used to design a nonlinear sliding manifold such that the resulting sliding mode dynamics are described by a desired linear system. For the SM control technique, the equivalent control law is used in order to the plant output tracks a reference signal. For reducing the effect of unknown terms, it is proposed a specific desired dynamics for the sliding variables. The control problem is solved by the indirect approach, where an appropriate neural network (NN) identification model is selected; the NN parameters (synaptic weights) are adjusted according to a specific adaptive law (EKF), such that the response of the NN identifier approximates the response of the real plant for the same input. Then, based on the designed NN identifier a stabilizing or reference tracking controller is proposed (BC combined with SM). The proposed neural identifier and control applicability are illustrated by torque trajectory tracking for a DC motor with separate winding excitation via real-time implementation.     

A novel IMC controller based on bacterial foraging optimization algorithm applied to a high speed range PMSM drive

This paper is a proposal of a modified internal model control based on an intelligent technique. The indirect field oriented control strategy (IFOC) is used as a permanent magnet synchronous motor (PMSM) drive platform. Neural network controller and estimator are respectively added to replace the conventional speed regulator and the speed encoder in the global drive scheme. A wide speed working range is considered and high speed mode is incorporated in the study testes. In the IFOC inner control loops, the commonly used synchronous frame conventional proportional plus integral (PI) controllers are replaced by two modified internal model control (IMC) regulators. Therefore, a method based on the bacterial foraging optimization (BFO) algorithm is performed to optimize and adjust the IMC low pass filter parameters. The robustness of the proposed PMSM sensorless drive scheme is confirmed by simulation tests in the MATLAB/SIMULINK. Moreover, a comparative evaluation results are illustrated to prove the effectiveness of the proposed control algorithm according to different controllers combinations.     

EP-based kinematic control and adaptive fuzzy sliding-mode dynamic control for wheeled mobile robots

This paper proposes a complete control law comprising an evolutionary programming based kinematic control (EPKC) and an adaptive fuzzy sliding-mode dynamic control (AFSMDC) for the trajectory-tracking control of nonholonomic wheeled mobile robots (WMRs). The control gains for kinematic control (KC) are trained by evolutionary programming (EP). The proposed AFSMDC not only eliminates the chattering phenomenon in the sliding-mode control, but also copes with the system uncertainties and external disturbances. Additionally, the convergence of trajectory-tracking errors is proved by the Lyapunov stability theory. Computer simulations are presented to confirm the effectiveness of the proposed complete control law. Finally, real-time experiments are done in the test field to demonstrate the feasibility of real WMR maneuvers.     

A reinforcement discrete neuro-adaptive control for unknown piezoelectric actuator systems with dominant hysteresis

The theoretical and experimental studies of a reinforcement discrete neuro-adaptive control for unknown piezoelectric actuator systems with dominant hysteresis are presented. Two separate nonlinear gains, together with an unknown linear dynamical system, construct the nonlinear model (NM) of the piezoelectric actuator systems. A nonlinear inverse control (NIC) according to the learned NM is then designed to compensate the hysteretic phenomenon and to track the reference input without the risk of discontinuous response. Because the uncertainties are dynamic, a recurrent neural network (RNN) with residue compensation is employed to model them in a compact subset. Then, a discrete neuro-adaptive sliding-mode control (DNASMC) is designed to enhance the system performance. The stability of the overall system is verified by Lyapunov stability theory. Comparative experiments for various control schemes are also given to confirm the validity of the proposed control.     

Intelligent integrated optimization and control system for lead–zinc sintering process

The lead–zinc sintering process (LZSP) is an important step in imperial smelting. This paper presents an intelligent integrated optimization and control system (IIOCS) for the LZSP. The optimization and control scheme has a hierarchical configuration. First, the requirements of process control and the configuration of the IIOCS are described. Then, models for predicting quantity and quality (Q&Q) are established using correlation and mechanism analysis, and are implemented by improved back-propagation neural networks. Based on the models, an integrated algorithm combining c-means clustering, genetic, and chaos approaches is employed to optimize the operating parameters of the process. Finally, the control of the process state is carried out by a distributed control system to control the Q&Q of the product.     

变参考轨迹下的鲁棒迭代学习模型预测控制

迭代学习模型预测控制是针对间歇过程的先进控制方法.它能通过迭代高精度跟踪给定参考轨迹,并保证时域上的闭环稳定性.然而,现有的迭代学习模型预测控制算法大多基于线性/线性化系统,且没有考虑参考轨迹变化的情况.本文基于线性参变系统提出一种能有效跟踪变参考轨迹的鲁棒迭代学习模型预测控制算法.首先,采用线性参变模型准确涵盖原始非线性系统的动态特性.然后,将鲁棒H∞控制与传统迭代学习模型预测控制相结合,抑制变参考轨迹带来的跟踪误差波动,通过优化线性矩阵不等式约束下的目标函数求得控制输入.深入分析了鲁棒迭代学习模型预测控制的鲁棒稳定性和迭代收敛性.最后,通过对数值例子和连续搅拌反应釜系统的仿真验证了所提出算法的有效性.     

基于U模型的滑模增强控制方法

提出了一种新的基于U模型的滑模增强控制（U_SM控制）方法,用于控制一类具有内部不确定参数和外部系统控制噪声/干扰的单输入单输出（SISO）动态系统。系统地介绍了U模型的定义和其在多项式和状态空间模型下的表现形式,基于一般闭环系统结构建立了相应的U_SM控制系统的设计框架,并逐步解释了其设计过程。实验方面,选择了简化的二自由度非线性直升机模型并使用U_SM控制方法和Matlab/Simulink平台对其进行了控制性能测试,并根据输出信号跟踪能力仿真结果论证了所提出方法的可行性。通过比较与讨论U_SM控制和U控制方法产生的不同仿真实验结果,验证了所提出方法的稳定性和鲁棒性。     

Non-linear state dynamics: Computational methods and manufacturing application

Stochastic optimal control problems are considered that are non-linear in the state dynamics, but otherwise are an LQGP problem in the control, i.e. the dynamics are linear in the control vector and the costs are quadratic in the control. In addition the system is randomly perturbed by both continuous Gaussian (G) and discontinuous Poisson (P) noise. The approach to the solution is by way of computational stochastic dynamic programming using a new enhancement with a least squares equivalent LQGP problem in the state to accelerate the iterative convergence, without adding to the state space computational complexity since the LQGP coefficient equations are independent of the state. General Gauss statistics quadratures are developed to numerically handle Poisson jump integrals. The methods are illustrated for a multistage manufacturing system (MMS) with sufficient realism in an uncertain environment, together with implementation procedures needed to modify the formal general theory.     

A non-linear controller design method for processes with saturating actuators

An optimization approach is proposed to derive non-linear model-based control laws for non-linear processes with actuator saturation non-linearities. The derived control laws induce a linear closed-loop process output response in the absence of input constraints (are input-output linearizing), are able to minimize the mismatch between the constrained and the linear unconstrained process output responses, and inherently include optimal directionality and windup compensators. Connections between the derived control laws and (a) already available, input-output linearizing, non-linear, control methods, (b) modified internal model control, and (c) model state feedback control, are established. The application and performance of the derived control laws are shown by examples.     

An inversion-based Feedback/Feedforward control for robust and precise payload positioning in gantry crane systems

This paper deals with Feedback/Feedforward (FB/FF) control of a gantry crane system intended for the transport of payloads that take values over a known interval. It is also assumed that the crane is affected by unmeasurable disturbances. A new 2DoF control architecture is proposed whose purpose is to speed up the horizontal payload transition while minimizing its oscillations. The main features of the control design procedure are as follows: (1) The output FB controller is designed to ensure robust closed loop stability and steady-state exact payload positioning; (2) the disturbance is estimated by means of an observer, and its transient effect is compensated through the FF action; and (3) the robust FF control action is given by the optimally weighted sum of the two contributions due to FF Plant Inversion (FFPI) and FF Closed Loop Inversion (FFCLI) control schemes.     

Suboptimal control of bunches of trajectories of discrete deterministic automaton time-invariant systems

The optimal control problem for discrete deterministic time-invariant automaton systems under parametric uncertainty is considered. The changes in the state (switching) of the system are described by a recurrence equation. The switching times and their number are not specified in advance—they are found by optimization. The initial state of the system is not known exactly. For this reason, the problems of finding the optimal on the average and the optimal guaranteeing (minimax) controls of bunches of trajectories are formulated. It is proposed to solve these problems using the separation principle, which assumes that the bunch is controlled using the control that is optimal for a specially chosen individual trajectory in the bunch. Generally, this control is suboptimal. Algorithms for the synthesis of suboptimal control of bunches are developed. It is proved that, in the linear-quadratic time-invariant problems, the suboptimal bunch control is optimal on the average or is the optimal minimax control, depending on the problem being solved.     

Nonsingular predefined-time dynamic surface control of a flexible-joint space robot with actuator constraints

To achieve predefined-time trajectory tracking control of a flexible-joint space robot(FJSR) with actuator constraints, a nonsingular predefined-time dynamic surface control scheme is developed. The input saturation caused by actuator constraints is addressed via the designed predefined-time anti-saturation compensator. On this basis, two different control laws are designed for such high-order nonlinear systems by utilizing the backstepping technique, and a novel nonlinear filter is constructed to filter the virtual control signals, thus avoiding the “differential expansion” phenomenon. Moreover, a singularity-free auxiliary function is designed to solve the singularity issue generated by the derivative of fractional power terms in the predefined-time control algorithm framework. The closed-loop system is proven to be semi-globally predefined-timely uniformly ultimately bounded (SGPTUUB) via constructing the suitable Lyapunov function. The difference and effectiveness of the two designed control laws are illustrated by the conducted simulations. Both of them allow the FJSR system to track the desired trajectory in a reasonably predefined time.