Induction motor servo drive using robust PID-like neuro-fuzzy controller

A robust PID-like neuro-fuzzy controller, which has an ability to compensate for parameter variation, is proposed and applied to the speed control of the indirect vector-controlled induction motor. The controller gains are adjusted on-line using the tuning algorithm based on an artificial neural network (ANN). And a variable learning rate algorithm is proposed to improve the tracking performance while keeping the robustness. Simulation and experimental results confirm that good dynamic performance and high robustness to parameter variation and disturbance can be achieved by means of the proposed controller.     

Adaptive Precision Control of Permanent Magnet Linear Motors

This paper describes an adaptive control scheme developed for precision control of DC permanent magnet linear motors (PMLM). A sliding surface is defined on which a self‐tuning version of robust control capable of achieving tight set‐point regulation is developed, where the control gains are tuned using an adaptive algorithm. Parameter and states convergence properties of the adaptive controller are derived based on the control law formulated. Finally, simulation study and a real‐time experiment are provided to evaluate the performance of the proposed control system.     

Robust tuning for machine-directional predictive control of MIMO paper-making processes

This paper solves the controller tuning problem of machine-directional predictive control for multiple-input–multiple-output (MIMO) paper-making processes represented as superposition of first-order-plus-dead-time (FOPDT) components with uncertain model parameters. A user-friendly multi-variable tuning problem is formulated based on user-specified time domain specifications and then simplified based on the structure of the closed-loop system. Based on the simplified tuning problem and a proposed performance evaluation technique, a fast multi-variable tuning technique is developed by ignoring the constraints of the MPC. In addition, a technique to predict the computation time of the tuning algorithm is proposed. The efficiency of the proposed method is verified through Honeywell real time simulator platform with a MIMO paper-making process obtained from real data from an industrial site.     

Robust Adaptive Gain Higher Order Sliding Mode Observer Based Control-constrained Nonlinear Model Predictive Control for Spacecraft Formation Flying

This work deals with the development of a decentralized optimal control algorithm, along with a robust observer, for the relative motion control of spacecraft in leader-follower based formation. An adaptive gain higher order sliding mode observer has been proposed to estimate the velocity as well as unmeasured disturbances from the noisy position measurements. A differentiator structure containing the Lipschitz constant and Lebesgue measurable control input, is utilized for obtaining the estimates. Adaptive tuning algorithms are derived based on Lyapunov stability theory, for updating the observer gains, which will give enough flexibility in the choice of initial estimates. Moreover, it may help to cope with unexpected state jerks. The trajectory tracking problem is formulated as a finite horizon optimal control problem, which is solved online. The control constraints are incorporated by using a nonquadratic performance functional. An adaptive update law has been derived for tuning the step size in the optimization algorithm, which may help to improve the convergence speed. Moreover, it is an attractive alternative to the heuristic choice of step size for diverse operating conditions. The disturbance as well as state estimates from the higher order sliding mode observer are utilized by the plant output prediction model, which will improve the overall performance of the controller. The nonlinear dynamics defined in leader fixed Euler-Hill frame has been considered for the present work and the reference trajectories are generated using Hill-Clohessy-Wiltshire equations of unperturbed motion. The simulation results based on rigorous perturbation analysis are presented to confirm the robustness of the proposed approach.      

Nonlinear CSTR control system design using an artificial bee colony algorithm

This paper proposes a novel controller design method based on using artificial bee colony (ABC) algorithms for an unstable nonlinear continuously stirred tank reactor (CSTR) chemical system. Such CSTR process is highly nonlinear and its dynamic is significantly dominated by system parameters. It is a good challenge to access the controller design performance when the controller is applied in the CSTR control system. The commonly used proportional–integral-derivative (PID) controller is taken into account in this study, and tuning three PID control gains is carried out by the artificial bee colony algorithm. With the use of the optimal ABC algorithm, PID controller gains can be derived suitably by means of minimizing the cost function given in advance. Finally, several control operations are provided to confirm the feasibility and effectiveness of the proposed method. We also discuss the influence of algorithm initial conditions on the control performance with many different tests.     

Adaptive backstepping sliding mode control with tuning functions for nonlinear uncertain systems

An adaptive backstepping tuning functions sliding mode controller is proposed for a class of strict-feedback nonlinear uncertain systems. In this control design, adaptive backstepping is used to deal with unknown or uncertain parameters and the matching condition restricting the Lyapunov based design. The main drawback of the Lyapunov based adaptive backstepping which is the overparametrisation is eliminated by the tuning functions. The adaptive backstepping tuning functions design is combined with the sliding mode control in order to overcome quickly varying parametric and unstructured uncertainties, and to obtain chattering free control. The proposed controller not only provides robustness property against uncertainty but also copes with the overparametrisation problem. Experimental results of the proposed controller are compared with those of the standard sliding mode controller. The proposed controller exhibits satisfactory transient performance, good estimates of the uncertain parameters, and less chattering.     

Adaptive Nonlinear Stabilization Control for a Quadrotor UAV: Theory, Simulation and Experimentation

In this paper an adaptive control scheme along with its simulation, and its implementation on a quadrotor are presented. Parametric and non- parametric uncertainties in the quadrotor model make it difficult to design a controller that works properly in various conditions during flight time. Decentralized adaptive controller, which is synthesized based on improved Lyapunov-based Model Reference Adaptive Control (MRAC) technique, is suggested to solve the problem. The proposed control scheme does not need knowing the value of any physical parameter for generating appropriate control signals, and retuning the controller is not required for different payloads. An accurate simulation that includes empirical dynamic model of battery, sensors, and actuators is performed to validate the stability of the closed loop system. The simulation study simplifies implementation of the controller on our real quadrotor. A practical algorithm is proposed to alleviate and accelerate the tuning of controller parameters. The controller is implemented on the quadrotor to stabilize its attitude and altitude. Simulation and experimental results demonstrate the efficiency and robustness of the proposed controller.     

Fuzzy controllers gains tuning: a constrained nonlinear optimization approach

This paper presents a methodology for tuning the gains of fuzzy proportional-integral controllers where the concept of closed-loop control system performance is explicitly taken into account. The fuzzy controller gains are found by solving a nonlinear constrained optimization problem considering the system’s dynamics described by a nonlinear model and a set of constraints on the controller gains, control actions and outputs. Experimental results collected on a test-bed show the pertinence of using the proposed tuning technique.     

Distributed adaptive output consensus control of second-order systems containing unknown non-linear control gains

In this paper, we address the output consensus problem of tracking a desired trajectory for a group of second-order agents on a directed graph with a fixed topology. Each agent is modelled by a second-order non-linear system with unknown non-linear dynamics and unknown non-linear control gains. Only a subset of the agents is given access to the desired trajectory information directly. A distributed adaptive consensus protocol driving all agents to track the desired trajectory is presented using the backstepping technique and approximation technique of Fourier series (FSs). The FS structure is taken not only for tracking the non-linear dynamics but also the unknown portion in the controller design procedure, which can avoid virtual controllers containing the uncertain terms. Stability analysis and parameter convergence of the proposed algorithm are conducted based on the Lyapunov theory and the algebraic graph theory. It is also demonstrated that arbitrary small tracking errors can be achieved by appropriately choosing design parameters. Though the proposed work is applicable for second-order non-linear systems containing unknown non-linear control gains, the proposed controller design can be easily extended to higher-order non-linear systems containing unknown non-linear control gains. Simulation results show the effectiveness of the proposed schemes.     

Velocity‐free attitude stabilization with inertial vector measurements

This paper deals with the attitude stabilization problem of a rigid body, where neither the angular velocity nor the attitude is used in the feedback; only body‐referenced vector measurements are needed. The proposed control scheme is based on an angular velocity observer‐like system relying solely on vector measurements. The proposed controller ensures almost global asymptotic stability and provides some interesting performance properties through an appropriate tuning of the control gains. The performance and effectiveness of the proposed control scheme are illustrated via simulation results where the control gains are adjusted using a nonlinear optimization. Copyright © 2015 John Wiley & Sons, Ltd.     

Fractional order fuzzy-PID control of a combined cycle power plant using Particle Swarm Optimization algorithm with an improved dynamic parameters selection

The effectiveness of the Particle Swarm Optimization (PSO) algorithm in solving any optimization problem is highly dependent on the right selection of tuning parameters. A better control parameter improves the flexibility and robustness of the algorithm. In this paper, a new PSO algorithm based on dynamic control parameters selection is presented in order to further enhance the algorithm's rate of convergence and the minimization of the fitness function. The powerful Dynamic PSO (DPSO) uses a new mechanism to dynamically select the best performing combinations of acceleration coefficients, inertia weight, and population size. A fractional order fuzzy-PID (fuzzy-FOPID) controller based on the DPSO algorithm is proposed to perform the optimization task of the controller gains and improve the performance of a single-shaft Combined Cycle Power Plant (CCPP). The proposed controller is used in speed control loop to improve the response during frequency drop or change in loading. The performance of the fuzzy-FOPID based DPSO is compared with those of the conventional PSO, Comprehensive Learning PSO (CLPSO), Heterogeneous CLPSO (HCLPSO), Genetic Algorithm (GA), Differential Evolution (DE), and Artificial Bee Colony (ABC) algorithm. The simulation results show the effectiveness and performance of the proposed method for frequency drop or change in loading.     

基于改进粒子群算法的四旋翼自抗扰控制器优化设计

针对四旋翼飞行器自抗扰控制器参数较多,人工整定困难且难以得到最优控制效果的问题,提出一种基于改进粒子群算法的四旋翼自抗扰控制器优化方法。在设计了四旋翼飞行器的自抗扰控制器之后,将自抗扰控制器的参数作为粒子群中的粒子进行迭代寻优,同时在传统的粒子群算法基础上,参考遗传算法,对适应值不好的粒子进行交叉保优,以提高粒子的多样性,加快寻优速度。仿真结果表明,对比人工整定参数的控制器,优化后的控制器超调更小,调节时间更快。该方法能够解决四旋翼飞行器自抗扰控制器人工参数整定困难的问题,且优化后的控制器具有更好的控制效果。     

An adaptive controller for batch feed bioprocess application to lysine production

This paper presents an adaptive algorithm designed for batch feed bioprocess control, The proposed algorithm is based on a non-linear ‘grey box’ model, which is adapted on-line to the process behaviour by the estimation of a key parameter. Stability and convergence analysis of this algorithm is presented, and from this, a practical tuning method is given. The proposed algorithm is compared to a standard adaptive generalized predictive controller approach, and is shown to exhibit similar performance while being easier to use. A real application of this adaptive algorithm to a batch feed lysine process is presented. Good results are obtained, which show that this control scheme is a worthwhile alternative for batch feed bioprocess control.     

Evolutionary learning of fuzzy logic controllers and theiradaptation through perpetual evolution

This paper presents an adaptive control architecture, where evolutionary learning is applied for initial learning and real-time tuning of a fuzzy logic controller. The initial learning phase involves identification of an artificial neural network model of the process and subsequent development of a fuzzy controller with parameters obtained via a genetic search. The neural network model is utilized for evaluating trial fuzzy controllers during the genetic search. The proposed adaptive mechanism is based on the concept of perpetual evolution, where parameters of the fuzzy controller are updated at each time step with solutions extracted from a continuously evolving population of trials. There are two mechanisms that accommodate the real-time changes in the control task and/or the process into the continuous genetic search: a scheme that dynamically modifies the fitness evaluation criteria of the genetic algorithm, and an online learning of the neural network model used for evaluating the trial controllers. The potential of using evolutionary learning for real-time adaptive control is illustrated through computer simulations, where the proposed technique is applied to a chemical process control problem     

容错控制系统鲁棒H∞和自适应补偿设计

通过设计动态输出反馈控制策略研究线性时不变系统执行器故障下的鲁棒自适应容错H∞控制问题. 结合自适应技术和线性矩阵不等式(Linear matrix inequalities, LMI)技术, 设计一个控制策略同时实现系统的故障补偿控制和性能优化控制. 在设计中, 提出由自适应律在线调节控制增益方程补偿未知执行器故障和摄动; 并设计一个基于模式依赖李亚普诺夫方程的LMI条件解出控制参数及次优H∞性能. 所设计的动态输出反馈控制器可以处理一般执行器卡死故障, 并得到更少保守性的H∞性能指标. 此外, 一个更具挑战性的问题, 即通过自适应机构补偿故障致使系统多少性能退化得到论证. 所提方法的有效性由一个解耦线性化动态飞行器系统仿真验证.     

An experimental study on the optimization of controller gains for an electro-hydraulic servo system using evolution strategies

This paper deals with an experimental optimization problem of the controller gains for an electro-hydraulic position control system through evolution strategies (ESs)-based method. The optimal controller gains for the control system are obtained by maximizing fitness function designed specially to evaluate the system performance. In this paper, for an electro-hydraulic position control system which would represent a hydraulic mill stand for the roll-gap control in plate hot-rollings, the time delay controller (TDC) is designed, and three control parameters of this controller are directly optimized through a series of experiments using this method. It is shown that the near-optimal value of the controller gains is obtained in about 5th generation, which corresponds to approximately 150 experiments. The optimal controller gains are experimentally confirmed by inspecting the fitness function topologies that represent system performance in the gain spaces. It is found that there are some local optimums on a fitness function topology so that the optimization of the three control parameters of a TDC by manual tuning could be a task of great difficulty. The optimized results via the ES coincide with the maximum peak point in topologies. It is also shown that the proposed method is an efficient scheme giving economy of time and labor in optimizing the controller gains of fluid power systems experimentally.     

Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.

A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot.     

Nonfragile containment control of nonlinear multi-agent systems via a disturbance observer-based approach

In this article, we consider the nonfragile containment control problem of nonlinear multi-agent systems (MASs) with exogenous disturbance where the communication links among agents under consideration is directed. Firstly, based on relative output measurements between the agent and its neighbors, a disturbance observer-based control protocol is proposed to solve the containment control problem of MASs with inherent nonlinear dynamics and exogenous disturbances. Secondly, because of the additional tuning of parameters in the real control systems, uncertainties in the designing of observer and controller gains always occur, and as a result, an output feedback controller with disturbance rejection is conceived and the containment control problem of nonlinear MASs with nonfragility is thoroughly investigated. Then, depending on matrix transformation and inequality technique, sufficient conditions of the designed controller gains exist, which is derived from the asymptotic stability analysis problem of some containment error dynamics of MASs. Finally, two simulation examples are exploited to illustrate the effectiveness of the proposed techniques.     

An optimal adaptive robust PID controller subject to fuzzy rules and sliding modes for MIMO uncertain chaotic systems

In this paper, an optimal adaptive robust PID controller based on fuzzy rules and sliding modes is introduced to present a general scheme to control MIMO uncertain chaotic nonlinear systems. In this control scheme, the gains of the PID controller are updated by using an adaptive mechanism, fuzzy rules, the gradient search method, and the chain rule of differentiation in order to minimize the sliding surfaces of sliding mode control. More precisely, sliding mode control is used as a supervisory controller to provide sufficient control inputs and guarantee the stability of the control approach. To ascertain the parameters of the proposed controller and avoid trial and error, the multi-objective genetic algorithm is employed to augment the performance of proposed controller. The chaotic system of a Duffing-Holmes oscillator and an industrial robotic manipulator are the case studies to evaluate the performance of the proposed control approach. The obtained results of this study regarding both systems are compared with the outcomes of two notable studies in the literature. The results and analysis prove the efficiency of the proposed controller with regard to MIMO uncertain systems having challenging external disturbances in terms of stability, minimum tracking error and optimal control inputs.     

Nonlinear adaptive control for power integrator triangular systems by switching linear controllers

In this paper, a nonlinear adaptive stabilizer is designed for a class of power integrator triangular systems with the following four features: (i) the chained integrators have the powers of positive odd numbers, which makes the linearization of the studied system uncontrollable; (ii) the nonlinear function contains the virtual control variables; (iii) the bound of the nonlinear parameters entering the function nonlinearity is not required to be known a priori; and (iv) there exists an unknown control coefficient with the unknown bound in the control channel. Our proposed adaptive controller is a switching type controller, in which the designed adaptive stabilizer takes a two‐step procedure: a linear stabilizing controller containing the tuning gains is first designed by the adding a power integrator technique. Switching logic is then proposed to tune online the gains in a switching manner. The proposed adaptive controller globally asymptotically stabilizes the considered system in the sense that, for any initial conditions, the state converges to the origin while all the signals of the closed‐loop system are bounded. Simulation studies clarify and verify the approach. Copyright © 2014 John Wiley & Sons, Ltd.