Genetic algorithm tuning of Lyapunov-based controllers: anapplication to a single-link flexible robot system

In this paper, a systematic controller design approach is proposed to guarantee both closed-loop stability and desired performance of the overall system by effectively combining genetic algorithms (GAs) with Lyapunov's direct-controller design method. The effectiveness of the approach is shown by using a simple and efficient decimal GA optimization procedure to tune and optimize the performance of a Lyapunov-based robust controller for a single-link flexible robot. The feedback gains of the controller are tuned by the GA optimization process to achieve good results for tip motion control of the single-link flexible robot based on some suitable fitness functions. The paper includes results of simulation experiments demonstrating the effectiveness of the proposed genetic algorithm approach     

Performance tuning of robust motion controllers for high-accuracy positioning systems

This paper presents a structural design method of robust motion controllers for high-accuracy positioning systems, which makes it possible to tune the performance of the whole closed-loop system systematically. First, a stabilizing control input is designed based on Lyapunov redesign for the system in the presence of uncertainty and disturbance. And adopting the internal model following control, robust internal-loop compensator (RIC) is proposed. By using the structural characteristics of the RIC, disturbance attenuation properties and the performance of the closed-loop system determined by the variation of controller gains are analyzed. Next, in order to design a robust motion controller for a high performance positioning system, dual RIC structure is proposed and it is shown that if the synthesis of the robust motion control law is performed in the RIC framework, the robust property of RIC can be naturally implanted in the feedback controller. The proposed structural design of robust motion controller provides a systematic approach to the problem of robust stability and performance requirement in the face of uncertainty. Furthermore, by allowing the tradeoffs between robust stability and performance to be quantified in a simple fashion, it can illuminate systematic design procedure of the robust motion controllers. Finally, the proposed method is verified through simulation and the performance is evaluated by experiments using a high-accuracy positioning system.     

Adaptive quasi-sliding-mode tracking control for discrete uncertaininput-output systems

In this paper, a discrete robust adaptive quasi-sliding-mode tracking controller is presented for input-output systems with unknown parameters, unmodeled dynamics, and bounded disturbances. The robust tracking controller is comprised of adaptive control and a sliding-mode-based control design. The bounded motion of the system around the sliding surface and the stability of the global system in the sense that all signals remain bounded are guaranteed. The adaptive algorithm, in which the deadzone method is employed even though the upper and lower bounds of the disturbances are unknown, is the extension of the authors' previous work for the state-space systems. An example and its simulation results are presented to illustrate the proposed approach     

Robust servosystem design with two degrees of freedom and itsapplication to novel motion control of robot manipulators

A novel robust servosystem design method based on the two-degree-of-freedom (TDOF) controller and its application to advanced motion control for a robot manipulator is proposed. This servosystem is derived from the simple parametrization. The command input response and the closed loop characteristics can be specified independently by using two parameters which belong to the ring of stable and proper rational functions. The sensitivity and the complementary sensitivity functions can be determined straightforwardly through the optimization of the two design parameters. The control performance of the servosystem has been demonstrated. A completely decentralized joint control system for multiaxis robut manipulators has been realized. In particular, various kinds of robot motion controls, such as compliance, force, and hybrid controls, are realized in a unified way based on the robust position control. This servosystem has been implemented using DSP     

Hybrid fuzzy proportional-integral plus conventional derivativecontrol of linear and nonlinear systems

This paper presents a new approach toward the optimal design of a hybrid proportional-integral-derivative (PID) controller applicable for controlling linear as well as nonlinear systems using genetic algorithms (GAs). The proposed hybrid PID controller is derived by replacing the conventional PI controller by a two-input normalized linear fuzzy logic controller (FLC) and executing the conventional D controller in an incremental form. The salient features of the proposed controller are as follows: (1) the linearly defined FLC can generate nonlinear output so that high nonlinearities of complex systems can be handled; (2) only one well-defined linear fuzzy control space is required for both linear and nonlinear systems; (3) optimal tuning of the controller gains is carried out by using a GA; and (4) it is simple and easy to implement. Simulation results on a temperature control system (linear system) and a missile model (nonlinear system) demonstrate the effectiveness and robustness of the proposed controller     

Recurrent-Fuzzy-Neural-Network-Controlled Linear Induction Motor Servo Drive Using Genetic Algorithms

A recurrent fuzzy neural network (RFNN) controller based on real-time genetic algorithms (GAs) is developed for a linear induction motor (LIM) servo drive in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an online training RFNN with a backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, a real-time GA is developed to search the optimal learning rates of the RFNN online. The GA-based RFNN control system is proposed to control the mover of the LIM for periodic motion. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance     

耦合电感型Zeta变换器的参数优化方法

针对耦合电感型Zeta变换器的开环频率特性复杂导致的控制器设计困难的问题,文中提出了变换器参数的优化方法。该方法通过调整变换器的电路参数,进而调整变换器幅频特性中的高频谐振峰与谐振谷的位置,使得峰谷之间相互削弱,把相频特性曲线上超过180°部分最大程度减小到180°以内,消除了输出电压响应中的高频振荡成分,降低了变换器从控制变量到输出电压之间的频率特性的复杂程度。测试结果表明,通过该方法进行参数优化后,控制变量到输出电压之间的幅频特性接近二阶系统的幅频特性,相频特性上的相位滞后小于180°。在采用相同控制器的条件下,与优化前相比较,利用文中方法提高了控制器的带宽,改善了系统的输出电压的动态响应,增强了闭环系统的稳定性,验证了所提方法的有效性。     

Intelligent optimal control of single-link flexible robot arm

This paper addresses the design and properties of an intelligent optimal control for a nonlinear flexible robot arm that is driven by a permanent-magnet synchronous servo motor. First, the dynamic model of a flexible robot arm system with a tip mass is introduced. When the tip mass of the flexible robot arm is a rigid body, not only bending vibration but also torsional vibration are occurred. In this paper, the vibration states of the nonlinear system are assumed to he unmeasurable, i.e., only the actuator position can be acquired to feed into a suitable control system for stabilizing the vibration states indirectly. Then, an intelligent optimal control system is proposed to control the motor-mechanism coupling system for periodic motion. In the intelligent optimal control system a fuzzy neural network controller is used to learn a nonlinear function in the optimal control law, and a robust controller is designed to compensate the approximation error. Moreover, a simple adaptive algorithm is proposed to adjust the uncertain bound in the robust controller avoiding the chattering phenomena. The control laws of the intelligent optimal control system are derived in the sense of optimal control technique and Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. In addition, numerical simulation and experimental results are given to verify the effectiveness of the proposed control scheme.     

基于遗传算法的自适应神经模糊控制器

分析了自适应神经模糊系统的特性，以此设计一个智能控制器，控制器的输入为误差和误差变化率，输出为控制量。遗传算法是一种有效的全局最优搜索算法，对于复杂、非线性的空间也能够快速地搜索最优解，因而本文利用遗传算法来优化自适应神经模糊控制器的参数。仿真实验表明，该方法优化得到的控制器具有优良的控制效果。     

通讯资源受限网络系统的非均匀采样切换控制

针对含有通道资源受限和量化器的网络控制系统难于控制的问题,提出了基于切换原理的输出反馈控制器设计和动态调度方法.考虑到介质访问约束的影响,利用开关调度矩阵将通信受限的网络化控制系统,转化为含有多个子系统的非均匀采样的切换系统.利用Lyapunov稳定性理论推导出系统鲁棒镇定的充分条件,设计了可以满足任意切换稳定的最优鲁棒控制器和最优动态调度器.最后,通过仿真实例验证了所提出方法的有效性.     

Robust learning control of a high precision planar parallel manipulator

End-point positioning accuracy and fast settling time are essential in the motion system aimed at semiconductor packaging applications. In this paper, a novel robust learning control method for a direct-drive planar parallel manipulator is presented. A frequency-domain system identification approach is used to identify the high frequency dynamic of the manipulator. A robust control design method is employed to design a stable, fast tracking response feedback controller with less sensitivity to high frequency disturbance and the control parameters are determined using genetic algorithm. A Fourier-series-based iterative learning controller is designed and used on the feedforward path of the controller to further improve the settling time by reducing the dynamic tracking error of the manipulator. Experimental results demonstrate that the planar parallel manipulator has significant improvements on motion performance in terms of positioning accuracy, settling time and stability when compared with traditional XY-stages. This shows that the proposed manipulator provides a superior alternative to XY-motion stages for high precision positioning.     

改进的扰动LPV系统输出反馈预测控制

针对一类带有有界状态干扰的多胞描述LPV系统,文中提出了一种鲁棒预测控制改进方法,并设计了保证系统渐近稳定的输出反馈控制器。为抵消有界状态干扰,该控制器考虑无扰动LPV系统,基于离线状态观测器,采用线性矩阵不等式求解预测控制无穷时域最小-最大优化问题。随后利用离线状态观测器获得扰动LPV系统与无扰动LPV系统状态的估计值之差,以确定保性能的反馈增益,从而得到使扰动LPV系统渐近稳定的最优偏移量,并将其与无扰动系统控制律组合作为最优控制律施加于实际系统。实验结果表明,运用改进的鲁棒预测控制方法能获得较好地控制性能,同时提高了系统的稳定性和解决优化问题的效率,仿真试验也验证了该算法的有效性。     

Robust Model Predictive Control for Piecewise Affine Systems

In this paper, we study a robust model predictive control (MPC) strategy for piecewise affine (PWA) systems with uncertainty that is described as a set of polytopic parameter-varying models in a polytope corresponding to each partition of the PWA systems. First, an infinite horizon MPC technique for guaranteeing robust stability is developed for uncertain PWA systems. According to the condition of the PWA system states, the sequence of piecewise linear feedback controller at each sampling time is derived on-line by solving a convex optimization problem involving linear matrix inequalities. The feasible PWA control law design can robustly stabilize the uncertain PWA systems. However, the on-line optimization problems may lead to a computational burden. Then we further propose an improved robust MPC algorithm. When the current state is outside of the region of PWA systems containing the origin, the proposed on-line robust MPC algorithm is utilized; once the current state enters the region with the origin, sequence attraction domains where the origin is included are constructed off-line one within another, and the explicit control laws corresponding to different attraction domains can drive the state to the origin. The two algorithms are illustrated with a numerical example. The simulation results show that both controller design methods can stabilize the PWA systems with polytopic uncertainty, and the improved algorithm can reduce the on-line computation cost.     

Robust Disturbance Attenuation with Stabilization for Uncertain Networked Control Systems

This paper investigates the problem of robust stabilization and attenuation for a class of uncertain networked control systems (NCSs) with random communication network-induced delays. Based on the Lyapunov–Razumikhin method, a controller is designed such that both robust stability and a prescribed disturbance attenuation performance for the closed-loop NCS are achieved, irrespective of the uncertainties and network-induced delays. The controller design technique is given in terms of the solvability of bilinear matrix inequalities. An iterative algorithm is proposed to change this non-convex problem into quasi-convex optimization problems, which can be solved effectively using available mathematical tools. The effectiveness of the proposed design methodology is verified by a numerical example.     

Reliability optimization of series-parallel systems using a genetic algorithm

A problem-specific genetic algorithm (GA) is developed and demonstrated to analyze series-parallel systems and to determine the optimal design configuration when there are multiple component choices available for each of several k-out-of-n:G subsystems. The problem is to select components and redundancy-levels to optimize some objective function, given system-level constraints on reliability, cost, and/or weight. Previous formulations of the problem have implicit restrictions concerning the type of redundancy allowed, the number of available component choices, and whether mixing of components is allowed. GA is a robust evolutionary optimization search technique with very few restrictions concerning the type or size of the design problem. The solution approach was to solve the dual of a nonlinear optimization problem by using a dynamic penalty function. GA performs very well on two types of problems: (1) redundancy allocation originally proposed by Fyffe, Hines, Lee, and (2) randomly generated problem with more complex k-out-of-n:G configurations.     

Motion synchronization for dual-cylinder electrohydraulic liftsystems

This paper presents a nonlinear control algorithm to address the motion synchronization problem for a dual-cylinder electrohydraulic (EH) lift system. A two-step design approach is applied that utilized linear multiple input-multiple output (MIMO) robust control technique to design an outer-loop motion synchronization controller. A nonlinear single input-single output (SISO) perturbation observer-based pressure-force controller is designed for each of the lift cylinders as the inner-loop controller to handle the nonlinearities associated with the EH actuators. Experimental results on a 2-cylinder lift system verified the effectiveness of the proposed approach     

Fuzzy optimization techniques applied to the design of a digital PMSM servo drive

This paper presents a novel design approach by applying gradient optimization with fuzzy step-sizing techniques to the design of a digital permanent magnet synchronous motor (PMSM) servo drive. The servo specifications and design variables are specified and analyzed to formulate a controller optimization problem. The servo responses are then fed back to evaluate the overall system performances, which can be expressed as objective functions with respect to the servo control parameters. According to the objective functions and design specifications, the servo control parameters can be properly tuned toward their optimal values by using the proposed optimization techniques. In order to improve the convergent rate of the optimization process, a fuzzy-logic based step-size tuning strategy is presented. Because of the nonlinear property of the digital servo drives, the tuned servo control parameters may be only optimal for a particular operating point, therefore, once the optimum design is achieved, the proposed fuzzy optimizing controller can perform as an intelligent tuner for on-line gain adaptation under different loading conditions. The proposed fuzzy optimization servo tuner has been realized under a PC-MATLAB-based environment with an on-line controlled digital PMSM servo drive. Simulation and experimental results indicate that the control parameters of a digital PMSM servo drive can be optimized for its dynamic responses under various load conditions.     

Design optimization of double-acting hybrid magnetic thrust bearings with control integration using multi-objective evolutionary algorithms

Integration of the geometric and control designs in conjunction with the optimization is the current trend in mechatronic products. In the present work, an optimal design methodology of double-acting hybrid active magnetic thrust bearings has been proposed. Double-acting actuators and controller are optimized as a unified system. Conventionally, in control of rotors in the axial direction using double-acting magnetic bearings, two identical bearings are used. However, in the present design two different bearing geometries with different operating parameters have been considered. Minimization of the powerloss, the weight, the control input and dynamic performance indices and maximization of the load capacity have been considered as objectives. The design considers the 10 geometric, two electrical, and two control design parameters. The constraints are classified into three categories, namely the geometric, electrical, and control constraints. Real coded genetic algorithm has been implemented to carry out the constrained multi-objective optimization of the present problem. The convergence and Pareto-front spaces are studied by using different populations of sizes run for different generations. Some of the convergence criterions have been observed for actuator–controller systems. Designs which are nearest to the utopia point in Pareto-front fronts are compared. Air gaps, bias currents, and lengths of permanent magnets are observed to be consistently different for individual actuators of the double-acting bearing. Performance parameters of double-acting actuators and the controller of the magnetic bearing for different choices have been presented.     

Development of Lyapunov-Based Genetic Algorithm Control for Linear Piezoelectric Ceramic Motor Drive

In general, the role of genetic algorithm (GA) is operated offline as a minor compensator or tuner in the control engineering because the systematic design and the latent stability problem of a GA-based control scheme are required to be solved. This paper originally designs a Lyapunov-based GA control (LGAC) scheme, and it applies for a practical control engineering example of the online motion control of a linear piezoelectric ceramic motor driven by a hybrid resonant inverter. In this control scheme, a GA control system via backstepping design technique is utilized to be the major controller, and adaptation laws derived from Lyapunov stability analyses are manipulated to adjust appropriate evolutionary steps. As a result, the system stability can be guaranteed directly without strict constraint conditions and detailed system knowledge. The effectiveness of the proposed drive and control system is verified by experimental results in the presence of uncertainties. From the measured results, the LGAC system performs superior high-precision motion control under wide operation range than conventional backstepping control system.