Gotowa bibliografia na temat „Optimal Tuning of H Infinity Speed Controller”

Purpose The efficient speed controller is found to be an important requirement to run the motor for the brushless direct current (BLDC) motor. This requirement is considered as superior, as it may increase the operating speed and system efficiency. In the existing methods, proportional plus integral (PI) controller has been included because of its simple architecture. But the PI controller produces load disturbance, control complexity and some parametric (Proportional plus integral) variations. The purpose of this proposed controller is to overcome the problems produced by PI controller in BLDC motor. Design/methodology/approach The proposed BLDC motor is developed with fixed order H-infinity controller. In this architecture, both the weight functions and transfer functions were included to design the controller. This controller has been included in this BLDC to detect the rotor position. The optimal position of rotor is identified by introducing particle swarm optimization algorithm. Findings The torque that obtained in the motor is highly reduced by this proposed controller and also enhances the speed. The BLDC motor is modelled in a MATLAB environment. Practical implications The performance of the torque, speed and back electro-motive force is analysed and compared with the existing controllers such as fuzzy proportional plus integral plus derivative, sensing algorithm and fuzzy proportional plus derivative controller. Originality/value Simulation results show that the proposed technique gives better results than the other existing controllers.

The robust controller designed by conventional H∞optimal control is complicated, high-order, and difficult to implement practically. In industrial applications, structures such as lead-lag compensators and PID are widely used because their structure is simple, tuning parameters are fewer, and they are lower-order. Their disadvantages are that control parameters are difficult to tune for good performance and they lack robustness. To solve these problems, we propose an algorithma genetic-algorithm-based fixed-structure robust H∞loop-shaping controlfor designing the robust controller. Conventional H∞loop shaping is a sensible procedure for designing the robust controller. To obtain parameters in the proposed controller, we proposed a genetic algorithm to optimize specified-structure H∞loop shaping problem. The infinity norm of transfer function from disturbances to states is minimized via searching and evolutionary computation. The resulting optimal parameters stabilize the system and guarantee robust performance. We applied the evolutionary robust controller to a pneumatic servosystem. To compare performance, we studied three types of controller PID with a derivative first-order filter controller, a PI controller, and an H∞loop-shaping controller. Results of experiments demonstrate the advantages of a simple structure and robustness against parameters changing. Simulations verify the effectiveness of the proposed technique.

It's not easy to implement the mixed / optimal controller for high order system, since in the conventional mixed / optimal feedback the order of the controller is much than that of the plant. This difficulty had been solved by using the structured specified PID controller. The merit of PID controllers comes from its simple structure, and can meets the industry processes. Also it have some kind of robustness. Even that it's hard to PID to cope the complex control problems such as the uncertainty and the disturbance effects. The present ideas suggests combining some of model control theories with the PID controller to achieve the complicated control problems. One of these ideas is presented in this paper by tuning the PID parameters to achieve the mixed / optimal performance by using Intelligent Genetic Algorithm (IGA). A simple modification is added to IGA in this paper to speed up the optimization search process. Two MIMO example are used during investigation in this paper. Each one of them has different control problem.

Left ventricular assist devices (LVADs) are emerging innovations that provide a feasible alternative treatment for heart failure (HF) patients to enhance their quality of life. In this work, a novel physiological control system to optimize LVAD pump speed using an H-infinity controller was developed. The controller regulates the calculated target pump flow vs. measured pump flow to meet the changes in metabolic demand. The method proposes the implementation of the Frank–Starling mechanism (FSM) approach to control the speed of an LVAD using the left ventricle end-diastolic volume (Vlved) parameter (preload). An operating point was proposed to move between different control lines within the safe area to achieve the FSM. A proportional–integral (PI) controller was used to control the gradient angle between control lines to obtain the flow target. A lumped parameter model of the cardiovascular system was used to evaluate the proposed method. Exercise and rest scenarios were assessed under multi-physiological conditions of HF patients. Simulation results demonstrated that the control system was stable and feasible under different physiological states of the cardiovascular system (CVS). In addition, the proposed controller was able to keep hemodynamic variables within an acceptable range of the mean pump flow (Qp) (max = 5.2 L/min and min = 3.2 L/min) during test conditions.

The microgrid has two main steady-state modes: grid-connected mode and islanded mode. The microgrid needs a high-performance controller to reduce the overshoot value that affects the efficiency of the network. However, the high voltage value causes the inverter to stop. Thus, an improved power-sharing response to the transfer between these two modes must be insured. More important points to study in a microgrid are the current sharing and power (active or reactive) sharing, besides the match percentage of power sharing among parallel inverters and the overshoot of both active and reactive power. This article aims to optimize the power response in addition to voltage and frequency stability, in order to make this network’s performance more robust against external disturbance. This can be achieved through a self-tuning control method using an optimization algorithm. Here, the optimized droop control is provided by the H-infinity (H∞) method improved with the artificial bee colony algorithm. To verify the results, it was compared with different algorithms such as conventional droop control, conventional particle swarm optimization, and artificial bee colony algorithms. The implementation of the optimization algorithm is explained using the time domain MATLAB/SIMULINK simulation model.

Objective: In this paper, an optimal control scheme for the Interlinking Converter (IC) system is achieved by the proper regulation of its gate switching functions through appropriate optimal feedback controller design. Methods: Proportional-Integral-Derivative (PID), Fractional Order Proportional-Integral-Derivative (FOPID) and Hinfinity loop shaping controller have been designed for the two-fold control objective of simultaneous improvement in system robustness and reduced tracking error using parameter tuning via Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) optimization algorithms. Results: The controller parameters are obtained by optimization algorithms. The comparative analysis of the controller performance is carried out through simulation in MATLAB platform to validate the effectiveness in the controller design under various changing situations. Conclusion: The optimized controller parameters obtained through ABC algorithm are better than that obtained through PSO algorithm in terms of both objective function values and execution time. The resultant robust control strategy for IC system obtained through H-infinity loop shaping controller provides reduced tracking error and improved stability as compared to PID and FOPID controller, as proved by the simulation results.

Abstract This paper presents a two-step optimization-based design method for iterative learning control and applies it onto the quadrotor unmanned aerial vehicles (UAVs) trajectory tracking problem. Iterative learning control aims to improve the tracking performance through learning from errors over iterations in repetitively operated systems. The tracking errors from previous iterations are injected into a learning filter and a robust filter to generate the learning signal. The design of the two filters usually involves nontrivial tuning work. This paper presents a new two-optimization design method for the iterative learning control, which is easy to obtain and implement. In particular, the learning filter design problem is transferred into a feedback controller design problem for a purposely constructed system, which is solved based on H-infinity optimal control theory thereafter. The robust filter is then obtained by solving an additional optimization to guarantee the learning convergence. Through the proposed design method, the learning performance is optimized and the system's stability is guaranteed. The proposed two-step optimization-based design method and the regarding iterative learning control algorithm are validated by both numerical and experimental studies.

Purpose Voltage source inverter-fed permanent magnet synchronous motors (VSI-PMSMs) are widely used in industrial actuation and mechatronic systems in water pumping stations, as well as in the traction of transportation systems (such as electric vehicles and electric trains or ships with electric propulsion). The dynamic model of VSI-PMSMs is multivariable and exhibits complicated nonlinear dynamics. The inverters’ currents, which are generated through a pulsewidth modulation process, are used to control the stator currents of the PMSM, which in turn control the rotational speed of this electric machine. So far, several nonlinear control schemes for VSI-PMSMs have been developed, having as primary objectives the precise tracking of setpoints by the system’s state variables and robustness to parametric changes or external perturbations. However, little has been done for the solution of the associated nonlinear optimal control problem. The purpose of this study/paper is to provide a novel nonlinear optimal control method for VSI-fed three-phase PMSMs. Design/methodology/approach The present article proposes a nonlinear optimal control approach for VSI-PMSMs. The nonlinear dynamic model of VSI-PMSMs undergoes approximate linearization around a temporary operating point, which is recomputed at each iteration of the control method. This temporary operating point is defined by the present value of the voltage source inverter-fed PMSM state vector and by the last sampled value of the motor’s control input vector. The linearization relies on Taylor series expansion and the calculation of the system’s Jacobian matrices. For the approximately linearized model of the voltage source inverter-fed PMSM, an H-infinity feedback controller is designed. For the computation of the controller’s feedback gains, an algebraic Riccati equation is iteratively solved at each time-step of the control method. The global asymptotic stability properties of the control method are proven through Lyapunov analysis. Finally, to implement state estimation-based control for this system, the H-infinity Kalman filter is proposed as a state observer. The proposed control method achieves fast and accurate tracking of the reference setpoints of the VSI-fed PMSM under moderate variations of the control inputs. Findings The proposed H-infinity controller provides the solution to the optimal control problem for the VSI-PMSM system under model uncertainty and external perturbations. Actually, this controller represents a min–max differential game taking place between the control inputs, which try to minimize a cost function that contains a quadratic term of the state vector’s tracking error, the model uncertainty, and exogenous disturbance terms, which try to maximize this cost function. To select the feedback gains of the stabilizing feedback controller, an algebraic Riccati equation is repetitively solved at each time-step of the control algorithm. To analyze the stability properties of the control scheme, the Lyapunov method is used. It is proven that the VSI-PMSM loop has the H-infinity tracking performance property, which signifies robustness against model uncertainty and disturbances. Moreover, under moderate conditions, the global asymptotic stability properties of this control scheme are proven. The proposed control method achieves fast tracking of reference setpoints by the VSI-PMSM state variables, while keeping also moderate the variations of the control inputs. The latter property indicates that energy consumption by the VSI-PMSM control loop can be minimized. Practical implications The proposed nonlinear optimal control method for the VSI-PMSM system exhibits several advantages: Comparing to global linearization-based control methods, such as Lie algebra-based control or differential flatness theory-based control, the nonlinear optimal control scheme avoids complicated state variable transformations (diffeomorphisms). Besides, its control inputs are applied directly to the initial nonlinear model of the VSI-PMSM system, and thus inverse transformations and the related singularity problems are also avoided. Compared with backstepping control, the nonlinear optimal control scheme does not require the state-space description of the controlled system to be found in the triangular (backstepping integral) form. Compared with sliding-mode control, there is no need to define in an often intuitive manner the sliding surfaces of the controlled system. Finally, compared with local model-based control, the article’s nonlinear optimal control method avoids linearization around multiple operating points and does not need the solution of multiple Riccati equations or LMIs. As a result of this, the nonlinear optimal control method requires less computational effort. Social implications Voltage source inverter-fed permanent magnet synchronous motors (VSI-PMSMs) are widely used in industrial actuation and mechatronic systems in water pumping stations, as well as in the traction of transportation systems (such as electric vehicles and electric trains or ships with electric propulsion), The solution of the associated nonlinear control problem enables reliable and precise functioning of VSI-fd PMSMs. This in turn has a positive impact in all related industrial applications and in tasks of electric traction and propulsion where VSI-fed PMSMs are used. It is particularly important for electric transportation systems and for the wide use of electric vehicles as expected by green policies which aim at deploying electromotion and at achieving the Net Zero objective. Originality/value Unlike past approaches, in the new nonlinear optimal control method, linearization is performed around a temporary operating point, which is defined by the present value of the system’s state vector and by the last sampled value of the control input vector and not at points that belong to the desirable trajectory (setpoints). Besides, the Riccati equation, which is used for computing the feedback gains of the controller, is new, as is the global stability proof for this control method. Comparing with nonlinear model predictive control, which is a popular approach for treating the optimal control problem in industry, the new nonlinear optimal (H-infinity) control scheme is of proven global stability, and the convergence of its iterative search for the optimum does not depend on initial conditions and trials with multiple sets of controller parameters. It is also noteworthy that the nonlinear optimal control method is applicable to a wider class of dynamical systems than approaches based on the solution of state-dependent Riccati equations (SDRE). The SDRE approaches can be applied only to dynamical systems that can be transformed to the linear parameter varying form. Besides, the nonlinear optimal control method performs better than nonlinear optimal control schemes which use approximation of the solution of the Hamilton–Jacobi–Bellman equation by Galerkin series expansions.

This chapter describes the controller modeling for PID gain tuning against the external disturbances with constrained internal parameter variation of the PMDC motor based on an optimization technique of H-infinity. To fit the goals in the H infinite framework, auto tuning of the PID controller gains is used. Different performance goals for tracking are preset as design objectives. Researchers in literature have presented many Robust Control techniques for motor control applications. Methods like back-stepping algorithms, fuzzy and neural based control systems, model predictive control and SMC (Sliding Mode Control) are available in literature. In this chapter, SC (speed control) of PMDC-motor is addressed with variations in outer load disturbances and internal variations of the system parameters for a particular application. C-PID (conventional PID controllers) is preferred, and equivalent robustness characteristics are established using the H-infinity development procedures. The optimization effort is to get simultaneous fast-tracking response and better disturbance rejection.

Abstract A feedforward controller which respects the underlying hybrid nature of digital control systems, i.e. continuous-time plant and discrete-time feedback loop, is proposed for high speed/high precision motion systems. The feedforward controller combines the shift(q) and the delta (δ) operators such that uncancellable discrete-time zeros caused by sampling the continuous-time plant at high rates, which make the mathematical inverse unstable, are handled in a natural way. The controller is optimized by tuning two parameters in a pre-filter of the feedforward controller. The optimization problem is generalized to an H∞ problem. Convex minimization techniques are used to find the solution to the optimization problem. Experiments are carried out on a Matsuura Vertical Machining Center. The performance of the proposed optimal hybrid feedforward controller is compared with that of the zero phase error tracking controller. Some improvements have been observed from the experimental results.