Extremum-seeking Technique Research Articles

Controlling a vehicle braking and longitudinal acceleration using a seeking control approach

Traditional methods for tracking the paths of driverless vehicles use plant models to determine the corresponding control laws. Due to the intricate interactions between the road and the tires, time-varying characteristics, and unidentified disturbances. It is challenging to create an accurate vehicle model. As a result, data-driven controllers, which are independent of a predetermined plant model are becoming more and more well-liked. This work implements adaptive cruise control (ACC) by employing a control approach called extremum seeking technique (EST), which is a model-free control (MFC), to control a vehicle braking and longitudinal acceleration. The main aim here is to create an ego vehicle that travels at a specific speed with maintaining a secure space with respect to a guide vehicle. A car including an ACC technique called ego car, exploits radar to determine relative velocity and relative space relating to the guiding car. The ACC technique is considered to keep maintain a relatively secure space or a preferred cruising velocity concerning the guiding vehicle. The developed model succeeded to determine the relative velocity and relative space according for the ego car to another guiding car with acceleration not more than ±2 m/s2 and spacing error less than 6 m.

Advanced Control Strategy of Switched Reluctance Generator-Based Wind Energy Conversion Systems Using Backstepping and Extremum Seeking Techniques

Switched reluctance generators (SRG) become increasingly competitive with other electrical machines due to their design, which includes only a stator winding. The SRG are known for their reliability and capacity to operate over a wide speed range, these generators find significant use in variable wind power plants. The main limitations of these machines are torque fluctuation and relatively low efficiency. In this respect, the aim of this work is to mitigate these drawbacks. Presently, an extremum seeking online method has been developed, and a backstepping controller is designed for SRG-based wind energy conversion system (WESC) connected to the grid via a VSC converter. One of the main objectives of the proposed controller is to achieve the optimum rotation speed of the turbine and to regulate the SRG for tracking the maximum power point (MPPT) while minimizing torque ripple via Direct Instantaneous Torque Control (DITC). To show the efficiency and performance of the proposed controllers, simulation examples of the proposed controllers are established using MATLAB/SIMULINK tools. The simulation results show that the efficiency and performance of the system using the proposed controller are significantly improved.

Research on various PV arrays manufacturing data for power comparison and optimization through extremum seeking technique

The increasing environmental repercussions and depletion of nonrenewable energy resources have cautioned and enabled researchers to incorporate renewable energy systems. Amongst the renewable energy resources, the solar energy system has been utilized in most parts of the world due to cheaper, reliable, robust and sustainable energy resource than other resources. The Maximum Power Point Tracking (MPPT) techniques are used for increasing the power output of PV array. The Perturb and Observe (P&O) technique is widely used MPPT technique due to higher efficiency and ease in implementation. The proposed “Perturb and Observe (P&O)” MPPT technique is incorporated through Matlab Simulation software on PV arrays of various companies. The results are then compared through comparative analysis and optimum results are recommended for the manufacturing companies.

Constrained extremum seeking of a MIMO dynamic system

Constraints on outputs or measured states arise in many practical engineering problems, yet are not readily handled by conventional extremum seeking techniques that require the output or states to be mapped to a corresponding input region. In this work, an alternative approach utilising non-linear programming techniques along with measurements of the constraint functions is proposed to enable a constrained extremum to be sought directly. Under reasonable assumptions and tuning, the proposed approach is shown to provide semi-global practical asymptotic stability guarantees for a class of MIMO plants. Simulation results are provided to demonstrate the efficacy of the proposed approach.

Extremum seeking dead-zone pre-compensator for an industrial control system

Abstract PID type industrial controllers such as PI, PD, PID are mature control algorithms and they are intensively used in industry due to their simplicity and easily implementability. However, they start to fail when there is an unknown or unpredictable nonlinear behavior in the plant or actuator. In this paper, a novel compensation algorithm is proposed for PD type industrial control systems, which possess an unknown dead-zone nonlinearity. An extremum-seeking technique is utilized in the compensation algorithm. The aim is to propose a new, effective and robust compensator which can be added easily to an existing industrial controller without any need to change/retune the controller settings/parameters. It is shown that by adding the compensator to an existing PD control system, the sensitivity of the controller to the dead-zone nonlinearity is removed.

Multiplexed extremum seeking for calibration of spark timing in a CNG-fuelled engine

The compositional variability of many alternative fuels, coupled with fuel agnostic behaviour like engine ageing and vehicle-to-vehicle differences, leads to the desire for some form of online calibration in order to optimise fuel efficiency. This has led to the incorporation of extremum seeking techniques within the field in order to continually fine tune engine performance. These typically address steady state engine performance and are characterised by slow convergence times, hindering their deployment in typical dynamic driving scenarios. To address this potential shortcoming, in this paper a novel multiplexed extremum seeking scheme is proposed to track a time-varying extremum caused by a measurable disturbance. It consists of multiple extremum seeking agents that are individually activated based on the disturbance. The multiplexed approach accommodates the rigorous practical stability results of the “traditional” extremum seeking approaches, but offers improved results in dynamic scenarios. The proposed approach is implemented both in simulation and experimentally on a compressed natural gas (CNG) engine operating over a drive cycle. The experimental results show that under proper tuning, the proposed controller can improve the engine fuel efficiency for unknown natural gas compositions without requiring gas composition sensing at little additional calibration effort.

Peak Searching for Multimodal Optimization through Gradient-based Stochastic Extremum Seeking Technique

In this paper, a global peak searching algorithm is proposed for multimodal optimization problem. The proposed algorithm is built through combining gradient-based stochastic extremum seeking method with iterative approach. By increasing the initial search point, the gradient-based stochastic extremum seeking algorithm with different initial condition is iteratively operated to seek a list of the local extreme values near different initial points. Then, the global peak can be found by comparing the list of the local extreme values. The stability anlysis and working process of the global peak searching algorithm is proven. Simulation results are given to show the effectiveness of the proposed control method.

Multi-Degree-of-Freedom Stabilization of Large-Scale Photonic-Integrated Circuits

We present and elaborate the principles of a novel control and calibration (C&C) systematic approach, aiming at feedback stabilization of large scale photonic-integrated circuits (PIC) to their nominal operating points in D -dimensional spaces of tuning parameters, based on refinement of extremum-seeking (ES) real-time optimization techniques. The novel methodology enables, in principle, stabilizing a large number D of tuning degrees of freedom (DOF) of PICs, with just one optical power monitoring (probe) point. The proposed C&C digital controller ports multidimensional ES stabilization concepts to photonics for the first time and further introduces novel improvements to known ES techniques, proposing a new frame-based discrete-multitone method of actuation signals generation and probe signals detection, akin to an orthogonal-frequency-division multiplexing modulation format. Another novel element is that the iterative digital algorithm adaptively selects between gradient- versus Newton-based descent and between various methods of line search. This approach enables real-time application of unconstrained optimization techniques for C&C of photonic circuits with multiple tuning DOFs. The new technique is exemplified by numeric simulations of the stabilization of a Silicon photonic microring modulator with D = 2 tuning DOFs, concurrently tuning both resonant phase and a second coupling phase parameter, optimizing critical coupling into the microring based on observing the optical power at single low-frequency monitoring point.

Online optimization of spark advance in alternative fueled engines using extremum seeking control

Alternative fueled engines offer greater challenges for engine control courtesy of uncertain fuel composition. This makes optimal tuning of input parameters like spark advance extremely difficult in most existing ECU architectures. This paper proposes the use of grey-box extremum seeking techniques to provide real-time optimization of the spark advance in alternative fueled engines. Since practical implementation of grey-box extremum seeking methods is typically done using digital technology, this paper takes advantage of emulation design methods to port the existing continuous-time grey-box extremum seeking methods to discrete-time frameworks. The ability and flexibility of the proposed discrete-time framework is demonstrated through simulations and in practical situation using a natural gas fueled engine.

Extremum Seeking Methods for Online Optimization of Spark Advance in Alternative Fueled Engines

Alternative fueled engines offer greater challenges for engine control courtesy of uncertain fuel composition. This make optimal tuning of input parameters like spark advance extremely difficult in most existing ECU architectures. This paper proposes the use of grey-box extremum seeking techniques to provide real-time optimization of the spark advance in alternative fueled engines. The ability and flexibility of the proposed framework is demonstrated through simulation examples. The approaches demonstrated may be extended to other engine inputs requiring online optimization.

A Simultaneous Approach for Correcting Differences between Units in Multi-unit Optimization

Multi-unit optimization is a recently proposed extremum-seeking technique where the gradient, estimated by finite difference between two identical units, is controlled to zero. The main assumption of having two identical units is rarely verified in practice and it has been noted that differences in the static characteristics can affect the stability and the equilibrium point. So, correctors have been proposed, where optimization and correction for differences are performed alternatively. This, in turn, causes a discontinuous operation leading to a hybrid dynamics. To avoid such a scenario, an approach where optimization and correction take place simultaneously, is presented. A proof of stability of the simultaneous scheme is also provided.

A new extremum seeking technique and its application to maximize RF heating on FTU

We propose a new global extremum seeking algorithm to optimize the coupling between the emitting Lower Hybrid (LH) antennas and the plasma scrape off layer in the radiofrequency (RF) heating problem in tokamak plasmas. The new algorithm, where the existing disturbances affecting the system are seen as the probing signals, requires less constraining properties than the previous ones and is more robust. In particular, simulation results are presented illustrating the effectiveness of the algorithm on the Lower Hybrid RF heating of the Frascati Tokamak Upgrade (FTU).

Maximization of the lower hybrid power coupling in the Frascati Tokamak Upgrade via extremum seeking

This paper reports on the experimental results arising from the implementation of extremum seeking techniques to maximize in real time the radio-frequency (RF) power coupled to the plasma in the Frascati Tokamak Upgrade (FTU) experimental facility. The RF under consideration is the Lower Hybrid (LH) and the coupling issue consists in the match between an antenna structure (an array of waveguides, the grill) and a load, i.e. the plasma. The optimization of the coupling level reduces the reflected power thus maximizing the RF effects on the plasma and at the same time avoiding temporary power shutdown to protect the system from the excess of reflection. Since it is unviable to inject a dither signal in the plasma control system, the implemented extremum seeking relies on existing disturbances in the experimental system, thus following a somewhat novel paradigm. First, simulation results are given based on a model of the closed-loop and then the experimental tests which confirmed the effectiveness of the proposed approach are shown.