Self-tuning Algorithm Research Articles

The Application of Embedding Operating System at the High-Precision Temperature Calibration Device

In order to meet the fast on-site calibration of temperature sensor and temperature measuring instruments, A kind of intellectualized high-precision temperature calibration device based on Embedding System is developed. This device based on ARM and a proportional-fuzzy self-tuning PID optimal control algorithm is characterized by high accuracy and fast response. Its performance parameter is given as following: measuring range, 35oC-600 oC, accuracy, ± 0.50 oC (at 600 oC), resolution ,0.01 oC, stability time 40 minutes(at 600 oC). To ensure the portability of μC/OS-II, a set of data types is defined by authors. As same as all the real time cores, it is necessary for μC/OS-II to reject the interrupt before accessing the critical section of codes, and the interrupt is to be allowed again after the access. In this paper, the design of system , especially the porting of μC/OS-II operating system, is introduced.

Local density adaptive similarity measurement for spectral clustering

Similarity measurement is crucial to the performance of spectral clustering. The Gaussian kernel function is usually adopted as the similarity measure. However, with a fixed kernel parameter, the similarity between two data points is only determined by their Euclidean distance, and is not adaptive to their surroundings. In this paper, a local density adaptive similarity measure is proposed, which uses the local density between two data points to scale the Gaussian kernel function. The proposed similarity measure satisfies the clustering assumption and has an effect of amplifying intra-cluster similarity, thus making the affinity matrix clearly block diagonal. Experimental results on both synthetic and real world data sets show that the spectral clustering algorithm with our local density adaptive similarity measure outperforms the traditional spectral clustering algorithm, the path-based spectral clustering algorithm and the self-tuning spectral clustering algorithm.

A Self-Tuning Proportional-Integral-Derivative Controller for an Autonomous Underwater Vehicle, Based On Taguchi Method

Problem statement: Conventional Proportional-Integral-Derivative (PID) controllers exhibit moderately good performance once the PID gains are properly tuned. However, when the dynamic characteristics of the system are time dependent or the operating conditions of the system vary, it is necessary to retune the gains to obtain desired performance. This situation has renewed the interest of researchers and practitioners in PID control. Self-tuning of PID controllers has emerged as a new and active area of research with the advent and easy availability of algorithms and computers. This study discusses self-tuning (auto-tuning) algorithm for control of autonomous underwater vehicles. Approach: Self-tuning mechanism will avoid time consuming manual tuning of controllers and promises better results by providing optimal PID controller settings as the system dynamics or operating points change. Most of the self-tuning methods available in the literature were based on frequency response characteristics and search methods. In this study, we proposed a method based on Taguchi’s robust design method for self-tuning of an autonomous underwater vehicle controller. The algorithm, based on this method, tuned the controller gains optimally and robustly in real time with less computation effort by using desired and actual state variables. It can be used for the Single-Input Single-Output (SISO) systems as well as Multi-Input Multi-Output (MIMO) systems without mathematical models of plants. Results: A simulation study of the AUV control on the horizontal plane (yaw plane control) was used to demonstrate and validate the performance and effectiveness of the proposed scheme. Simulation results of the proposed self-tuning scheme are compared with the conventional PID controllers which are tuned by Ziegler-Nichols (ZN) and Taguchi’s tuning methods. These results showed that the Integral Square Error (ISE) is significantly reduced from the conventional controllers. The robustness of this proposed self-tuning method was verified and results are presented through numerical simulations using an experimental underwater vehicle model under different working conditions. Conclusion/Recommendations: By using this scheme, the PID controller gains are optimally adjusted automatically online with respect to the system dynamics or operating condition changes. This technique found to be more effective than conventional tuning methods and it is even very convenient when mathematical models of plants are not available. Computer simulations showed that the proposed method has very good tracking performance and robustness even in the presence of disturbances. The simple structure, robustness and ease of computation of the proposed method make it very attractive for real time implementation for controlling of underwater vehicle and it offers a chance to extend the same technique to the three dimensional vehicle tracking control as well.

Automated Model Generation Algorithm for High-Level Fault Modeling

High-level modeling for operational amplifiers (opamps) has been previously carried out successfully using models generated by published automated model generation approaches. Furthermore, high-level fault modeling (HLFM) has been shown to work reasonably well using manually designed fault models. However, no evidence shows that published automated model generation approaches based on opamps have been used in HLFM. This paper describes HLFM for analog circuits using an adaptive self-tuning algorithm called multiple model generation system using delta. The generation algorithms and simulation models were written in MATLAB and the hardware description language VHDL-AMS, respectively. The properties of these self-tuning algorithms were investigated by modeling complementary metal-oxide-semiconductor opamps, and comparing simulations using the HLFM against those of the original simulation program with integrated circuit emphasis circuit utilizing transient analysis. Results show that the models can handle both linear and nonlinear fault situations with better accuracy than previously published HLFMs.

Higher order sliding mode control with self-tuning law based on integral sliding mode

A higher order sliding mode control (SMC) with self-tuning law algorithm for uncertain non-linear systems is proposed. The method can be viewed as the finite time stabilisation based on geometric homogeneity and integral SMC. In order to reduce chattering and solve system uncertainties with unknown bound, a bipolar sigmoid function on-line adaptation and an adjustable control gain tuning approach without high-frequency switching are developed. Control system stability is ensured using the Lyapunov method. An example is given to show the effectiveness of the developed approach.

A self-tuning phase-shifting algorithm for interferometry

In Phase Stepping Interferometry (PSI) an interferogram sequence having a known, and constant phase shift between the interferograms is required. Here we take the case where this constant phase shift is unknown and the only assumption is that the interferograms do have a temporal carrier. To recover the modulating phase from the interferograms, we propose a self-tuning phase-shifting algorithm. Our algorithm estimates the temporal frequency first, and then this knowledge is used to estimate the interesting modulating phase. There are several well known iterative schemes published before, but our approach has the unique advantage of being very fast. Our new temporal carrier, and phase estimator is capable of obtaining a very good approximation of their temporal carrier in a single iteration. Numerical experiments are given to show the performance of this simple yet powerful self-tuning phase shifting algorithm.

Approximate Predictive versus Self-Tuning Adaptive Control Strategies of Biodiesel Reactors

Producing biodiesel from palm oil as a raw material involves complex transesterification reactions which add up to the process nonlinearity. In this work, more emphasis will be focused on the reactor nonlinearity and ways of solving its control problem. The reactor nonlinearity is addressed via the application of an instantaneous linearization technique to control the reactor temperature and the triglyceride product concentration. A feed-forward neural network with delayed inputs and outputs was trained and validated to capture the dynamics of the biodiesel process. The generated nonlinear model was then utilized in an instantaneous linearization algorithm using two control algorithms adopting the self-tuning adaptive control and an approximate model predictive framework. The two algorithms were compared in terms of set-point tracking capability, efficiency, and stability. The minimum variance control algorithm attained poor performance compared to the pole-placement self-tuning adaptive algorithm. However, the approximate model predictive control strategy was superior to the self-tuning control in terms of its ability for forcing the output to follow the set-point trajectory efficiently with smooth controller moves.

Efficient and adaptive proportional share I/O scheduling

Quality of service (QoS) guarantees for applications are desirable under many scenarios. Despite much prior research on providing QoS in storage systems, current storage systems do not support extensive QoS guarantees. We believe this is mainly due to the low I/O efficiency of the various mechanisms designed for QoS. We find that I/O efficiency has received surprisingly little attention in storage QoS research. This is puzzling since the well known characteristics of I/O devices indicate that their efficiency depends crucially on the order in which the requests are served. In this paper, we attempt to alleviate the I/O efficiency concerns of proportional share schedulers. We first study the inherent trade-off between fairness and I/O efficiency. We find that significantly higher I/O efficiency can be achieved by slightly relaxing short-term fairness guarantees. We then develop several low-level mechanisms for proportional share schedulers and present a self-tuning algorithm that achieves good efficiency while still providing fairness guarantees. Experimental results indicate that an I/O efficiency of over 90% is achievable by allowing the scheduler to deviate from proportional service for a few seconds at a time.

Fuzzy self-tuning PID control of the operation temperatures in a two-staged membrane separation process

A two-staged membrane separation process for hydrogen recovery from refinery gases is introduced. The principle of the gas membrane separation process and the influence of the operation temperatures are analyzed. As the conventional PID controller is difficult to make the operation temperatures steady, a fuzzy self-tuning PID control algorithm is proposed. The application shows that the algorithm is effective, the operation temperatures of both stages can be controlled steadily, and the operation flexibility and adaptability of the hydrogen recovery unit are enhanced with safety. This study lays a foundation to optimize the control of the membrane separation process and thus ensure the membrane performance.

Self-tuning gross heat release computation for internal combustion engines

The paper describes a novel method for self-tuning gross heat release computation in internal combustion engines suitable for both online usage in combustion phasing control applications and post-processing of cylinder pressure measurements. The method estimates the polytropic exponents and cylinder pressure offsets immediately preceding and succeeding the combustion event, respectively, using a fast nonlinear least-squares method. The polytropic exponent and the pressure offset are subsequently interpolated during the combustion event and a net heat release computation is performed based on the interpolated exponent and pressure. The result is a self-tuning gross heat release algorithm with no need to model heat losses, crevice losses and blow-by explicitly.

STLS Algorithm for Power-Quality Indices Estimation

This paper introduces a new two-stage, self-tuning least-squares digital signal processing algorithm for power-quality (PQ) indices estimation according to the power components and PQ indices definitions given in the IEEE Standard 1459-2000. The algorithm is based on the nonrecursive least error square technique accompanied with an tuning procedure, which generally improves the algorithm properties: the measurement range, the immunity to a random noise, convergence, and accuracy. The presented algorithm models typical signal distortions and it can be used for the real-time PQ indices estimation. In order to estimate signal spectra and fundamental frequency, current and voltage signals are processed in the first algorithm stage, whereas in the second stage, the power components and PQ indices are calculated based on the results obtained from the first stage. To demonstrate the efficiency of the proposed algorithm, the results of computer simulated and laboratory tests are presented.

Self-tuning PID controllers based on the Lyapunov approach

In this paper, a self-tuning PID controller using just-in-time learning (JITL) technique is proposed. A set of linear models obtained by the JITL provides the information required to adjust the parameters of PID controller. The self-tuning algorithm for the PID parameters is derived by a rigorous analysis based on the Lyapunov method such that the JITL's predicted tracking error converges asymptotically. Simulation results are presented to illustrate the proposed self-tuning PID controller and a comparison with its conventional counterparts is made.

Non-smooth/non-convex economic dispatch by a novel hybrid differential evolution algorithm

This paper presents a novel stochastic optimisation approach to determining the feasible optimal solution of the economic dispatch (ED) problem considering various generator constraints. Many practical constraints of generators, such as ramp rate limits, prohibited operating zones and the valve point effect, are considered. These constraints make the ED problem a non-smooth/non-convex minimisation problem with constraints. The proposed optimisation algorithm is called self-tuning hybrid differential evolution (self-tuning HDE). The self-tuning HDE utilises the concept of the 1/5 success rule of evolution strategies (ESs) in the original HDE to accelerate the search for the global optimum. Three test power systems, including 3-, 13- and 40-unit power systems, are applied to compare the performance of the proposed algorithm with genetic algorithms, the differential evolution algorithm and the HDE algorithm. Numerical results indicate that the entire performance of the proposed self-tuning HDE algorithm outperforms the other three algorithms.

Reduced Complexity Self-Tuning Adaptive Algorithms in Application to Channel Estimation

In this letter, reduced complexity self-tuning algorithms are proposed using simplified parameter updating procedures. Convergence analysis based on the independence assumption and the ordinary differential equation (ODE) method shows that the tuning parameter of the proposed algorithm attains the same limit as the conventional self-tuning adaptive algorithm. Simulations are carried out for channel estimation to support the analysis and performance of the proposed algorithms.

Direct neural network-based self-tuning control for a class of nonlinear systems

Most self-tuning control algorithms for nonlinear systems become invalid when the controlled systems have nonminimum phase property. In this article, a direct neural network-based self-tuning control strategy is developed to deal with this problem under the certainty equivalence principle. Based on an equivalent linearized model from the local linearization, the controller structure is designed using a modified Clarke index with the guaranteed closed-loop stability and without the traditional requirement of the globally boundedness. For the system with unknown parameters, the controller is self-tuned by an on line RBF neural network identifier. Satisfactory simulations illustrate the effectiveness and adaptability of the proposed strategy even under system parameter variations.

Precision-controlled elution of a 82Sr/82Rb generator for cardiac perfusion imaging with positron emission tomography

A rubidium-82 (82Rb) elution system is described for use with positron emission tomography. Due to the short half-life of 82Rb (76 s), the system physics must be modelled precisely to account for transport delay and the associated activity decay and dispersion. Saline flow is switched between a 82Sr/82Rb generator and a bypass line to achieve a constant-activity elution of 82Rb. Pulse width modulation (PWM) of a solenoid valve is compared to simple threshold control as a means to simulate a proportional valve. A predictive-corrective control (PCC) algorithm is developed which produces a constant-activity elution within the constraints of long feedback delay and short elution time. The system model parameters are adjusted through a self-tuning algorithm to minimize error versus the requested time-activity profile. The system is self-calibrating with 2.5% repeatability, independent of generator activity and elution flow rate. Accurate 30 s constant-activity elutions of 10–70% of the total generator activity are achieved using both control methods. The combined PWM–PCC method provides significant improvement in precision and accuracy of the requested elution profiles. The 82Rb elution system produces accurate and reproducible constant-activity elution profiles of 82Rb activity, independent of parent 82Sr activity in the generator. More reproducible elution profiles may improve the quality of clinical and research PET perfusion studies using 82Rb.

Online Self-Tuning Precompensation for a PID Heading Control of a Flying Robot

In this paper, an online self-tuning precompensation for a Proportional-Integral-Derivative (PID) controller is proposed to control heading direction of a flying robot. The flying robot is a highly nonlinear plant, it is a modified X-Cell 60 radio-controlled helicopter. Heading direction is controlled to evaluate efficiency of the proposed precompensation algorithm. The heading control is based on the conventional PID control combined with an online self-tuning precompensation so that both the desired transient and steady state responses can be achieved. The precompensation is applied to compensate unsatisfied performances of the conventional PID controller by adjusting reference command. The precompensator is based on Takagi-Sugeno's type fuzzy model, which learns to tune itself online. The main contribution of the proposed controller is to enhance the controlled performance of the conventional PID controller by adding a self-tuning precompensator on the existing conventional PID controller. The results show that the conventional PID controller with an online self-tuning precompensation has a superior performance than the conventional PID controller. In addition, the online self-tuning precompensation algorithm is implemented simply by adding the precompensator to the existing conventional PID controller and letting the self-tuning mechanism tune itself online.

PREDICTOR-BASED SELF TUNING CONTROL OF POWER PLANT

Design problems of predictor-based self tuning digital control systems for power plants are discussed. Investigation results for self-tuning control algorithms for a nuclear reactor are presented. These algorithms provide the minimum variance of power deviations from the given trajectory in transition and stationary regimes of operation.

Adaptive control of the WEDM process using a self-tuning fuzzy logic algorithm with grey prediction

A control system to improve the efficiency of machining a workpiece with varying thickness in the wire electrical discharge machining (WEDM) process is proposed. The abnormal ratio R ab defined by the proportion of abnormal sparks in a sampling period is taken as the controlled variable. It is allowed to reduce temporarily as the cutting thickness is changing. A gain self-tuning fuzzy control algorithm is used so that the transient situation as the cutting thickness is suddenly increasing can be suppressed immediately, and a stable performance can be achieved. In addition, the grey predictor is adopted to compensate the time-delayed R ab caused by the low-pass filter data processing. Experiments reveal that there is a slight variance in the optimal reference of R ab when the cutting thickness is larger than 20 mm, and its value is set to 55% in these cases. Three cases were tested: the constant machining parameters, the constant R ab and the proposed adaptive R ab . The results show that the cutting speed can be obviously improved by the proposed control strategy.

SELF-TUNING PI CONTROLLER

A self-tuning algorithm comprising the steps of identification and tuning is proposed. The methodology of process parameters identification is based on the measurement of the locus of a perturbed relay system (LPRS) points from a single or multiple asymmetric relay feedback tests. An algorithm of identification of the first order plus dead time process model is developed in details. Also, a tuning algorithm using the criterion of minimal settling time subject to the overshoot constraints is developed and presented in the form of simple formulas for the first-order plus dead time process model. Examples of simulations and application are given.