Showing papers in "Isa Transactions in 2012"

Event-triggered control design of linear networked systems with quantizations

Abstract: This paper is concerned with the control design problem of event-triggered networked systems with both state and control input quantizations. Firstly, an innovative delay system model is proposed that describes the network conditions, state and control input quantizations, and event-triggering mechanism in a unified framework. Secondly, based on this model, the criteria for the asymptotical stability analysis and control synthesis of event-triggered networked control systems are established in terms of linear matrix inequalities (LMIs). Simulation results are given to illustrate the effectiveness of the proposed method.

A robust decentralized load frequency controller for interconnected power systems

Abstract: A novel design of a robust decentralized load frequency control (LFC) algorithm is proposed for an inter-connected three-area power system, for the purpose of regulating area control error (ACE) in the presence of system uncertainties and external disturbances. The design is based on the concept of active disturbance rejection control (ADRC). Estimating and mitigating the total effect of various uncertainties in real time, ADRC is particularly effective against a wide range of parameter variations, model uncertainties, and large disturbances. Furthermore, with only two tuning parameters, the controller provides a simple and easy-to-use solution to complex engineering problems in practice. Here, an ADRC-based LFC solution is developed for systems with turbines of various types, such as non-reheat, reheat, and hydraulic. The simulation results verified the effectiveness of the ADRC, in comparison with an existing PI-type controller tuned via genetic algorithm linear matrix inequalities (GALMIs). The comparison results show the superiority of the proposed solution. Moreover, the stability and robustness of the closed-loop system is studied using frequency-domain analysis.

Fractional order sliding-mode control based on parameters auto-tuning for velocity control of permanent magnet synchronous motor.

Abstract: A fractional order sliding mode control (FROSMC) scheme based on parameters auto-tuning for the velocity control of permanent magnet synchronous motor (PMSM) is proposed in this paper. The control law of the proposed FROSMC scheme is designed according to Lyapunov stability theorem. Based on the property of transferring energy with adjustable type in FROSMC, this paper analyzes the chattering phenomenon in classic sliding mode control (SMC) is attenuated with FROSMC system. A fuzzy logic inference scheme (FLIS) is utilized to obtain the gain of switching control. Simulations and experiments demonstrate that the proposed FROSMC not only achieve better control performance with smaller chatting than that with integer order sliding mode control, but also is robust to external load disturbance and parameter variations.

Adaptive terminal sliding-mode control strategy for DC–DC buck converters

Abstract: This paper presents an adaptive terminal sliding mode control (ATSMC) strategy for DC–DC buck converters. The idea behind this strategy is to use the terminal sliding mode control (TSMC) approach to assure finite time convergence of the output voltage error to the equilibrium point and integrate an adaptive law to the TSMC strategy so as to achieve a dynamic sliding line during the load variations. In addition, the influence of the controller parameters on the performance of closed-loop system is investigated. It is observed that the start up response of the output voltage becomes faster with increasing value of the fractional power used in the sliding function. On the other hand, the transient response of the output voltage, caused by the step change in the load, becomes faster with decreasing the value of the fractional power. Therefore, the value of fractional power is to be chosen to make a compromise between start up and transient responses of the converter. Performance of the proposed ATSMC strategy has been tested through computer simulations and experiments. The simulation results of the proposed ATSMC strategy are compared with the conventional SMC and TSMC strategies. It is shown that the ATSMC exhibits a considerable improvement in terms of a faster output voltage response during load changes.

Improved correlation analysis and visualization of industrial alarm data.

Abstract: The problem of multivariate alarm analysis and rationalization is complex and important in the area of smart alarm management due to the interrelationships between variables. The technique of capturing and visualizing the correlation information, especially from historical alarm data directly, is beneficial for further analysis. In this paper, the Gaussian kernel method is applied to generate pseudo continuous time series from the original binary alarm data. This can reduce the influence of missed, false, and chattering alarms. By taking into account time lags between alarm variables, a correlation color map of the transformed or pseudo data is used to show clusters of correlated variables with the alarm tags reordered to better group the correlated alarms. Thereafter correlation and redundancy information can be easily found and used to improve the alarm settings; and statistical methods such as singular value decomposition techniques can be applied within each cluster to help design multivariate alarm strategies. Industrial case studies are given to illustrate the practicality and efficacy of the proposed method. This improved method is shown to be better than the alarm similarity color map when applied in the analysis of industrial alarm data.

RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

Abstract: In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance.

Design of PID controllers in double feedback loops for SISO systems with set-point filters

Abstract: A PID controller is widely used to control industrial processes that are mostly open loop stable or unstable. Selection of proper feedback structure and controller tuning helps to improve the performance of the loop. In this paper a double-feedback loop/method is used to achieve stability and better performance of the process. The internal feedback is used for stabilizing the process and the outer loop is used for good setpoint tracking. An internal model controller (IMC) based PID method is used for tuning the outer loop controller. Autotuning based on relay feedback or the Ziegler–Nichols method can be used for tuning an inner loop controller. A tuning parameter ( λ ) that is used to tune IMC-PID is used as a time constant of a setpoint filter that is used for reducing the peak overshoot. The method has been tested successfully on many low order processes.

PI and fuzzy logic controllers for shunt Active Power Filter--a report.

Abstract: This paper presents a shunt Active Power Filter (APF) for power quality improvements in terms of harmonics and reactive power compensation in the distribution network. The compensation process is based only on source current extraction that reduces the number of sensors as well as its complexity. A Proportional Integral (PI) or Fuzzy Logic Controller (FLC) is used to extract the required reference current from the distorted line-current, and this controls the DC-side capacitor voltage of the inverter. The shunt APF is implemented with PWM-current controlled Voltage Source Inverter (VSI) and the switching patterns are generated through a novel Adaptive-Fuzzy Hysteresis Current Controller (A-F-HCC). The proposed adaptive-fuzzy-HCC is compared with fixed-HCC and adaptive-HCC techniques and the superior features of this novel approach are established. The FLC based shunt APF system is validated through extensive simulation for diode-rectifier/R-L loads.

Decentralized PI/PID controllers based on gain and phase margin specifications for TITO processes.

Abstract: In this paper, a decentralized PI/PID controller design method based on gain and phase margin specifications for two-input-two-output (TITO) interactive processes is proposed. The decouplers are designed for systems to minimize the interaction between the loops, and the first order plus dead time (FOPDT) model is achieved for each decoupled subsystem based on the frequency response fitting. An independent PI/PID controller is designed for each reduced order decoupled subsystem to obtain the desired gain and phase margins, and the performance is verified on the original interactive system to show the effectiveness of the proposed design method for the general class of TITO systems. Simulation examples are incorporated to validate the usefulness of the presented algorithm. An experimentation is performed on the Level-Temperature reactor process to show the practical applicability of the proposed method for the interactive system.

Centralized PI controllers for interacting multivariable processes by synthesis method.

Abstract: In this article, two methods of designing a centralized control system for multi-input, multi-output (MIMO) processes are presented. Centralized proportional–integral (PI) controllers are designed based on a direct synthesis method. The inverse of the process transfer function matrix in the direct synthesis method is approximated based on the relative gain array concept. The method is further improved by using a relative normalized gain array, and an equivalent transfer function for each element in the process transfer function matrix is derived for the closed-loop control system. The transpose of the effective transfer function is used to approximate the inverse of the process transfer function matrix. The simulation studies demonstrate the effectiveness of this method. The proposed centralized controllers reduce the interactions better than recently reported decentralized controllers do. A centralized controller designed based on a relative normalized gain array (RNGA) gives a better performance than a centralized controller designed based on a relative gain array (RGA).

Sliding mode fault tolerant control dealing with modeling uncertainties and actuator faults

Abstract: In this paper, two sliding mode control algorithms are developed for nonlinear systems with both modeling uncertainties and actuator faults The first algorithm is developed under an assumption that the uncertainty bounds are known Different design parameters are utilized to deal with modeling uncertainties and actuator faults, respectively The second algorithm is an adaptive version of the first one, which is developed to accommodate uncertainties and faults without utilizing exact bounds information The stability of the overall control systems is proved by using a Lyapunov function The effectiveness of the developed algorithms have been verified on a nonlinear longitudinal model of Boeing 747-100/200

Optimal control in microgrid using multi-agent reinforcement learning

Abstract: This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode.

Type-2 fuzzy model based controller design for neutralization processes.

Abstract: In this study, an inverse controller based on a type-2 fuzzy model control design strategy is introduced and this main controller is embedded within an internal model control structure. Then, the overall proposed control structure is implemented in a pH neutralization experimental setup. The inverse fuzzy control signal generation is handled as an optimization problem and solved at each sampling time in an online manner. Although, inverse fuzzy model controllers may produce perfect control in perfect model match case and/or non-existence of disturbances, this open loop control would not be sufficient in the case of modeling mismatches or disturbances. Therefore, an internal model control structure is proposed to compensate these errors in order to overcome this deficiency where the basic controller is an inverse type-2 fuzzy model. This feature improves the closed-loop performance to disturbance rejection as shown through the real-time control of the pH neutralization process. Experimental results demonstrate the superiority of the inverse type-2 fuzzy model controller structure compared to the inverse type-1 fuzzy model controller and conventional control structures.

Improved model reduction and tuning of fractional-order PIλDμ controllers for analytical rule extraction with genetic programming

Abstract: Genetic algorithm (GA) has been used in this study for a new approach of suboptimal model reduction in the Nyquist plane and optimal time domain tuning of proportional–integral–derivative (PID) and fractional-order (FO) P I λ D μ controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H2-norm-based reduced parameter modeling technique. With the tuned controller parameters and reduced-order model parameter dataset, optimum tuning rules have been developed with a test-bench of higher-order processes via genetic programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort. From the reported Pareto optimal front of the GP-based optimal rule extraction technique, a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP-based tuning rules has been compared with the original GA-based control performance for the PID and P I λ D μ controllers, handling four different classes of representative higher-order processes. These rules are very useful for process control engineers, as they inherit the power of the GA-based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three-dimensional plots of the required variation in PID/fractional-order PID (FOPID) controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP-based tuning rules has also been shown with credible simulation examples.

Adaptive cruise control with stop&go function using the state-dependent nonlinear model predictive control approach

Abstract: In the design of adaptive cruise control (ACC) system two separate control loops – an outer loop to maintain the safe distance from the vehicle traveling in front and an inner loop to control the brake pedal and throttle opening position – are commonly used. In this paper a different approach is proposed in which a single control loop is utilized. The objective of the distance tracking is incorporated into the single nonlinear model predictive control (NMPC) by extending the original linear time invariant (LTI) models obtained by linearizing the nonlinear dynamic model of the vehicle. This is achieved by introducing the additional states corresponding to the relative distance between leading and following vehicles, and also the velocity of the leading vehicle. Control of the brake and throttle position is implemented by taking the state-dependent approach. The model demonstrates to be more effective in tracking the speed and distance by eliminating the necessity of switching between the two controllers. It also offers smooth variation in brake and throttle controlling signal which subsequently results in a more uniform acceleration of the vehicle. The results of proposed method are compared with other ACC systems using two separate control loops. Furthermore, an ACC simulation results using a stop&go scenario are shown, demonstrating a better fulfillment of the design requirements.

Control chart pattern recognition using K-MICA clustering and neural networks.

Abstract: Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.

Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform.

Abstract: In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor.

A novel practical control approach for rate independent hysteretic systems.

Abstract: A disturbance rejection based control approach, active disturbance rejection control (ADRC), is proposed for hysteretic systems with unknown characteristics. It is an appealing alternative to hysteresis compensation because it does not require a detailed model of hysteresis, by treating the nonlinear hysteresis as a common disturbance and actively rejecting it. The stability characteristic of the ADRC is analyzed. It is shown that, in the face of the inherent dynamic uncertainties, the estimation and closed-loop tracking errors of ADRC are bounded, with their bounds monotonously decreasing with the observer and controller bandwidths, respectively. Simulation results on a typical hysteretic system further demonstrate the effectiveness of the proposed approach.

Applying mean shift, motion information and Kalman filtering approaches to object tracking

Abstract: Contemporary research is developing techniques to tracking objects in videos using color features, and the mean shift (MS) algorithm is one of the best. This known algorithm is employed to find the location of an object, in image sequence, by using a coefficient called the Bhattacharyya coefficient. This coefficient is calculated through an object tracking algorithm to present the similarity in appearance between an object and its candidate model, where the best representation of an object is acquired, once this is could be maximized. However, the MS algorithm performance is confounded by color clutter in background, various illuminations, occlusion types and other related limitations. Because of such effects, the algorithm necessarily decreases the value of the Bhattacharyya coefficient, indicating reduced certainty in the object tracking. In the present research, an improved convex kernel function is proposed to overcome the partial occlusion. Afterwards, in order to improve the MS algorithm against the low saturation and also sudden light, changes are made from motion information of the desired sequence. By using both the color feature and the motion information simultaneously, the capability of the MS algorithm is correspondingly increased, in the present approach. Moreover, by assuming a constant speed for the object, a robust estimator, i.e., the Kalman filter, is realized to solve the full occlusion problem. At the end, experimental results on various videos verify that the proposed method has an optimum performance in real-time object tracking, while the result of the original MS algorithm may be unsatisfied.

Modified Smith predictor based cascade control of unstable time delay processes.

Abstract: An improved cascade control structure with a modified Smith predictor is proposed for controlling open-loop unstable time delay processes. The proposed structure has three controllers of which one is meant for servo response and the other two are for regulatory responses. An analytical design method is derived for the two disturbance rejection controllers by proposing the desired closed-loop complementary sensitivity functions. These two closed-loop controllers are considered in the form of proportional–integral-derivative (PID) controller cascaded with a second order lead/lag filter. The direct synthesis method is used to design the setpoint tracking controller. By virtue of the enhanced structure, the proposed control scheme decouples the servo response from the regulatory response in case of nominal systems i.e., the setpoint tracking controller and the disturbance rejection controller can be tuned independently. Internal stability of the proposed cascade structure is analyzed. Kharitonov’s theorem is used for the robustness analysis. The disturbance rejection capability of the proposed scheme is superior as compared to existing methods. Examples are also included to illustrate the simplicity and usefulness of the proposed method.

Motion control of planar parallel robot using the fuzzy descriptor system approach.

Abstract: This work presents the control of a two-degree of freedom parallel robot manipulator. A quasi-LPV approach, through the so-called TS fuzzy model and LMI constraints problems is used. Moreover, in this context a way to derive interesting control laws is to keep the descriptor form of the mechanical system. Therefore, new LMI problems have to be defined that helps to reduce the conservatism of the usual results. Some relaxations are also proposed to leave the pure quadratic stability/stabilization framework. A comparison study between the classical control strategies from robotics and the control design using TS fuzzy descriptor models is carried out to show the interest of the proposed approach.

PWM-Switching pattern-based diagnosis scheme for single and multiple open-switch damages in VSI-fed induction motor drives

Abstract: This paper deals with a fault detection technique for insulated-gate bipolar transistors (IGBTs) open-circuit faults in voltage source inverter (VSI)-fed induction motor drives. The novelty of this idea consists in analyzing the pulse-width modulation (PWM) switching signals and the line-to-line voltage levels during the switching times, under both healthy and faulty operating conditions. The proposed method requires line-to-line voltage measurement, which provides information about switching states and is not affected by the load. The fault diagnosis scheme is achieved using simple hardware and can be included in the existing inverter system without any difficulty. In addition, it allows not only accurate single and multiple faults diagnosis but also minimization of the fault detection time to a maximum of one switching period ( T c ). Simulated and experimental results on a 3-kW squirrel-cage induction motor drive are displayed to validate the feasibility and the effectiveness of the proposed strategy.

Design of a simple setpoint filter for minimizing overshoot for low order processes.

Abstract: Setpoint filters are widely used along with a PID controller. The aim of the present paper is to reduce the peak overshoot to a desired/tolerable limit. To design a setpoint filter, numerous methods are available, which need extensive calculations. Moreover, the existing methods need information regarding the process parameters, values of controller settings and are laborious. But the proposed method is very simple and requires only the information about the peak overshoot and peak time of the system response regardless of type and order of the system with arbitrary PID parameters. Several examples are taken to show efficacy of the process.

A novel KFCM based fault diagnosis method for unknown faults in satellite reaction wheels.

Abstract: Reaction wheels are one of the most critical components of the satellite attitude control system, therefore correct diagnosis of their faults is quintessential for efficient operation of these spacecraft. The known faults in any of the subsystems are often diagnosed by supervised learning algorithms, however, this method fails to work correctly when a new or unknown fault occurs. In such cases an unsupervised learning algorithm becomes essential for obtaining the correct diagnosis. Kernel Fuzzy C-Means (KFCM) is one of the unsupervised algorithms, although it has its own limitations; however in this paper a novel method has been proposed for conditioning of KFCM method (C-KFCM) so that it can be effectively used for fault diagnosis of both known and unknown faults as in satellite reaction wheels. The C-KFCM approach involves determination of exact class centers from the data of known faults, in this way discrete number of fault classes are determined at the start. Similarity parameters are derived and determined for each of the fault data point. Thereafter depending on the similarity threshold each data point is issued with a class label. The high similarity points fall into one of the 'known-fault' classes while the low similarity points are labeled as 'unknown-faults'. Simulation results show that as compared to the supervised algorithm such as neural network, the C-KFCM method can effectively cluster historical fault data (as in reaction wheels) and diagnose the faults to an accuracy of more than 91%.

The weakest t-norm based intuitionistic fuzzy fault-tree analysis to evaluate system reliability

Abstract: In this paper, a new approach of intuitionistic fuzzy fault-tree analysis is proposed to evaluate system reliability and to find the most critical system component that affects the system reliability. Here weakest t-norm based intuitionistic fuzzy fault tree analysis is presented to calculate fault interval of system components from integrating expert's knowledge and experience in terms of providing the possibility of failure of bottom events. It applies fault-tree analysis, α-cut of intuitionistic fuzzy set and T(ω) (the weakest t-norm) based arithmetic operations on triangular intuitionistic fuzzy sets to obtain fault interval and reliability interval of the system. This paper also modifies Tanaka et al.'s fuzzy fault-tree definition. In numerical verification, a malfunction of weapon system "automatic gun" is presented as a numerical example. The result of the proposed method is compared with the listing approaches of reliability analysis methods.

Design and analysis of a model predictive controller for active queue management.

Abstract: Model predictive (MP) control as a novel active queue management (AQM) algorithm in dynamic computer networks is proposed. According to the predicted future queue length in the data buffer, early packets at the router are dropped reasonably by the MPAQM controller so that the queue length reaches the desired value with minimal tracking error. The drop probability is obtained by optimizing the network performance. Further, randomized algorithms are applied to analyze the robustness of MPAQM successfully, and also to provide the stability domain of systems with uncertain network parameters. The performances of MPAQM are evaluated through a series of simulations in NS2. The simulation results show that the MPAQM algorithm outperforms RED, PI, and REM algorithms in terms of stability, disturbance rejection, and robustness.

Decomposition of an ARX model on Laguerre orthonormal bases.

Abstract: In this paper, we propose a new reduced complexity model by expanding a discrete-time ARX model on Laguerre orthonormal bases. To ensure an efficient complexity reduction, the coefficients associated to the input and the output of the ARX model are expanded on independent Laguerre bases, to develop a new black-box linear ARX-Laguerre model with filters on model input and output. The parametric complexity reduction with respect to the classical ARX model is proved theoretically. The structure and parameter identification of the ARX-Laguerre model is achieved by a new proposed approach which consists in solving an optimization problem built from the ARX model without using system input/output observations. The performances of the resulting ARX-Laguerre model and the proposed identification approach are illustrated by numerical simulations and validated on benchmark manufactured by Feedback known as Process Trainer PT326. A possible extension of the proposed model to a multivariable process is formulated.

Cascade control of superheated steam temperature with neuro-PID controller.

Abstract: The reaction of alpha,beta-unsaturated ketoximes with fluoroalkanosulfonyl fluorides in the presence of 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) underwent the Beckmann rearrangement smoothly to afford the corresponding acid-sensitive enamides in moderate to excellent yields, which provides a new efficient method for the preparation of acid-sensitive enamides. (C) 2014 Elsevier Ltd. All rights reserved.

Stochastic behavior analysis of complex repairable industrial systems utilizing uncertain data.

Abstract: The purpose of this paper is to present a novel technique for analyzing the behavior of an industrial system stochastically by utilizing vague, imprecise, and uncertain data. In the present study two important tools namely Lambda-Tau methodology and particle swarm optimization are combinedly used to present a novel technique named as particle swarm optimization based Lambda-Tau (PSOBLT) for analyzing the behavior of a complex repairable system stochastically up to a desired degree of accuracy. Expressions of reliability indices like failure rate, repair time, mean time between failures (MTBF), expected number of failures (ENOF), reliability and availability for the system are obtained by using Lambda-Tau methodology and particle swarm optimization is used to construct their membership function. The interaction among the working units of the system is modeled with the help of Petri nets. The feeding unit of a paper mill situated in a northern part of India, producing approximately 200 ton of paper per day, has been considered to demonstrate the proposed approach. Sensitivity analysis of system's behavior has also been done. The behavior analysis results computed by PSOBLT technique have a reduced region of prediction in comparison of existing technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties.

A fast converging robust controller using adaptive second order sliding mode.

Abstract: This paper proposes an adaptive second order sliding mode (SOSM) controller with a nonlinear sliding surface. The nonlinear sliding surface consists of a gain matrix having a variable damping ratio. Initially the sliding surface uses a low value of damping ratio to get a quick system response. As the closed loop system approaches the desired reference, the value of the damping ratio gets increased with an aim to reducing the overshoot and the settling time. The time derivative of the control signal is used to design the controller. The actual control input obtained by integrating the derivative control signal is smooth and chattering free. The adaptive tuning law used by the proposed controller eliminates the need of prior knowledge about the upper bound of system uncertainties. Simulation results demonstrate the effectiveness of the proposed control strategy.