Unstructured Uncertainties Research Articles

Controlling chaos based on a novel intelligent integral terminal sliding mode control in a rod-type plasma torch

An integral terminal sliding mode controller is proposed in order to control chaos in a rod-type plasma torch system. In this method, a new sliding surface is defined based on a combination of the conventional sliding surface in terminal sliding mode control and a nonlinear function of the integral of the system states. It is assumed that the dynamics of a chaotic system are unknown and also the system is exposed to disturbance and unstructured uncertainty. To achieve a chattering-free and high-speed response for such an unknown system, an adaptive neuro-fuzzy inference system is utilized in the next step to approximate the unknown part of the nonlinear dynamics. Then, the proposed integral terminal sliding mode controller stabilizes the approximated system based on Lyapunov’s stability theory. In addition, a Bee algorithm is used to select the coefficients of integral terminal sliding mode controller to improve the performance of the proposed method. Simulation results demonstrate the improvement in the response speed, chattering rejection, transient response, and robustness against uncertainties.

Robust Control of an Unmanned Underwater Vehicles with Parametric Uncertainty

The complex dynamics coupled with operational condition have made an unmanned underwater vehicle susceptible to perturbation due to parametric uncertainty. This has necessitated the need for a robust control technique capable of not only guarantee robustness to unstructured uncertainty, but also account for inherent parametric uncertainty in the system dynamics. This paper presents an optimized based extended H-infinity loop-shaping design procedure for the synthesis of a control system capable of addressing the problem of parametric perturbations in the unmanned underwater vehicle dynamics. The design problem formulation and procedure, together with performance evaluation are highlighted in the paper. Validation of the robustness is examined explicitly using structured singular value analysis.

Adaptive robust control of fully constrained cable robots: singular perturbation approach

In this paper, adaptive robust control of fully constrained cable-driven parallel robots with elastic cables is studied in detail. A composite controller is proposed for the system under the assumption of linear axial spring model as the dominant dynamics of the cables and in presence of model uncertainties. The proposed controller which is designed based on the singular perturbation theory, consists of two main parts. An adaptive robust controller is designed to counteract the unstructured and parametric uncertainties of the robot and a fast control term which is added to control the longitudinal vibrations of the cables. Moreover, to ensure that all cables remain in tension, the proposed control algorithm benefits from internal force concept. Using the results of the singular perturbation theory, the stability of the overall closed-loop system is analyzed through Lyapunov second method, and finally, the effectiveness of the proposed control algorithm is verified through some simulations on a planar cable-driven parallel robot.

Optimal controllers designs for automatic reactive power control in an isolated wind-diesel hybrid power system

The paper presents the bacterial foraging optimization algorithm (BFOA) and particle swarm optimization (PSO) algorithm based robust controllers for voltage deviations due to the variation of reactive power in an isolated wind-diesel hybrid power system. The isolated wind-diesel system consists of wind energy conversion system (WECS) utilizing a permanent magnet induction generator (PMIG). Further, a synchronous generator (SG) is used with the diesel engine set for power generation. The mismatch between generated and consumed reactive power in the system causes voltage fluctuations, which will occur at generator terminals. These oscillations further causes reduction in the stability and quality of the power supply. The static synchronous compensator (STATCOM) and an automatic voltage regulator (AVR) are used to suppress voltage fluctuations in an isolated wind-diesel hybrid power system. The STATCOM is used as a reactive power compensator and the AVR is used to keep the terminal voltage constant for the synchronous generator. Both STATCOM and AVR are having proportional and integral (PI) controllers with single input. In modeling for the system, a normalized co-prime factorization is applied to show the possible unstructured uncertainties in the power system such as variation of system parameters and generating and loading conditions. The performance and robust stability conditions of the control system are formulated as the optimization problem, which is based on the Hα loop shaping. BFOA and PSO algorithms are implemented to solve this optimization problem and to achieve PI control parameters of STATCOM and AVR simultaneously. In order to show the efficiency of the proposed controllers, the performance of the proposed controllers is compared with the performance of the conventional controller and genetic algorithm (GA) based PI controllers for the same wind-diesel system. The dynamic responses of the system for four different small-disturbance case studies has been carried out in MATLAB environment.

Reduced-order synchronization of time-delay chaotic systems with known and unknown parameters

In this article, a novel robust nonlinear controller approach is focused to study theoretically, the reduced-order synchronization phenomena of two unrelated time-delayed chaotic systems under the determined and unknown parameters. It is assumed that the two systems are perturbed by the bounded unstructured uncertainties and unknown external disturbance. Based on the Lyapunov–Krasovskii functional theory, a robust nonlinear synchronization controller is focused and a suitable Lyapunov functional is constructed so that they establish the globally asymptotical stability of the closed-loop at the origin. Subsequently, suitable adaptive laws of unknown parameters are designed to identify the unknown parameters. Finally, the effectiveness of the proposed reduced-order synchronization approach is verified by numerical simulations. A brief comparison of the present study with prior works has been given.

Certainty equivalence adaptation combined with super-twisting sliding-mode control

ABSTRACTIn this paper, a Lyapunov-based control concept is presented that combines variable structure and adaptive control. The considered system class consists of nonlinear single input systems which are affected by matched structured and unstructured uncertainties. Resorting to the certainty equivalence principle, the controller exploits advantages of both the sliding-mode and the adaptive control methodology. It is demonstrated that the gains of the discontinuous control action may be reduced remarkably when compared with pure sliding-mode-based approaches. The efficiency of the presented concept is demonstrated in detail, using results of numerical simulations.

Adaptive Control of Venturini Modulation Based Matrix Converters Using Interval Type-2 Fuzzy Sets

In this paper, an adaptive interval type-2 fuzzy control scheme is proposed for matrix converters. This strategy combines the power of adaptive control theory with type-2 fuzzy logic systems to achieve accurate tracking and robustness to both structured and unstructured uncertainties. Unlike other control techniques, no a priori offline training, weights initialization, parameters, or dynamics’ knowledge is required. Results for different situations highlight the performance of the proposed controller in transient and steady-state conditions under grid voltage uncertainties. Furthermore, the adaptive type-2 fuzzy controller is compared against its type-1 counterpart.

System Identification of Vessel Steering With Unstructured Uncertainties by Persistent Excitation Maneuvers

System identification of vessel steering associated with unstructured uncertainties is considered in this paper. The initial model of vessel steering is derived by a modified second-order Nomoto model (i.e., nonlinear vessel steering with stochastic state-parameter conditions). However, that model introduces various difficulties in system identification, due to the presence of a large number of states and parameters and system nonlinearities. Therefore, partial feedback linearization is proposed to simplify the proposed model, where the system-model unstructured uncertainties can also be separated. Furthermore, partial feedback linearization reduces the number of states and parameters and the system nonlinearities, given the resulting reduced-order state model. Then, the system identification approach is carried out, for both models (i.e., full state model and reduced-order state model), resorting to an extended Kalman filter (EKF). As illustrated in the results, the reduced-order model was able to successfully identify the required states and parameters when compared to the full state model in vessel steering under persistent excitation maneuvers. Therefore, the proposed approach can be used in a wide range of system identification applications.

Adaptive Fuzzy Output Feedback Control for Switched Nonlinear Systems With Unmodeled Dynamics.

This paper investigates a robust adaptive fuzzy control stabilization problem for a class of uncertain nonlinear systems with arbitrary switching signals that use an observer-based output feedback scheme. The considered switched nonlinear systems possess the unstructured uncertainties, unmodeled dynamics, and without requiring the states being available for measurement. A state observer which is independent of switching signals is designed to solve the problem of unmeasured states. Fuzzy logic systems are used to identify unknown lumped nonlinear functions so that the problem of unstructured uncertainties can be solved. By combining adaptive backstepping design principle and small-gain approach, a novel robust adaptive fuzzy output feedback stabilization control approach is developed. The stability of the closed-loop system is proved via the common Lyapunov function theory and small-gain theorem. Finally, the simulation results are given to demonstrate the validity and performance of the proposed control strategy.

Bridging the Gaps between Impact Assessments and Resettlement Planning: A Case Study of Padma Multipurpose Bridge Project, Bangladesh

The traditional methodologies including spatial decision support systems have at times showed their ineffectiveness in terms of gathering, analyzing and incorporating diversified views of affected persons into resettlement planning. This ineffectiveness is in dealing with unstructured problems and uncertainty in analyzing alternative strategies. This paper therefore proposes an integrated methodology (combination of argumentative mapping and qualitative probabilistic networks) to counter these limitations. The new methodology was introduced in a large-scale infrastructure project to verify its applicability. Results derived from the study not only provide better insights into resettlement-related problems, but also indicate critical management options for desired objectives.

Adaptive Control of Active Dental Joint

In this paper, a PID model-based adaptive robust control method is proposed in order to design a high performance robust controller in the presence of structured (parametric) uncertainties and unstructured uncertainties. The approach improves performance by using the advantages of sliding mode control, adaptive control, and PID controller, while the disadvantages attributed to these methods are remedied by each other. This is achieved without increasing the complexities of the overall design and analysis of the control system (controller). The proposed controller attenuates the effect of model uncertainties from both structured uncertainties and unstructured uncertainties. Thus, transient performance and final tracking accuracy is guaranteed by proper design of the controller. Therefore, asymptotic tracking (or zero final tracking error) can be achieved without using high-gain feedback. The design is conceptually simple and is reliable in applications because of its high performance and strong robustness.

Decentralized voltage control of distributed generation using a distribution system structural MIMO model

The voltage control problem in Low Voltage (LV) distribution systems is becoming increasingly important due to the presence of Distributed Generation (DG). Recently, DG units have been proposed to contribute to voltage control according to a Volt/Var law which does not realize regulation. Moreover, since the existing LV systems are operated in a decentralized way without communication links, the simultaneous response of the controllers of the DG units may result into operational conflicts and instability. To overcome these problems, the present paper illustrates a design methodology for decentralized voltage controllers that act on DG reactive power injections. The controllers are suitable for the LV systems since they ensure voltage regulation and stability by using only local measurements and without information exchanges. The design is based on a proposed structural MIMO model of the distribution system. Robust stability is also analyzed: changes in the operating conditions of the distribution system are modeled as unstructured additive uncertainties affecting the MIMO model. A case study gives evidence of the applicability of the proposed design; the performance of the controllers in terms of both stability and regulation of the nodal voltages of three DG units connected to a LV distribution feeder is tested by numerical simulations; finally, a comparison with a Volt/Var technique is performed.

Adaptive Neural Network Control of a Compact Bionic Handling Arm

In this paper, autonomous control problem of a class of bionic continuum robots named “Compact Bionic Handling Arm” (CBHA) is addressed. These robots can reproduce biological behaviors of trunks, tentacles, or snakes. The modeling problem associated with continuum robots includes nonlinearities, structured and unstructured uncertainties, and the hyperredundancy. In addition to these problems, the CBHA comprises the hysteresis behavior of its actuators and a memory phenomenon related to its structure made of polyamide materials. These undesirable effects make it difficult to design a control system based on quantitative models of the CBHA. Thus, two subcontrollers are proposed in this paper. One, encapsulated in the other, and both implemented in real time allow controlling of the CBHA's end-effector position. The first subcontroller controls the CBHA's kinematics based on a distal supervised learning scheme. The second subcontroller controls the CBHA's kinetics based on an adaptive neural control. These subcontrollers allow a better assessment of the stability of the control architecture while ensuring the convergence of Cartesian errors. The obtained experimental results using a CBHA robot show an accurate tracking of the CBHA's end-effector position.

The Sine Wave Tuning method: Robust PID controller design in the frequency domain

The paper presents a novel robust PID controller design method for nominal performance specified in terms of maximum overshoot and settling time. The PID controller design provides guaranteed gain margin GM. The parameter of the tuning rules is a suitably chosen point of the plant frequency response obtained by a sine-wave signal with excitation frequency ωn. Then, the designed controller moves this point into the phase crossover with the required gain margin GM. The couple (ωn;GM) is specified with respect to closed-loop performance requirements in terms of ηmax (maximum overshoot) and ts (settling time) according to developed parabolic dependences. The new approach has been verified on a vast batch of benchmark examples; subsequently, the developed algorithm has been extended to robust PID controller design for plants with unstable zero and unstructured uncertainties.

Iterative Learning Control for Strict-Feedback Nonlinear Systems with Both Structured and Unstructured Uncertainties

In this paper, the problem of designing a new iterative learning control has been investigated for a class of strict-feedback nonlinear systems subject to both structured and unstructured uncertainties and dynamic disturbances. The considered systems are assumed to perform the same operation repeatedly under alignment condition. Simple learning mechanisms are proposed to estimate the unknown state-dependent nonlinear functions satisfying local Lipschitz conditions. By using the concept of command filtered backstepping, the problem of the explosion of complexity existing in conventional backstepping is eliminated and the proposed controller is greatly simplified. Lyapunov-like functional method is used to prove the boundedness of all signals of the resulting closed-loop system and the convergence of the tracking errors to zero over iterations. Simulation results are provided to show the effectiveness of the proposed control scheme.

Automated Two-Degree-of-Freedom Model Predictive Control Tuning

This work considers the automated tuning of a two-degree-of-freedom model predictive controller (MPC) for single-input, single-output industrial processes with model uncertainties. The objective of the tuning algorithm is to automatically determine the MPC tuning parameters such that (1) the robust stability can be guaranteed, (2) the worst-case overshoot is controlled, (3) the oscillations in process outputs are attenuated, and (4) the worst-case settling time is minimized. A rigorous robust stability analysis is first conducted based on the connection between parametric uncertainties and unstructured uncertainties, and a tight robust stability condition is derived. As the specification on process output variation is not easily made by the end users, two alternative methods are proposed to automatically determine the tolerable total variation, which lead to two automatic tuning algorithms that achieve the tuning objectives. The proposed results are tested and verified through examples extracted from industrial processes in the pulp and paper industry, and comparisons are made with other existing results.

Design of an RMPC with a time‐varying terminal constraint set for tracking problem

SummaryThis paper presents a robust model predictive control algorithm with a time‐varying terminal constraint set for systems with model uncertainty and input constraints. In this algorithm, the nonlinear system is approximated by a linear model where the approximation error is considered as an unstructured uncertainty that can be represented by a Lipschitz nonlinear function. A continuum of terminal constraint sets is constructed off‐line, and robust stability is achieved on‐line by using a variable control horizon. This approach significantly reduces the computational complexity. The proposed robust model predictive controller with a terminal constraint set is used in tracking set‐points for nonlinear systems. The effectiveness of the proposed method is illustrated with a numerical example. Copyright © 2015 John Wiley & Sons, Ltd.

Adaptive robust control with extended disturbance observer for motion control of DC motors

This Letter proposes an extended disturbance observer (EDO) based adaptive robust control method for motion control of DC motors. An adaptive part for structured uncertainties, an EDO for unstructured uncertainties and a feedback robust law for global robustness are designed in this method. The theoretical analysis guarantees a prescribed tracking performance under various uncertainties and comparative numerical simulation shows the excellent high-precision performance of proposed method on DC motor motion control.

Output torque tracking control of direct-drive rotary torque motor with dynamic friction compensation

Accurate output torque tracking control problem of Direct-Drive Rotary (DDR) torque motor is concerned in this paper. An adaptive robust torque control algorithm with LuGre model based dynamic friction compensation is proposed to improve the torque tracking accuracy. Adaptive robust control (ARC) backstepping design technique and Lyapunov stability theorem are employed to design the controller to deal with both structured and unstructured uncertainties in a practical control system. In the ARC algorithm, discontinuous projection based parameter adaptive law is utilized to estimate unknown system parameters, and robust control law ensures the robustness of closed-loop control system. In order to further improve the torque tracking control performance, LuGre model based dynamic friction compensation is designed to estimate and compensate friction torque in the practical torque motor system. The mathematically design procedure of the proposed algorithm is developed. Finally, the proposed algorithm is implemented on a practical DDR torque motor control system. Experimental results with three compared control algorithms validate that the proposed algorithm can effectively improve the torque tracking performance and LuGre model based dynamic friction compensation is also necessary for high-accuracy torque tracking control.

Smooth Switching Robust Adaptive Control for Omnidirectional Mobile Robots

Issues of modeling and control design for omnidirectional mobile robots with both structured and unstructured uncertainties are presented. First, system dynamics with an arbitrary location of the center of mass (CM) are derived. The system parameters, including the location of CM and those in the motor dynamics, are assumed unknown. The commonly adopted adaptive linearizing control suffers from the so-called control singularity under such circumstances. A smooth switching adaptive robust controller is then proposed to alleviate such a difficulty. It consists of four parts, a nominal adaptive linearizing controller, a deputy adaptive sliding-mode controller being in charge near singularity, a switching algorithm monitoring the exchange of control authority between the above two controllers, and a standard robust controller for handing the unstructured uncertainty. Not only does it significantly reduce the control voltages near singularity, but it also achieves better transients than a single adaptive controller and less control effort than a single robust controller. Both the simulation and experiments on an omnidirectional mobile robot are provided to demonstrate the validity of the proposed design.