Control Engineering Problems Research Articles

Fractal research of transverse sections of a long river channel below a dam

Research on the propagation law of unsteady flow from dam discharge has significance for the engineering problems of shipping and urban flood control. Due to the problem of many factors influencing the process of unsteady flow, we propose using fractal dimensions to replace various hydraulic factors. In this paper, we choose a navigation-obstructing beach section in a river as the research object, establish a two-dimensional unsteady fractal mathematical model to obtain statistics on the channel width at the minimum maintenance water depth and then calculate the fractal dimension of the river cross section. The results show the first-order feature, and it is also shown that the fractal dimension of the river width has a strong relationship with the depth at the beach section and the shape of the cross section. In general, the shoal’s fractal dimension increases with the depth. The fractal dimension of the transverse sections has the trend of gradually reducing as a whole with the velocity. Compared to the fractal dimension of the beach in the same river section, the river cross-section topography is smoother and the section fractal dimension is smaller when the upstream flow is the same.

Decentralised adaptive robust control schemes of uncertain large‐scale time‐delay systems with multiple unknown dead‐zone inputs

A simple design method is presented such that the decentralised adaptive robust control schemes with a relatively simple structure can be synthesised for a class of uncertain large-scale interconnected time-delay systems with multiple dead-zone inputs. It is supposed that any information on dead-zone input non-linearities is completely unknown. On the basis of adaptive technique, a self-tuning control gain function is introduced for each subsystem to construct a simple local state feedback controller which is structurally linear in the local state variables. The simple design method presented in this study might be well understood by engineering designers, and the resulting simple control schemes can be easily implemented in a number of practical engineering control problems. By combining the presented design method with other control methods, one may expect to obtain a number of interesting results for a rather large class of uncertain interconnected dynamical systems with completely unknown dead-zone in the actuators. Finally, the simulations of some numerical examples are provided to show the design procedure of the presented method and to demonstrate the validity of the theoretical results.

Operation of a mechanically agitated semi-continuous multi-enzymatic reactor by using the Pareto-optimal multiple front method

One essential engineering problem when developing an industrial enzymatic process concerns the choice of the reactor operating alternative based on à-priori knowledge of the process kinetics and enzyme inactivation characteristics. For a multi-enzymatic system, involving complex interactions among enzymes that exhibit optimal activity on different parametric domains, and a high-order deactivation, this problem requires an extended analysis. The optimization and control engineering problems become very challenging due to the process high nonlinearity and the presence of a large number of complex constraints. All these will translate into a multi-objective optimization problem to be solved for each case. An elegant option developed in this paper is to obtain sets of Pareto optimal solutions, also called Pareto-optimal fronts, successively generated for pairs of adverse objectives. Then, the final choice of the enzymatic reactor operating policy results from the comparative analysis of these Pareto-fronts. Exemplification is made for the complex case of the oxidation of D-glucose (DG) to 2-keto-d-glucose (kDG) in the presence of P2Ox (Pyranose oxidase, EC 1.1.3.10) and catalase, continuously operated in a three-phase mechanically agitated semibatch reactor (MASCR) with co-immobilized enzymes on alginate beads. Optimal operating policy choice is based on the minimum amount of required P2Ox that ensures an imposed reaction conversion and maximum reactor productivity under various technological constraints, and for catalase/P2Ox ratios of 200–300U/U.

Proposal of novel hybrid-MEID mechanism for shock response control -a method with time-difference-operation of passive and active motions

Several mechanisms have been proposed to solve shock response control problems in vehicle engineering. The momentum exchange impact damper (MEID) mechanism has been considered for solving shock response control problems in spacecraft landing systems. The effectiveness of MEIDs against the shock responses of a running cart collision problem was investigated using simulation. A novel hybrid MEID mechanism is proposed by considering the time-difference operation of both passive and active MEIDs to optimize the actuator parameters. In this time-difference hybrid MEID (TDHMEID), the amount of passive momentum exchange can be easily identified. Therefore, TDHMEID can be used to set the optimal parameters for active actuator operation. The effectiveness of the TDHMEID mechanism on controlling the shock response and saving actuator energy is verified by simulation.

Fuzzy Dynamic Equations on Time Scales under Generalized Delta Derivative via Contractive-like Mapping Principles

Background/Objectives: The first order nonlinear Fuzzy Dynamic Equations (FDEs) play an important role in recent years to model the dynamic systems with uncertainties and vagueness. The present work deals with obtaining the existence and uniqueness criteria for nonlinear FDEs on time scales which provides a foundation for the nonlinear studies in the field of FDEs. Methods/Statistical Analysis: In place of Banach contraction principle, contractive-like mapping principles on partial ordered sets is used as a tool to study the FDEs on time scales under generalized delta derivative. Findings: The nonlinear FDEs on time scales using generalized delta derivative are not studied so far. The generalized delta derivative is based on four forms which allow us to obtain new solutions for FDEs with decreasing length of their support. Moreover, the differentiability in third and fourth forms is linked with the concept of switching points. These results include both continuous and discrete FDEs under one framework. Application/Improvements: These results are useful to study qualitative and quantitative properties for nonlinear FDEs on time scales which arise in biological, economical and control engineering problems.

Guaranteed Cost Control of Uncertain Discrete Time Systems Subjected to Actuator Saturation via Homogenous Polynomial Lyapunov Function

Estimation of an optimal controller is a fundamental problem in control engineering and is widely known as Optimization. Numerous computation and numerical techniques have been evolved during the past years for the estimation of the optimal solution. Optimal control of a discrete-time system is concerned with optimizing a given objective function using “Homogenous Polynomial Lyapunov Function (HPLF)”. This research focuses upon the design of optimal guaranteed cost controller (GCC) for discrete-time uncertain system using HPLF. The uncertainties are assumed to be norm bounded uncertainties. The effect of actuator saturation is also incorporated in the system. Sufficient conditions for the existence of HPLF are derived in term of Linear Matrix Inequalities (LMI). The LMI approach has the advantage that it can be solved efficiently using Convex Optimization. LMI’s combined with HPLF helps to design the guaranteed cost controller which minimizes the cost by minimizing cost function. Furthermore, the state trajectories and the invariant set is also shown to observe the overall performance.

A diversity control mechanism in many objective optimizations

Multi-objective evolutionary optimization algorithms are among the best optimizers for solving problems in control systems, engineering and industrial planning. The performance of these algorithms degrades severely due to the loss of selection pressure exerted by the Pareto dominance relation which will cause the algorithm to act randomly. Various recent methods tried to provide more selection pressure but this would cause the population to converge to a specific region which is not desirable. Diversity reduction in high dimensional problems which decreases the capabilities of these approaches is a decisive factor in the overall performance of these algorithms. The novelty of this paper is to propose a new diversity measure and a diversity control mechanism which can be used in combination to remedy the mentioned problem. This measure is based on shortest Hamiltonian path for capturing an order of the population in any dimension. In order to control the diversity of population, we designed an adaptive framework which adjusts the selection operator according to diversity variation in the population using different diversity measures as well as our proposed one. This study incorporates the proposed framework in MOEA/D, an efficient widely used evolutionary algorithm. The obtained results validate the motivation on the basis of diversity and performance measures in comparison with the state-of-the-art algorithms and demonstrate the applicability of our algorithm/method in handling many-objective problems. Moreover, an extensive comparison with several diversity measure algorithms reveals the competitiveness of our proposed measure.

Frequency Interval Cross Gramians for Linear and Bilinear Systems

AbstractIn many control engineering problems, it is desired to analyze the systems at particular frequency intervals of interest. This paper focuses on the development of frequency interval cross gramians for both linear and bilinear systems. New generalized Sylvester equations for calculating the frequency interval cross gramians are derived in order to be used to obtain information regarding controllability and observability within a single matrix. The advantage of the proposed method is that it is computationally more efficient compared to existing gramian‐based techniques since only half of the number of equations need to be solved in order to obtain information regarding the controllability and observability of a system compared to existing techniques. Numerical examples are provided to demonstrate the computational efficiency of the proposed method which uses frequency interval cross gramians relative to existing methods.

Robust output tracking of uncertain nonlinear systems with completely unknown dead‐zone input: A self‐tuning design approach

SummaryThe main purpose of this paper is to propose a direct and simple approach, called a self‐tuning design approach, to dealing with any nonsymmetric dead‐zone input nonlinearity where its information is completely unknown. In order to describe the approach, the output tracking problem is considered for a class of uncertain nonlinear systems with any nonsymmetric dead‐zone input. First, a dead‐zone input is represented as a time‐varying input‐dependent function such that the considered dynamical system with dead‐zone input can be transfered into an uncertain nonlinear dynamical system subject to a linear input with time‐varying input coefficient. Then, by making use of the self‐tuning design approach, a class of adaptive robust output tracking control schemes with a rather simple structure is synthesized. Thus, the proposed direct and simple self‐tuning design approach can be easily understood by the engineering designers, and the resulting simple adaptive robust control schemes can be well implemented in most practical engineering control problems. By combining the proposed self‐tuning design approach with other control methods, one may expect to obtain a number of interesting results for a rather large class of uncertain nonlinear dynamical systems with dead‐zone in the actuators. Finally,the simulations of some numerical examples are provided to demonstrate the validity of the theoretical results. Copyright © 2016 John Wiley & Sons, Ltd.

A single sensor and single actuator approach to performance tailoring over a prescribed frequency band

Restricted sensing and actuation control represents an important area of research that has been overlooked in most of the design methodologies. In many practical control engineering problems, it is necessitated to implement the design through a single sensor and single actuator for multivariate performance variables. In this paper, a novel approach is proposed for the solution to the single sensor and single actuator control problem where performance over any prescribed frequency band can also be tailored. The results are obtained for the broad band control design based on the formulation for discrete frequency control. It is shown that the single sensor and single actuator control problem over a frequency band can be cast into a Nevanlinna–Pick interpolation problem. An optimal controller can then be obtained via the convex optimization over LMIs. Even remarkable is that robustness issues can also be tackled in this framework. A numerical example is provided for the broad band attenuation of rotor blade vibration to illustrate the proposed design procedures.

Wheel slip control for all-wheel drive electric vehicle with compensation of road disturbances

Development of wheel slip control for ground vehicles with electric powertrain belongs to the one of the most challenging problems in automotive control engineering. The realization of the wheel slip control for anti-lock brake (ABS) and traction control (TC) systems is a more complex task in the case of vehicles designed both for on-road and off-road conditions. In this situation a control strategy must be able to handle different tyre-surface contact dynamics. Within this context, the presented paper introduces the wheel slip control and corresponding ABS algorithm developed for the all-wheel drive sport utility electric vehicle with four individually controlled on-board motors. The proposed paper, in particular, includes: Analysis of state-of-the art solutions for off-road ABS; Description of the developed ABS architecture based on the direct wheel slip control with predictive and reactive wheel torque contributions; Results of ABS operation in the vehicle simulator software with special attention given to the braking on rough surface; Procedure of the system tuning using hardware-in-the-loop technique; Experimental results of the system testing on the vehicle demonstrator in real operational conditions. The theoretical and experimental outcomes have confirmed improved functionality of the developed wheel slip control in terms of vehicle safety and energy efficiency.

Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process

Nowadays, even though cognitive control architectures form an important area of research, there are many constraints on the broad application of cognitive control at an industrial level and very few systematic approaches truly inspired by biological processes, from the perspective of control engineering. Thus, our main purpose here is the emulation of human socio-cognitive skills, so as to approach control engineering problems in an effective way at an industrial level. The artificial cognitive control architecture that we propose, based on the shared circuits model of socio-cognitive skills, seeks to overcome limitations from the perspectives of computer science, neuroscience and systems engineering. The design and implementation of artificial cognitive control architecture is focused on four key areas: (i) self-optimization and self-leaning capabilities by estimation of distribution and reinforcement-learning mechanisms; (ii) portability and scalability based on low-cost computing platforms; (iii) connectivity based on middleware; and (iv) model-driven approaches. The results of simulation and real-time application to force control of micro-manufacturing processes are presented as a proof of concept. The proof of concept of force control yields good transient responses, short settling times and acceptable steady-state error. The artificial cognitive control architecture built into a low-cost computing platform demonstrates the suitability of its implementation in an industrial setup.

LMI Approach for Robust Control of Interval System with Time Delay

Robust control of uncertain time delay system is a challenging problem for control engineers. The problem becomes more difficult when the system is having interval parameters. This paper proposes a method for solving the problem of robust control of an uncertain time delay system with interval parameters. The interval system with time delay has been transformed to a nominal system with time delay. An LMI controller is designed for the nominal system corresponding to the interval system at different values of parametric uncertainties. Then among all the controllers of different cases of parametric uncertainties, determined the controllers which are suitable for all the cases. Then identified the common controllers which are also suitable for the interval system. From the singular value analysis and the worst case gain analysis, the best suitable controller for the nominal system has been determined. The method is illustrated using examples and is seen providing good control performance. The chosen controller is robust for the range of uncertainty associated with the system

Time-series prediction modelling based on an efficient self-organization learning neural network

For solving some process control engineering problems which can be treated as a time-series, a fast and accurate self-organization learning strategy is proposed based on the significance evaluation of hidden neurons with respect to the network output. This approach is introduced to optimize the architecture and parameters of span-lateral inhibition neural network (S-LINN) simultaneously. The insignificant neuron(s) will be pruned automated step by step based on the determination of significance index. The proposed self-organizing approach has been tested on one time-series prediction benchmark problem. Simulation results demonstrate that the proposed method has good exploration and exploitation capabilities in terms of searching the optimal structure and parameters for S-LINN.

906 Application of Set-Based Design Method to Control Engineering Problem

906 Application of Set-Based Design Method to Control Engineering Problem

Distributed Solution of Constrained Optimal Control Problems Using the Alternating Direction Method of Multipliers

AbstractMany control problems in science and engineering can be formulated as optimal control problems (OCP) such as load changes in process control or point‐to‐point motion of industrial robots in a time‐optimal or energy‐optimal way while accounting for physical or security constraints. Hence, an efficient way to handle constrained OCPs is an important topic of research. A promising approach to address this issue is a transformation technique allowing to reformulate an inequality constrained OCP into an equality constrained counterpart. The reformulated OCP reveals to possess a particular structure that is favorable for a decomposition and the application of distributed optimization methods. Motivated by the Augmented Lagrangian approach, the structure of the reformulated OCP can be exploited to derive a decomposition method for splitting up the entire OCP into smaller subproblems. In addition, an algorithm is presented that follows the ideas of the Alternating Direction Method of Multipliers (ADMM) and solves the resulting subproblems in a distributed manner. The approach is applied to a mechatronic example system to demonstrate the performance of the presented method. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions.

Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human ‘player’ to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference learning-based control method. In all experiments, HuTMaC outperforms the other control methods in terms of average delay and variance over delay. The conclusion is that these results add weight to the suggestion that HuTMaC may be a viable alternative, or supplemental method, to approximate optimization for some practical engineering control problems where the optimal strategy is computationally intractable.

Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

Control engineering problems are generally multi-objective problems; meaning that there are several specifications and requirements that must be fulfilled. A traditional approach for calculating a solution with the desired trade-off is to define an optimisation statement. Multi-objective optimisation techniques deal with this problem from a particular perspective and search for a set of potentially preferable solutions; the designer may then analyse the trade-offs among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are discussed. Throughout this paper it is noticeable that EMO research has been developing towards different optimisation statements, but these statements are not commonly used in controller tuning. Gaps between EMO research and EMO applications on controller tuning are therefore detected and suggested as potential trends for research.

Tracking Control System Design Based on Adaptive Neural Network

The implicit function theorem and the mean value theorem non-affine function of the system into an affine form, and then RBF neural network approximation virtual control and actual control signals desired. Finally Lyapunov functional theory and backstepping method are used to design a self-adapt tracking control scheme. The proposed control scheme can guaranteed to achieve better tracking performance, thus avoiding the problems caused by the high-gain control affine term sector caused by design problems and overcome loop controller. In order to solve practical engineering control problems provides a theoretical basis and can learn from the ideas, and finally through numerical examples to demonstrate the effectiveness of the proposed method.

Modeling and control of a self-sensing polymer metal composite actuator

An ion polymer metal composite (IPMC) is an electro-active polymer (EAP) that bends in response to a small applied electrical field as a result of mobility of cations in the polymer network and vice versa. One drawback in the use of an IPMC is the sensing problem for such a small size actuator. The aim of this paper is to develop a physical model for a self-sensing IPMC actuator and to verify its applicability for practical position control. Firstly, ion dynamics inside a polymer membrane is investigated with an asymmetric solution in the presence of distributed surface resistance. Based on this analysis, a modified equivalent circuit and a simple configuration to realize the self-sensing IPMC actuator are proposed. Mathematical modelling and experimental evaluation indicate that the bending curvature can be obtained accurately using several feedback voltage signals along with the IPMC length. Finally, the controllability of the developed self-sensing IPMC actuator is investigated using a robust position control. Experimental results prove that the self-sensing characteristics can be applied in engineering control problems to provide a more convenient sensing method for IPMC actuating systems.