Adaptive architectures for control of uncertain dynamical systems with actuator and unmodeled dynamics

In adaptive control of uncertain dynamical systems, it is well known that the presence of actuator and/or unmodeled dynamics in feedback loops can yield to unstable closed‐loop system trajectories. Motivated by this standpoint, this paper focuses on the analysis and synthesis of multiple adaptive architectures for control of uncertain dynamical systems with both actuator and unmodeled dynamics. Specifically, we first analyze model reference adaptive control architectures with standard, hedging‐based, and expanded reference models for this class of uncertain dynamical systems and develop sufficient stability conditions. We then synthesize a robustifying term for the latter architecture and analytically show that this term can allow for a relaxed sufficient stability condition. The proposed theoretical treatments involve Lyapunov stability theory, linear matrix inequalities, and matrix mathematics. Finally, we compare the resulting sufficient stability conditions of the considered adaptive control architectures on a benchmark mechanical system subject to actuator and unmodeled dynamics.     

Optimisation of cutting parameters for minimizing carbon emission and maximising cutting quality in turning process

Modern manufacturing systems are faced with the challenge of reducing the carbon emission related to manufacturing technologies. Machining centres consume large amounts of energy and as a consequence; carbon emissions are generated owing to this consumption. This paper presents a design of experiment work related to the optimisation of machining factors in the turning process of aluminium alloys. Carbon emission and surface quality were concurrently optimised. A set of experimental scenarios was set using a Box–Behnken design and the response surface methodology was applied to get the regression model for the carbon emission and surface roughness during turning process. The relationship between factors and the responses (carbon emission and surface quality) was investigated using surface plots. Furthermore, the desirability function method using the Response Optimizer tool in MINITAB and goal programming methodology was used to obtain the values of the parameters that achieved minimum surface roughness and a minimum quantity of carbon emission.     

Micro-end-milling-I. Wear and breakage

Unpredictable tool life and premature tool failure are major problems in micro-machining. In this study, the failure mechanisms of micro-end-mills were studied during the machining of aluminum, graphite electrodes and mild steel workpieces. Hundreds of machining operations were performed, and the pictures of cutting edges were taken with a scanning electron microscope to identify fatigue and extensive stress-related failure mechanisms. Also, the cutting force variation was monitored, i.e. the relationship between the utilization-related changes at the tool structure (wear), and the outcomes (increasing cutting force which means raising stress on the tiny shaft). Inspection of the cutting force variation patterns of large numbers of micro-end-mills indicated that tool failure occurs with chip clogging, fatigue and wear-related excessive stress depending on the characteristics of the workpiece. Two tool breakage prediction methods were developed by considering the variation of the static part of the feed direction cutting force. These methods used segmental averages and wavelet transformation coefficients. The accuracy of the proposed approaches were tested with experimental data and the agreement between the predictions and actual observations are reported.     

Forced convection heat transfer and pressure drop for a horizontal cylinder with vertically attached imperforate and perforated circular fins

In this study, the effect of holes placed on perforated finned heat exchangers on convective heat transfer experimentally investigated. Six millimeter-diameter holes were opened on each circular fin on a heating tube in order to increase convective heat transfer. These holes were placed on the circular fins in such a way as to follow each other at the same chosen angle. The holes created turbulence in a region near the heating tube surface on the bottom of the fin. Some experiments were then performed to analyze the effect of this turbulence on heat transfer and pressure drop. These experiments were carried out at six different angular locations in order to determine the best angular location. In addition, a perforated finned heater was compared with an imperforate finned heater to observe the differences. In the cases of the Re above the critical value, Nusselt numbers for the perforated finned positions are 12% higher than the Nusselt numbers for the imperforate state. Moreover, a correlation has been obtained between the Re and Nu in the Re number above the critical value and the Re below the critical value. Meanwhile, correlations regarding pressure drops in the flow areas have been obtained.     

Recognition of chatter with neural networks

Chatter deteriorates surface finish, reduces tool life, and damages machine tools. A chatter development prediction procedure is proposed for the cylindrical turning of long slender bars. The procedure uses two synthetically trained neural networks to recognize the harmonic acceleration signals and their frequency, and based on these observations, the future vibration characteristics of the system are estimated. The developed neural networks are capable of identifying 98% of the harmonic signals with over 90% certainty and estimate their frequencies with less than ±5% error from very short data sequences (only 11 sampled points). The accuracy of the neural networks is equivalent to time domain time series method based approaches; however, the proposed procedure can be implemented very quickly by using commercially available neural network hardware and software, and can use the new neural network chips to make the estimations very quickly by using parallel processors. The validity of the chatter prediction procedure is also demonstrated on the experimental data.     

Robust parallel hybrid artificial bee colony algorithms for the multi-dimensional numerical optimization

The Journal of Supercomputing - This study proposes a set of new robust parallel hybrid metaheuristic algorithms based on artificial bee colony (ABC) and teaching learning-based optimization (TLBO)...     

Probabilistic estimation of the reachable set of model reference adaptive controllers using the scenario approach

A fundamental and critical problem for Model Reference Adaptive Control (MRAC) systems is the characterisation of the system response during transients. This problem is strictly related to the estimation of the reachable set (RS) from a fixed set of initial conditions and it is typically tackled using the Lyapunov's direct method. One well-known drawback of this approach is the excessive conservatism in the estimation of the RS. To overcome this limitation the authors propose a novel probabilistic framework where uncertain parameters and control signals are considered as random variables. In this framework the RS design is translated into a stochastic convex optimisation problem. This brings the benefit that (probabilistic) LMIs with reduced conservatism can be worked out. The so-called scenario optimisation approach is then used to solve the stochastic optimisation problem with a-priori specified level of reliability. The novel approach is compared with an existing worst-case approach in determining the RS of MRAC systems in the presence of matched and input uncertainty via simulation studies. The proposed methodology can potentially be a useful tool for the probabilistic analysis and design of a broad category of existing adaptive control systems.     

Development of a new multi-criteria optimization method for engineering design problems

In this study, we developed a new multiple criteria optimization method in the context of engineering design. The design of experiment (DoE) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods are combined to identify an objective function of the selected problem by fitting a polynomial to the experimental data in a multiple linear regression analysis. Then, the regression function is incorporated into a mathematical model with the criteria constraints to determine an optimal criteria set. The proposed method can be used to compare the functionality and results provided by different scenario analyses of a building design optimization problem, which represent design solutions. We applied the DoE–TOPSIS model to solve different multi-criteria design optimization problems using two examples from the literature and obtained satisfactory results. In the examples, the results obtained using the combined DoE–TOPSIS model are almost in agreement with those derived from conventional multi-criteria design optimization methods, which demonstrates the simplicity, usability, and flexibility of the proposed method in solving engineering design problems.     

A new model reference control architecture: Stability,performance, and robustness

In this paper, we develop a new model reference control architecture to effectively suppress system uncertainties and achieve a guaranteed transient and steady‐state system performance. Unlike traditional robust control frameworks, only a parameterization of the system uncertainty given by unknown weights with known conservative bounds is needed to stabilize uncertain dynamical systems with predictable system performance. In addition, the proposed architecture's performance is not dependent on the level of conservatism of the bounds of system uncertainty. Following the same train of thought as adaptive controllers that modify a given reference system to improve system performance, the proposed method is inspired by a recently developed command governor theory that minimizes the effect of system uncertainty by augmenting the input signal of the uncertain dynamical and reference systems. Specifically, a dynamical system, called a command governor, is designed such that its output is used to modify the input of both the controlled uncertain dynamical and reference systems. It is theoretically shown that if the command governor design parameter is judiciously selected, then the controlled system approximates the given original, unmodified reference system. The proposed approach is advantageous over model reference adaptive control approaches because linearity of the uncertain dynamical system is preserved through linear control laws, and hence, the closed‐loop performance is predictable for different command spectrums. Additionally, it is shown that the architecture can be modified for robustness improvements with respect to high frequency content due to, for example, measurement noise. Modifications can also be made in order to accommodate actuator dynamics and retain closed‐loop stability and predictable performance. The main contribution of this paper is the rigorous analysis of the stability and performance of a system utilizing the command governor framework. A numerical example is provided to illustrate the effectiveness of the proposed architecture. Copyright © 2015 John Wiley & Sons, Ltd.