On hybrid intelligence-based control approach with its application to flexible robot system
- E. Khoobjou^{1} and
- A. H. Mazinan^{2}Email author
DOI: 10.1186/s13673-017-0086-5
© The Author(s) 2017
Received: 12 May 2015
Accepted: 16 January 2017
Published: 27 January 2017
Abstract
Flexible robot system is in general taken into real consideration as most important process in a number of academic and industrial environments. Due to the fact that the aforementioned system is so applicable in real domains, the novel ideas with respect to state-of-the-art in outperforming its performance are always valuable. With this purpose, a number of the soft computing techniques can be preferred with reference to the traditional ones to predict and optimize the overall performance of the above-captioned process. The approach proposed here is in fact organized in line with the integration of the fuzzy-based approach in association with the neural networks, in order to enable the process under control to be capable of learning and adapting to be matched, in a number of real environments. It can be shown that the outcomes tolerate the imprecise circumstances, as one of advantages regarding the fuzzy-based approach. In the present investigation, a new hybrid approach is proposed to deal with the arm of flexible robot system through the neural networks, the fuzzy-based approach and also the particle swarm optimization. It should be noted that the objective of the proposed research is to control the claw of robot system including two-degree-of-freedom movable arms. The results indicate that the mean-square error and the root-mean-square error are accurately outperformed with reference to the traditional ones, tangibly.
Keywords
Flexible robot system Intelligence-based control approach Particle swarm optimizationBackground
The main idea of the soft computing is first introduced by Professor Zadeh,^{1} entitled What is Soft Computing?, published in 1981. By its definition, the soft computing is just known as an integration of a number of fields including the fuzzy logic, the neuro-computing techniques, the evolutionary procedures, the genetic computing and the statistical computing algorithms. This field leads to provide the combination of methods that can be used to represent a set of complex systems in modeling of which is impossible or very hard by applying computational regulations of pure mathematics and hard logic to them, whilst the applicable simulations and practical implementations may be conducted by using the soft computing techniques.
Development of the soft computing boundaries [2]
Methods | Presenters | Years |
---|---|---|
Neural network | McCulloch | 1943 |
Evolutionary computing | Rechenberg | 1960 |
Fuzzy logic | Zadeh | 1965 |
Soft computing | Zadeh | 1981 |
Unlike hard computing, the soft computing is well-flexible against imprecision, approximation and partial truth, as well. The importance of using the soft computing techniques is generally clarified, as long as non-linear systems and also complex physical structures can all be modeled more precisely and flexibly, at a lower cost, and, in a shorter period of time, so they match with elite human decisions, in a high correctness percentage. The only noteworthy matter is that the soft computing is not precisely given as a concoction, mixture or combination, while it is considered to be a type of cooperation in which each member moves toward the desirable objective, in its unique way. The main principle in the soft computing is complementation, not competition. Therefore, the above-referenced soft computing is considered to be a foundation emerging in perceptual intelligence [1–3].
The fuzzy-based approach and the neural networks
Regarding the fuzzy-based approach, it should be noted that there are many types of fuzzy-based knowledge methods, in the real world, which indicate vague, imprecise, incorrect, uncertain and obscure behaviors, as well. Computing systems that are based on the classical theory of permutationor two-valued (binary) logic are not able to answer all the questions, which human can answer in general. In many cases, they produce numerous errors even if they have answers. Although, the assumption that the machine operates similarly to human is ideal, it is reasonable to expect the proposed system to realize the significant relationships in a problem (with an acceptable margin of error). It is quite clear that the behavior toward an uncertain problem should be flexible and therefore the fuzzy-based approach is applied to deal with such problems [4].
The most intriguing application of fuzzy-based approach is the interpretation that this science provides for the structure of decisions made by smart beings and human intelligence above all. This logic well indicates why binary logic of classic mathematics is not able to explain and describe imprecise concepts, such as heat and cold, which constitute the basis of many smart decisions.
A group of inputs which are shown as \( x_{k} ,\;k = 1, \ldots ,K \) are applied to the neuron. These inputs, which are totally considered as a vector resemble the signals are sent to the synapses of the nerve cell. Before being applied to summation unit, shown with a Σ, each signal is multiplied by its corresponding weight that represents the power of a biologically synaptic single connection. The summation unit that is slightly similar to the body of biological cell sums all weighted inputs algebraically and produces the output, which is here shown by n representing \( NET \). This procedure may briefly be stated through a vector symbol as is NET = XW.
The particle swarm optimization algorithm
Reviewing the previous activities
The results of applying the soft computing methods in this field are so improving that several studies are presented in the form of authentic investigations. Now, it is considered to be one of the up-to-date interdisciplinary subjects to majors such as control, electronics, computer, mechanics and mechatronics. For instance, the effects of the fuzzy-based concepts can be discussed in the area of control systems of smart robots. The neural network is used to design the control system of a flexible robot system. The research can realize the artificial neural networks in the above-referenced system under control, in order to cope with the extent of a robot’s movement, in a very high precision. As the technology of manufacturing instruments with high processing capabilities marches on, it is possible to apply both algorithms. The presence of the fuzzy-based approach, which increases the flexibility power and tolerance of uncertainty along with the neural networks advantages in terms of learning leads to the fact that the theory of concurrent becomes more powerful and the necessary incentive can be created for the studies of consistent with this idea. The research deals with the hybrid neural-fuzzy-based technique in the area of flexible connections control approaches.
A description of neural-fuzzy-based control technique is presented, as the flexible control approach [9]. In the method presented, the weight factors that fuzzy-based control approach is given by the neural network with recursive nature are considered, while the same control approach operates without having any knowledge of control system’s structure. A structure having four neural networks is introduced as the control system of tracking the position of a connection target with high flexibility. Its structuring is as follows: two neural networks are as the learning section of recursive errors, in order to educate the inverted dynamic correspondence to redefine the output of target position, the third neural network participates in the controlling operation with target function by learning the steepest decent technique and the fourth neural network which consists of two neural networks is applied to implement the online learning section and the appropriate output feedback (on the implementation of a system with minimal fuzzy-based behavior) [13–17].
Researchers named Lin and Lewis developed the fuzzy-based approach and applied to the singular perturbation method for controlling robot flexible arm system. Using this technique, two sub-systems of fast and slow have been created, where with these two sub-systems, automatic damp behavior and path tracking have been implemented [14]. A neural-fuzzy-based structure is proposed to control the target position of a flexible connection controlling the robot [15]. The factor of fuzzy-based control approach is in fact adjusted through a neural network, which is trained by the error post-distribution method. In the method proposed, a fuzzy-based control approach upon the inverted dynamic behavior is used so that tracking and refraction can be achieved under the control of such a system [19]. Moreover, the fuzzy-based in association with the neural networks are proposed to develop a self-organizing neural-fuzzy-based control approach, in order to deal with the tracking of the target position regarding the controlling system. The fuzzy-based regulations are determined and optimized in the control process concerning the method proposed. And the process of changing the membership values is conducted by applying the online neural network [19–29].
The recommended control system
Two consecutive links (i & i + 1) are assumed in this figure. The fuzzy logic control (FLC) of input i includes the angle error connection (e _{ i } = θ _{ di } − θ _{ i }) and also the acceleration signal at the tip of a _{ ti }. The scale factor is related to the tracking error, the acceleration at tip and input torque for FLC of input i. It is respectively equal to k _{ pi }, k _{ ai } and k _{ ti}. Now, the FLC of input i + 1 also follows the procedure of input i and controls its relevant output. It is recommended that scale factors to be normalized in the period of [−1, 1], so the computing process becomes easier.
The radial based function neural network training
Determining neural networks factors by using PSO algorithm
Here, \( X_{i} = \left\{ {x_{i1} , \ldots , x_{in} } \right\}^{T} \in {\text{S}} \) and \( V_{i} = \left\{ {v_{i 1} , \ldots , v_{in} } \right\}^{T} \in {\text{S}} \) are taken, while \( {\text{S}} \subset {\Re } \) is the search-space. Here, \( {\text{X}}_{i} ({\text{t}} + 1) \) is the next position of particle that is updated in accordance with the value of \( V_{i} (t + 1) \). In fact, this procedure is applied to determine \( W_{i} (t + 1) \) from \( W_{i} (t) \) and therefore the whole of weight factors can efficiently be updated.
The configuration of the proposed fuzzy-based approach
As it has been pointed out, designing the multi-variable control approach for a flexible arm with some links, which are in the form of soft and hard connections should benefit from the fuzzy-based approach, in order to have a desirable functionality. In fact, control systems are considered to be in the form of the MIMO. However, the implementation of such systems is difficult and its computations are time-consuming.
Also, a regulation is required for the two-connection arm so that the robot’s claw can be placed in the right position. The applied regulation is given as follows
If (A_{1} is NM) and (δP _{ x } is PS) Then δθ _{1} is NS
Types of movements from fuzzy-based regulations and claw position [1]
δθ _{1} | ||||||||
---|---|---|---|---|---|---|---|---|
A _{ i } | NB | NM | NS | Z | PS | PM | PB | |
NB | PB | PM | PS | Z | NS | NM | NB | |
NM | PB | PM | PS | Z | NS | NM | NB | |
NS | PB | PM | PS | Z | NS | NM | NB | |
Z | Z | Z | Z | Z | Z | Z | Z | |
PS | NB | NM | NS | Z | PS | PM | PB | |
PM | NB | NM | NS | Z | PS | PM | PB | |
PB | NB | NM | NS | Z | PS | PM | PB |
The definitions of robot’s claw movement
Membership function | Definitions |
---|---|
PB | Positive big |
PM | Positive medium |
PS | Positive small |
Z | Zero |
NS | Negative small |
NM | Negative medium |
NB | Negative big |
For a normal structure, the network’s parameters, especially its weight factors, are updated by reducing the gradients. However, here, the process is replaced by the PSO.
The topology of hybridizing fuzzy-based neural network approach
Simulation results
The MSE and, the RMSE and the regression of the proposed fuzzy-based neural network approach
Test data | Training data | All data | |
---|---|---|---|
Regression | 0.99687 | 0.99999 | 0.99999 |
MSE | 281.25e−5 | 9.9841e−5 | 91.353e−5 |
RMSE | 0.053029 | 0.009992 | 0.030255 |
Conclusion and its recommendation
A hybridized idea of fuzzy-based neural network approach has been designed as the proposed control approach, in this investigation. In the approach realized here, the PSO algorithm is used to decrease error and time, in order to generate the neural network factors. The aforementioned network is in fact designed in five layers to control the movement of the robot system under control with two arms, in a completely flexible manner, in which this network can momentarily be trained by moving the rotation ring. By using the Sugeno-type of the fuzzy-based approach, circumstances have been provided for the robot claw to cover even the untrained areas. Although these areas are now limited, arm claw’s ability is upgraded to move, efficiently. This ability is in fact due to the Sugeno-type of the fuzzy-based approach that is fewer than 150 regulations. Moreover, the arm claw is illustrated, in order to achieve the balanced state. This parameter, which is very important to consider in movement control systems, indicates the speed and precision of the claw, in the achieving balanced state. Furthermore, the investigated results can be improved by applying some changes to the internal structures of each fuzzy-based neural network framework. For instance, the neural networks such as the multilayer perceptron (MLP), the Kohonen and other ones can be used instead of the RBFNN. Also, the genetic algorithm (GA), the imperialistic competitive algorithm and other related ones may correspondingly be used instead of the PSO algorithm, in order to determine the key parameters of the neural networks. It can be shown that each of these can cause a variation in the overall outcome of the aforementioned control approach.
Lotfali Askarzadeh (Baku 1920), Known as Lotfalizadeh or Lotfali A. Zadeh, Fuzzy Logic Founder and Professor of the University of Berkeley in California.
Declarations
Authors’ contributions
AHM carried out the proposal of designing the method and its verification. EK carried out the initial procedure of designing the method and its simulations. Both authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
References
- Subudhi B, Morris AS (2009) Soft computing methods applied to the control of a flexible robot manipulator. Appl Soft Comput 9(1):149–158View ArticleGoogle Scholar
- Ahmad I (2013) Optimized intrusion detection mechanism using soft computing techniques. Telecommun Syst 52:2187View ArticleGoogle Scholar
- Mazinan AH (2015) High-precision full quaternion based finite-time cascade attitude control strategy considering a class of overactuated space systems. Human Centric Comput Inf Sci 5:27View ArticleGoogle Scholar
- Martínez R, Castillo O, Aguilar LT (2009) Optimization of interval type-2 fuzzy-based control approaches for a perturbed autonomous wheeled mobile robot using genetic algorithms. Inf Sci 179(13):2158–2174View ArticleMATHGoogle Scholar
- R. Bill V. T. Jackson, translated by M. Alborzi (2007) An introduction to neural networks, third edition. Sharif University of Technology Press, Tehran, p 138Google Scholar
- Huang Y, Lan Y, Thomson SJ, Fang A, Hoffmann WC, Lacey RE (2010) Development of soft computing and applications in agricultural and biological engineering. Comput Electron Agric 71(2):107–127View ArticleGoogle Scholar
- Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Perth, p. 1942–8Google Scholar
- Linkens DA, Nyongesa HO (1994) Learning systems in intelligent control: an appraisal of fuzzy-based -based, neural and genetic algorithm control applications. IEE Proc Control Theory Appl 143(3):367–386MATHGoogle Scholar
- Tian L, Collins C (2005) Adaptive neuro-fuzzy-based control of a flexible manipulator. Mechatronics 15(10):1305–1320View ArticleGoogle Scholar
- Wai RJ, Lee M-C (2004) Intelligent optimal control of single-link flexible robot arm. IEEE Trans Ind Electron 51(1):201–220View ArticleGoogle Scholar
- Talebi HA, Khorasani K, Patel RV (1998) Neural network based control schemes for flexible-link manipulators: simulations and experiments, “neural net works”, special issue. Neural Control Robotics Biol Technol 11:1357–1377Google Scholar
- Moudgal VG, Passino KM, Yourkovitch S (1994) Rule-based control for flexible-link robot. IEEE Trans Control Syst Technol 2(4):393–405View ArticleGoogle Scholar
- Mannani A, Talebi HA (2007) A fuzzy-based Lyapunov-based control strategy for a macro-micro manipulator: experimental results. IEEE Trans Control Syst Technol 15(2):375–383View ArticleGoogle Scholar
- Lin J, Lewis FL (2002) Fuzzy-based control approach for flexible-link robot arm by reduced-order techniques. IEE Proc Control Theory Appl 149(3):177–187View ArticleGoogle Scholar
- Subudhi B, Morris AS (2003) Fuzzy-based and neuro-fuzzy-based approaches to control a flexible single-link manipulator. J Syst Control Eng Proc IMechE 217:387–399View ArticleGoogle Scholar
- Gutierrez LB, Lewis FL, Lowe JA (1998) Implementation of neural network tracking control approach for a single flexible link: comparison with PD and PID control approaches. IEEE Trans Ind Electron 45(2):307–318View ArticleGoogle Scholar
- Tian L, Wang J, Mao Z (2004) Constrained motion control of flexible robot manipulators based on recurrent neural networks. IEEE Trans Syst Man Cybern Part B Cybernetics 34(3):1541–1552View ArticleGoogle Scholar
- Caswara FM, Unbehauen H (2002) A neuro-fuzzy-based approach to the control of a flexible link manipulator. IEEE Trans Syst Man Cybern 18(6):932–944Google Scholar
- Renno JM (2007) Inverse dynamics based tuning of a fuzzy-based control approach for a single link flexible manipulator. J Vibr Control 13(12):1741–1759MathSciNetView ArticleMATHGoogle Scholar
- Sarraf S, Fallah A, Seyedena T (2007) A neuro-fuzzy-based control approach for a single link flexible manipulator, Lecture notes in computer science, Artificial neural networks, ICANN 2007, vol. 4669. Springer, Berlin, pp 621–629Google Scholar
- Lee CC (1990) Fuzzy-based in control systems—part I. IEEE Trans Syst Man Cybern 20(2):404–415View ArticleMATHGoogle Scholar
- Moody J, Darken C (1989) Fast learning in networks of locally-tuned processing units. Neural Comput 1(2):281–294View ArticleGoogle Scholar
- Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Perth, pp 1942–1948Google Scholar
- Mote TP, Lokhande DS (2012) Temperature control system using ANFIS. Int J Soft Comput Eng 2(1):156–161Google Scholar
- Mohammad MJ, Darus IZM (2014) Nonlinear system identification of vortex induced vibration on pipe cylinder. In: Proceedings of the 14th International Conference on Robotics, Control and Manufacturing Technology (ROCOM '14), Kuala Lumpur, Malaysia 23–25 April 2014Google Scholar
- Bazargan A (2012) Design, development, and human analogous control of a climbing robot (Doctoral dissertation, Faculty of Graduate Studies and Research, University of Regina)Google Scholar
- Manjaree S, Agarwal V, Nakra BC (2013) Inverse kinematic using neuro-fuzzy intelligent manipulator. Int J Adv Comput Res 3(4):160–165Google Scholar
- Hsu F-C, Lin MC, Yeh RG (2013) Applied soft computing. Elsevier, Amsterdam, pp 1620–1626Google Scholar
- Perng J-W, Chen G-Y, Hsieh S-C (2014) Optimal PID controller design based on PSO-RBF for wind turbine systems. Energies 7:191View ArticleGoogle Scholar