Brain Emotional Learning Based Intelligent Controller and its Application on CSTR AthulAjith & Mija S.J Electrical Engineering Department, NIT Calicut E-mail : [email protected], [email protected] Abstract – In this paper, a bio-inspired intelligent controller computational model, the concept of BELBIC was first namely Brain Emotional Learning Based Intelligent introduced by Lucas et.al in [3]. Controller or BELBIC and its effectiveness as a controller for complex nonlinear dynamical systems is studied. The Further, BELBIC was applied on some intriguing product concentration control of Continuously Stirred Tank systems and results prove its effectiveness as a Reactor (CSTR) is an important control problem because of controller and it‟s capability to adapt to parameter its highly nonlinear dynamics. An attempt is made to verify variations and disturbances [4,5,6]. The efficiency of the suitability of BELBIC for this control problem using one BELBIC for complex industrial process was illustrated sensory signal for the controller. The transient response of in [7]. Real-time implementation of the BELBIC for the developed controller is then improved using BELBIC with interior permanent magnet synchronous motor (IPMSM) two sensory signals. Moreover, the performance of the drives was presented in [8]. Moreover, ability of such a developed controller is compared with that of a 2 DOF PID controller in signal processing has been amply controller. demonstrated in [9]. However, the infancy of this Keywords – BELBIC, Emotional Controller, CSTR, controller in terms of periods for which other control Intelligent Controller. strategies have been studied and implemented, classical and intelligent alike, leaves room for further I. INTRODUCTION implementation and analysis of BELBIC in terms of its performance, adaptability and efficiency. In recent years , application of intelligent and bio- inspired approaches such as Neural Networks, Particle Chemical reactors have been perhaps the most Swarm Optimisation (PSO), Genetic Algorithm (GA) important unit of chemical power plant used for unit etc. have found wide spread acceptance among control operations. CSTR is of immense interest to control engineers. Good results have been regularly obtained for engineers even today and the reasons are manifold. Most even complexreal time systems through controllers importantly, it is a benchmark process control problem based on such approaches. The ability of this class of with complex nonlinear dynamics. The primary controllers to adapt to parameter variations and objective of the control mechanism developed for the disturbances is very much desirable. The latest entrant to CSTR would be to control the product concentration this class is the BELBIC- Brain Emotional Learning irrespective of disturbances. Many control strategies Based Intelligent Controller. BELBIC is a notion have already been used to control CSTR,conventional inspired from the way in which mammalian brain [10]-[12] and intelligent controllers [13]-[15] alike.The process and bring out emotions. product concentration control of CSTR using BELBIC is expected to provide a comprehensive idea on Emotion generation is generally regarded as a low application of BELBIC for nonlinear systems and point level reaction in brain. However, researches have out its effectiveness and simplicity in comparison with pointed out that amygdala, a small structure in medial classical controllers. The performance of BELBIC for temporal lobe of the brain is largely responsible for the CSTR is compared with that of a classical 2DOF PID evaluation of emotional stimuli. The idea of emotional controller. learning was first presented by Moren&Balkenus in [1,2] as a neurologically inspired computational model The paper is organised as follows. Section IIis of amygdala and orbitofrontal cortex. Based on this devoted to shed more light on the concept of BELBIC. ISSN (Print) : 2278-8948, Volume-2, Issue-3, 2013 122 International Journal of Advanced Electrical and Electronics Engineering, (IJAEEE) The third section deals with system descriptionof From Fig 1.b, the action can be either inhibitory or CSTRsystem and application of BELBIC to CSTR. In excitatory. From mathematical view point, this converts section IV, an improved BELBIC is presented with the in to supplementing the final result either through objective of enhancing the transient response of the additions or subtraction respectively. It is seen that CSTR system. Finally, the results are discussed and orbitofrontal part takes care of the latter and amygdala conclusions are drawn in section V. produces signal which aids the final result. Likewise, a plastic connection identifies a connections without any II. BELBIC MODEL adjustable weights and a connection which can learn indicates The neurobiological structure of amygdala and orbitofrontal parts of mammalian brain is found to be There is one „A‟ node for every stimulus including responsible for most of the process associated with thalamic input in amygdala and one „0‟ node for each emotional learning. A mathematical model of this stimulus except for thalamic input in orbitofrontal structure acts as the base for BELBIC. The important cortex. Output of system is given by point to note is that BELBIC is more or less a functional model of the structure and doesn‟t necessarily represent (1) the entire physical structure. Where Ai represent the output of ‘A’ node for i’ th A. Mathematical Model stimulus signal. The model, similar to the original emotional Similarly, thalamic connection is calculated as processing architecture in mammalian brains is divided maximum overall stimulus Si. Unlike other inputs to the into two parts, Orbitofrontal and Amygdala. amygdala, the thalamic input is not projected into the Amygdaloidal part receives input from thalamus and orbitofrontal part and cannot be inhibited. cortical areas where as orbitofrontal part receives inputs from cortical areas and amygdala only [3]. The system (2) also receives an emotional reinforcing signal called reward. The general structure of BELBIC is as given in For each „A‟ node, there is a plastic connection weight Fig 1. „V‟ s. Any input is multiplied with this weight to become the output (Ai) from the node. The „0‟ nodes behave in a similar manner with a connection weight W applied to the input signal to create an output (0i). (3) The connection weight Vi is adjusted proportionally to the difference between reinforcing reward and the activation output of „A‟ nodes. (4) Wi isalso adjusted in a similar fashion (5) (a) Where , are constants and Ej is the apparent outputwhich is actual output minus the effect of thalamic input. It is of great interest that the adjustment of Vi is monotonic in nature. Thus the connection weights of amygdala will never decrease. This is a major drift from the weight updating rules generally encountered. The (b) idea embraced here is that once the controller learns a Fig. 1: Structure of BELBIC model pattern, the same is not to be lost due to an opposing reward or the like. So whenever a result is inappropriate, ISSN (Print) : 2278-8948, Volume-2, Issue-3, 2013 123 International Journal of Advanced Electrical and Electronics Engineering, (IJAEEE) it is the task of orbitofrontal part to inhibit such an simple liquid phase reaction is considered output. This feature is unique to BELBIC structure. In here.Chemical species reacts to form species as fact, there is biological evidence that orbitofrontal shown in Fig 3. All the typical assumptions such as of cortex is found to inhibit the areas it is connected to as constant volume, perfect mixture, equal mass densities explained in [2]. of feed and product streams, uniform coolant The weight updating rule for orbitofrontal part is temperature negligent shaft work and heat losses etc. fairly straight forward and similar. As obvious, the are in order [16] and the primary objective is to control weight can both increase and decrease as needed to track the concentration of . the required inhibition. Thus the amygdaloidal part learns to predict and react to a given reinforcement and once a relation is learned, the subsystem can never unlearn the connection. On the other hand, the 0 nodescompare the expected and received reinforcing signal andinhibits the output of the model should there be a mismatch. The „ and‟terms are constants used to adjust the learning speed.Despite being constants, the values of and are very critical for optimal performance of controller. The aforementioned functional model was developed in Simulink® and is shown in the Fig 2. Fig 3. CSTR The controller will adjust the coolant temperature ( ), which is one of three plant inputs. The others are the concentration of the limiting reactant in the CSTR feed stream ( ) and the temperature of this stream ( ). The flow rate of the feed (input) and product (output) streams is kept constant ( ). The CSTR states are the temperature ( ) and Fig 2. BELBIC in Simulink concentration ( ) of the limiting reactant in the product stream and are assumedto be measurable and can be used for The most important consideration for BELBIC feedback. applied to any system is the number of signals involved and signal conditioning. Every system will have The objective is to control the product independent set of input and output signals including concentration of CSTR from an initial condition of low controller outputs and various errors. Thus the input (~19%) conversion to a desired steady state at 80% signals to amygdala and orbitofrontal cortex of the conversion. The reaction is exothermic (liberates heat) controller are to be properly selected for the system. In and the CSTR temperature must be controlled to prevent fact only these signals need to be changed for various a thermal runaway. systems. The underlying structure of BELBIC can remain the same and hence provides great portability The mathematical model of CSTR is derived using and cost savings in terms of application for various mass balance and energy balance as in [13]. The model systems. of CSTR is given by III. BELBIC FOR CSTR A. CSTR (6) Continuous Stirred Tank Reactor System (CSTR) is a typical chemical reactor system with complex nonlinear dynamic characteristics.