DC motors are used in numerous industrial applications like servo systems and speed control applications. For such systems, the Proportional+Integral+Derivative (PID) controller is usually the controller of choice due to its ease of implementation, ruggedness, and easy tuning. All the classical methods for PID controller design and tuning provide initial workable values for , , and which are further manually fine-tuned for achieving desired performance. The manual fine tuning of the PID controller parameters is an arduous job which demands expertise and comprehensive knowledge of the domain. In this research work, some metaheuristic algorithms are explored for designing PID controller and a comprehensive comparison is made between these algorithms and classical techniques as well for the purpose of selecting the best technique for PID controller design and parameters tuning.
PID CONTROLLER DESIGN FOR CONTROLLING DC MOTOR SPEED USING MATLAB ....pdfl
In this modern industrial age, there is hardly any industrial application in which DC motors are not being used [1, 2]. This is so because of ease of control, low cost maintenance especially of brushless DC motor type, low price, and ruggedness of DC motor over a wide range of applications. Some industrial applications, which are worth mentioning, in which DC motors are being used widely are machine tools, paper mills, textile industry, electric traction, and robotics. The flexibility in controller design of DC motors is due to the fact that armature winding and field winding could be controlled separately [3]. In most of the applications of speed control of DC motors, the current in field winding is kept constant and the current in armature winding is varied or vice versa which gives excellent speed control performance over a wide range of desired values. In these applications, the purpose is to track the speed command by keeping output speed at desired level and to achieve desire speed or position control in minimum time without having large overshoots and settling times [4, 5].
There have been previously many attempts for the nature inspired PID controller tuning; however, according to the best of our knowledge there has been a little work done in exploiting the power of hybrid techniques for the PID controller tuning. In this work, a PID controller design for speed control of DC motor is presented. First, the design through classical techniques like Zeigler-Nichols and Cohen-Coon methods is presented for establishing a base line. Then, six metaheuristic optimization algorithms are used to find the best possible parameters of PID controller subjected to minimization of a cost function and among these three of the hybrid techniques are used to establish the superiority of hybrid metaheuristic techniques over the others. A comprehensive comparison is made between the classical techniques and the metaheuristic techniques to show the strength, stability, and efficiency of these methods over the classical techniques. Although some stochastic algorithms have been used [13, 16] previously for design of PID controller for different applications, this study presents some hybrid techniques by combining the global and local search techniques of swarm intelligence and evolutionary algorithms for PID controller design and it could provide a framework of PID controller tuning by considering the hybrid nature of metaheuristic techniques.
Ziegler and Nichols proposed a rule for design and tuning of PID controller. From the open loop step response of system one can find the following set of points and . The first point corresponds to output of step response at a value of 35.3% and the second point corresponds to output of step response at a value of 85.3% [20]. By using this method, we get open loop step response values of DC motor given as follows: and dc gain is
By using the above values of , , and , PID controller was designed and the closed loop response of this controller with the plant is shown in Figure 3 and also the output values of , , and steady state error are shown in Table 6.
The same problem is also solved by six metaheuristic algorithms, namely, GA, PSO, SA, GA-NM, PSO-NM, and SA-NM, for designing and tuning of PID controller. Not only the global search but also the hybrid searching techniques using the Nelder-Mead algorithm as the local minimization search technique are also applied. The purpose is to study the optimization capabilities of these metaheuristic algorithms and to demonstrate that by using these techniques PID controller design and tuning give the more accurate and better results. There might be many possible objective functions like integral of time-absolute-error (ITAE), integral of absolute-error (IAE), integral of time-weighted-squared-error (ITSE), and integral of squared-error (ISE), but the objective function used in these algorithms for the minimization is defined as follows [24]:
A multiple setpoint command is applied to the system tuned by SA-NM, Z-N, and C-C. The response to multiple setpoint is also shown in Figures 8 and 9. It is quite obvious from Figure 9 that the multiple setpoint tracking is excellent when PID controller is tuned using SA-NM. In this work, a simplified linear model of the DC motor is considered neglecting the nonlinearities like backlash, dead zone, and effects of load torque changes.
The major challenges in applying a conventional speed controller in DC motor are the effects of motor non-linearity. The non-linear characteristics of a DC motor like, saturation and friction could degrade the performance of conventional controllers. The parameters of such a dynamic system changes with time and drive the system beyond the stability margins. The conventional feedback control system thereby fails to maintain the control especially when the plant parameters are unknown. To overcome these problems, an adaptive control system is proposed which can cope up with the changes in motor dynamics. The control scheme used here is the model reference adaptive system (MRAS) where the output of the unknown plant is tuned to track the output of the ideal reference model. The perfect adaptation is achieved by an adaptive estimator implemented based on MIT rule. The plant output is stabilized by an auto-PID controller (PID controller that tunes its parameters by its own) along with the adaptive estimator. The adaptation mechanism modulates the controller and update the controller parameters to minimize error and track the ideal output. The entire proposed system is modelled and simulated in MATLAB, SIMULINK. The results are analyzed and compared over conventional PI control scheme as a part of the study. The proposed system showed better resistance to the forced perturbations induced, with good decay ratio and fine settling. The system showed satisfactory results when operated in low, medium and high speeds. The motive of the thesis is to implement a self-adaptive and autonomous DC motor speed control for variable orbit tracking applications in robotics, launch vehicles, space probes, satellites, unmanned rovers etc.
B. R. Vinod received his B.E. degree in electronics and communication engineering from Bharathiar University, India, in 1993, and an M.E. degree in control system from Anna University, Chennai, India, in 2008 and a Ph.D degree from University of Kerala, India, in 2019. From 1999 to 2004, he was with the Kerala State Electricity Board, India. Since 2004, he has been a faculty member with the Department of Electronics and Communication Engineering, College of Engineering Trivandrum, Kerala, India. His research interests include multilevel inverter fed induction motor drives, vector controlled motor drives and controller design techniques. 2ff7e9595c
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