Rule-based PI controller autotuning for drive systems

Tuning a PI controller can be quite cumbersome for the non-expert as the closed-loop control system has to meet various requirements while the influence and interaction of the two degrees of freedom are not always clear. This paper addresses the design of an iterative PI controller autotuning for drive systems with the idea of imitating a human expert. In contrast to existing concepts, a new approach with multiple tuning strategies is applied which gives a compact rule base that is easy to modify. The performance of the proposed algorithm is illustrated through motion control testbench trials

New Algorithm for the Smoothing Speed Control of Induction Motor in Electric Car based on Self-Tuning Parameter PID-Fuzzy Logic

Driving system of electric car for low speed has a performance of controller that is not easily set up on large span so it does not give a comfort to passengers. The study has been tested in the bumpy road conditions, by providing disturbances in the motor load, it is to describe the condition of the road. To improve the system performance, the speed and torque controller was applied using Field Oriented Control (FOC) method. In this method, On-Line Proportional Integral Derivative Fuzzy Logic Controller (PID-FLC) is used to give dynamic response to the change of speed and maximum torque on the electric car and this results the smooth movement on every change of car performance both in fast and slow movement when breaking action is taken. Optimization of membership functions in Fuzzy PID controller is required to obtain a new PID parameter values which is done in autotuning in any changes of the input or disturbance. PID parameter tuning in this case using the Ziegler-Nichols method based on frequency response. The mechanism is done by adjusting the PID parameters and the strengthening of the system output. The test results show that the controller Fuzzy Self-Tuning PID appropriate for Electric cars because they have a good response about 0.85% overshoot at to changes in speed and braking of electric cars

Fuzzy adaptive control system of a non-stationary plant with closed-loop passive identifier

Abstract Typically chemical processes have significant nonlinear dynamics, but despite this, industry is conventionally still using PID-based regulatory control systems. Moreover, process units are interconnected, in terms of inlet and outlet material/energy flows, to other neighboring units, thus their dynamic behavior is strongly influenced by these connections and, as a consequence, conventional control systems performance often proves to be poor. This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller, also exploiting the results coming from an identification procedure that is carried on when an unmeasured step disturbance of any shape affects the process behavior. In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective

Robust controller design: Recent emerging concepts for control of mechatronic systems

The recent industrial revolution puts competitive requirements on most manufacturing and mechatronic processes. Some of these are economic driven, but most of them have an intrinsic projection on the loop performance achieved in most of closed loops across the various process layers. It turns out that successful operation in a globalization context can only be ensured by robust tuning of controller parameter as an effective way to deal with continuously changing end-user specs and raw product properties. Still, ease of communication in non-specialised process engineering vocabulary must be ensured at all times and ease of implementation on already existing platforms is preferred. Specifications as settling time, overshoot and robustness have a direct meaning in terms of process output and remain most popular amongst process engineers. An intuitive tuning procedure for robustness is based on linear system tools such as frequency response and bandlimited specifications thereof. Loop shaping remains a mature and easy to use methodology, although its tools such as Hinf remain in the shadow of classical PID control for industrial applications. Recently, next to these popular loop shaping methods, new tools have emerged, i.e. fractional order controller tuning rules. The key feature of the latter group is an intrinsic robustness to variations in the gain, time delay and time constant values, hence ideally suited for loop shaping purpose. In this paper, both methods are sketched and discussed in terms of their advantages and disadvantages. A real life control application used in mechatronic applications illustrates the proposed claims. The results support the claim that fractional order controllers outperform in terms of versatility the Hinf control, without losing the generality of conclusions. The paper pleads towards the use of the emerging tools as they are now ready for broader use, while providing the reader with a good perspective of their potential

Self-Adaptive Model-Based Control for VAV Systems

In North America one of the main users of primary energy are buildings, where HVAC equipment operation is the largest consumer of total energy by end use. This has triggered the need to develop better active strategies and building technologies for the enhancement of HVAC equipment performance. Great examples of solutions that large commercial and institutional buildings adopted, were the widespread use of Building Automation Systems (BAS), and approaches like Variable Air Volume (VAV) systems for ventilation, which allow for better part load regulation, reduction of energy consumption and building operation costs, without compromising occupant comfort or safety. But despite all these improvements, most BAS still rely on conventional control methods like rule based on-off control paired with Proportional Integral Derivative (PID) loops, which are single input single output (SISO) models that are not suitable for the complexities of the multivariable requirements of building systems. These outdated strategies have been estimated to annually waste up to 30% of building’s energy. To mitigate these issues the research community has strongly endorsed the use of more advanced and proven effective control methods such as Model-Based Control (MBC), in which abundant work has been done for the supervisory level control like optimal start/stop, setpoint reset scheduling, etc. However, little attention has been given to local level control where PID control remains the chief workhorse of HVAC systems. Mainly because of the difficulties of creating models, as well as the lack of research regarding the implementation of mechanisms required for continuous calibration (also known as adaptability) of model parameters as they start to drift away from their initial values due to system changes or deterioration, which challenges the reliability of any MBC approach. For such reasons the present body of work was conceived to design a practical methodology for a self-adaptive MBC and field data driven approach to improve VAV systems energy efficiency, based on the Total Air Volume (TAV) control method by modifying the shortcomings of its modeling, adaptability and control strategy procedures. Using a regular VAV system inside a high-rise institutional building as an experimental testbed for the proof of concept of this methodology. The results of the test demonstrated that the self adaptive field calibrated TAV method can match and exceed the capabilities of PID control, by improving response time, offset, and above all energy efficiency, were an average 56% of energy consumption was achieved in contrast to the conventional duct static pressure PID control

New Algorithm for the Smoothing Speed Control of Induction Motor in Electric Car based on Self-Tuning Parameter PID-Fuzzy Logic

Driving system of electric car for low speed has a performance of controller that is not easily set up on large span so it does not give a comfort to passengers. The study has been tested in the bumpy road conditions, by providing disturbances in the motor load, it is to describe the condition of the road. To improve the system performance, the speed and torque controller was applied using Field Oriented Control (FOC) method. In this method, On-Line Proportional Integral Derivative Fuzzy Logic Controller (PID-FLC) is used to give dynamic response to the change of speed and maximum torque on the electric car and this results the smooth movement on every change of car performance both in fast and slow movement when breaking action is taken. Optimization of membership functions in Fuzzy PID controller is required to obtain a new PID parameter values which is done in autotuning in any changes of the input or disturbance. PID parameter tuning in this case using the Ziegler-Nichols method based on frequency response. The mechanism is done by adjusting the PID parameters and the strengthening of the system output. The test results show that the controller Fuzzy Self-Tuning PID appropriate for Electric cars because they have a good response about 0.85% overshoot at to changes in speed and braking of electric cars

A Method for automatic tuning of PID controller following Luus-Jaakola optimization

Tuning parameters of a robot axis PID controller manually requires resources and expertise. Even a skilled person cannot always produce optimal tuning parameters manually. In addition, even if two robots would be copies of each other and should perform equally well with same tuning parameters, manufacturing tolerances and other physical differences and errors between axes cause them to perform differently with the same parameter settings. Using lower gains to prevent oscillations results in suboptimal performance. A robust autotuning method would increase axis performance, decrease axis tuning expenses and allow finding optimal tuning parameters for each mass-produced axis individually. This thesis investigates suitable machine learning approach for OptoFidelity's OptoDrive servo controller automatic tuning. Integrated squared error was used as a performance index to evaluate the PID controller tuning. Luus-Jaakola optimization was selected from the learning based optimization methods to optimize the tuning of velocity and position PID controllers in OptoDrive. Controller performance achieved with learning based autotuning was compared to results from manual tuning. To speed up the tuning process, a novel method to adjust model based tuning method with results from learning based method was developed. Both autotuning methods were superior to manual tuning of position controller by decreasing the squared error over 90%. They also performed comparably to manual tuning of velocity controller. Significance of the results were tested with two-sample Kolmogorov-Smirnov test

Speed controller design for three-phase induction motor based on dynamic adjustment grasshopper optimization algorithm

Three-phase induction motor (TIM) is widely used in industrial application like paper mills, water treatment and sewage plants in the urban area. In these applications, the speed of TIM is very important that should be not varying with applied load torque. In this study, direct on line (DOL) motor starting without controller is modelled to evaluate the motor response when connected directly to main supply. Conventional PI controller for stator direct current and stator quadrature current of induction motor are designed as an inner loop controller as well as a second conventional PI controller is designed in the outer loop for controlling the TIM speed. Proposed combined PI-lead (CPIL) controllers for inner and outer loops are designed to improve the overall performance of the TIM as compared with the conventional controller. In this paper, dynamic adjustment grasshopper optimization algorithm (DAGOA) is proposed for tuning the proposed controller of the system. Numerical results based on well-selected test function demonstrate that DAGOA has a better performance in terms of speed of convergence, solution accuracy and reliability than SGOA. The study results revealed that the currents and speed of TIM system using CPIL-DAGOA are faster than system using conventional PI and CPIL controllers tuned by SGOA. Moreover, the speed controller of TIM system with CPIL controlling scheme based on DAGOA reached the steady state faster than others when applied load torque