Towards a formal framework for integrated design-optimization and control of mechatronicsystems

This paper presents work towards a formal framework to support model-based integrated design and optimization of mechatronic products for early-phase conceptual design. This paper describes an integrated design framework through the introduction of its software implementation and a specific use case. The contribution is to introduce mathematical formalism to define the concepts, semantics, computation rules and system architectures of the formal framework. The advantage of the formal definitions is to clearly expose functionality and the limitations of the design framework and facilitate the software implementation. The modelling capability of the framework is enhanced to include non-linear mechatronic components, such as a two degrees-of-freedom arm. Further, an optimal proportional–integral–derivative control component is added to the software library supporting the framework.

Time-Delay Synchronization and Anti-Synchronization of Variable-Order Fractional Discrete-Time Chen–Rossler Chaotic Systems Using Variable-Order Fractional Discrete-Time PID Control

In this research paper, we solve the problem of synchronization and anti-synchronization of chaotic systems described by discrete and time-delayed variable fractional-order differential equations. To guarantee the synchronization and anti-synchronization, we use the well-known PID (Proportional-Integral-Derivative) control theory and the Lyapunov–Krasovskii stability theory for discrete systems of a variable fractional order. We illustrate the results obtained through simulation with examples, in which it can be seen that our results are satisfactory, thus achieving synchronization and anti-synchronization of chaotic systems of a variable fractional order with discrete time delay.

A Mathematical Model for Controlling a Quadrotor UAV

Given the recent surge in interest in UAVs and their potential applications, a great deal of work has lately been done in the field of UAV control. However, UAVs belong to a class of nonlinear systems that are inherently difficult to control. In this study we devised a mathematical model for a PID (proportional integral derivative) control system, designed to control a quadrotor UAV so that it follows a predefined trajectory. After first describing quadrotor flight dynamics, we present the control model adopted in our system (developed in MATLAB Simulink). We then present simulated results for the use of the control system to move a quadrotor UAV to desired locations and along desired trajectories. Positive results of these simulation support the conclusion that a quadrotor UAV spatial orientation control system based on this model will successfully fulfil its task also in real conditions.

A Cascade Proportional Integral Derivative Control for a Plate-Heat-Exchanger-Based Solar Absorption Cooling System

Automatic proportional integral derivative control techniques are applied in a single-stage solar absorption cooling system, showing 3.8 kW (~1 ton) cooling capacity, with a coefficient of performance of 0.6 and −4.1 °C evaporator cooling temperature. It is built with plate heat exchangers as main components, using ammonia–water as the working mixture fluid and solar collectors as the main source of hot water. Control tuning was verified with a dynamical simulation model for a solution regarding mass flow stability and temperature control in the solar absorption cooling system. The controller improved steady thermodynamic state and time response. According to experimental cooling temperatures, the system could work in ranges of refrigeration or air-conditioning end-uses, whose operation makes this control technique an attractive option to be implemented in the solar absorption cooling system.

Precise variable spraying system based on improved genetic proportional-integral-derivative control algorithm

Currently, the most efficient method of resolving the pollution problem of weed management is by using variable spraying technology. In this study, an improved genetic proportional-integral-derivative control algorithm (IGA-PID) was developed for this technology. It used a trimmed mean operator to optimize the selection operator for an improved searching rate and accuracy. An adaptive crossover operator and mutation operator were constructed for a rapid convergence speed. The weed density detection was performed through an image acquisition and processing subsystem which was capable of determining the spraying quantity. The variable spraying control sub-system completed variable spraying operation. The performance of the system was evaluated by simulations and field tests, and compared with conventional methods. The simulation results indicated that the parameters of the overshoot (1.25%), steady-state error (1.21%) and the adjustment time (0.157s) of IGA-PID were the lowest when compared with the standard algorithms. Furthermore, the field validation results showed that the system with the proposed algorithm achieved the optimal performance with spraying quantity error being 2.59% and the respond time being 3.84s. Overall, the variable spraying system based on an IGA-PID meets the real-time and accuracy requirements for field applications which could be helpful for weed management in precise agriculture.

Microfluidic Device Control System Based on Segmented Temperature Sensor

Microfluidic technology refers to the technique of controlling the flow, mass transfer, and heat transfer of a fluid with a volume of picoliter to nanoliter in a low-dimensional channel structure with at least one dimension of micron or even nanometer scale. It is widely used in biochemical analysis, immunity, minimally invasive surgery, and environmental monitoring. This paper proposes a microfluidic device based on a segmented temperature sensor. This device can be used for segmental temperature measurement and controlling the temperature of the solution in the microchannel of a glass microfluidic chip. The device is based on a transparent indium tin oxide film glass as a heating element. It adopts a temperature control platform of a proportional-integral-derivative control algorithm. The system uses a charged coupled device camera, a fluorescence microscope, and an image acquisition card to form a noncontact fluorescent indicator temperature measuring device. The device measures the temperature distribution of the microfluid space with time and controls the microfluidics. Moreover, the device has the advantages of simple structure, low cost, and convenient operation.

Robust Landing Control of a Quadcopter on a Slanted Surface

A robust landing control algorithm is proposed for a quadcopter, as well as for a landing platform to land on an inclined or problematic surface. To use the quadcopter for outdoor application, it is necessary to design a landing platform that can withstand environmental obstacles such as wind and weight load during landing. Conventional retractor landing platforms are not suitable for achieving a stable landing on inclined surfaces or obstacles. Therefore, in this paper, 2-link structured landing legs are applied to stably land on an inclined surface or obstacle with a suitable control algorithm. To achieve stable landing on a slanted surface, a cooperative control algorithm of the quadcopter and the landing platform has been proposed. The proposed robust landing system comprises two controllers, i.e., a high-speed proportional derivative control for the landing platform and a neural network-based proportional–integral–derivative control for controlling the quadcopter in real time. A quadcopter with a robust landing platform has been implemented, and the performance of the robust landing control algorithm has been demonstrated with the system.

Enhanced variable universe fuzzy proportional–integral–derivative control of structural vibration with real-time adaptive contracting–expanding factors

A conventional variable universe fuzzy proportional–integral–derivative control approach is widely used for semi-active control in mechanical engineering. The performance of the controller is dependent on an optimal selection of parameters of the contracting–expanding factors. An improved variable universe fuzzy proportional–integral–derivative control algorithm is developed in this study where these parameters are automatically determined in real-time according to the error in the controlled responses and its change rate based on fuzzy logic control. The proposed method is numerically and experimentally illustrated with a three-story frame structure with a magnetorheological damper. The amplitude of displacement, velocity, and acceleration at all floor levels under the proposed control method are smaller than those obtained from existing proportional–integral–derivative, fuzzy, and conventional variable universe fuzzy methods.

Analysis and Simulation of Proportional Derivative and Proportional Integral Derivative Control Systems Using Xcos Scilab

PID (Proportional Integral Derivative) control is a popular control in the industry and aims to improve the performance of a system. This control has controlling parameters, namely Kp, Ki, and Kd which will have a control effect on the overall system response. In this research, P, PD, and PID control simulations with the transfer function of the mass-damper spring as a plant using Xcos Scilab. The method used is the trial and error method by setting and varying the values of the control constants Kp, Ki, and Kd to produce the desired system response. The value adjustment of system control parameters is carried out with several variations, namely Kp control variation, Kp variation to constant Kd, Kd variation to constant Kp, Kp variation to Ki, constant Kd, variation of Ki to Kp, constant Kd and variation of Kd to Kp, Ki constant. The second method is automatic tuning which is done through mathematical calculations to obtain PID control constants, namely Zieglar Nichols PID tuning with the oscillation method. From the system simulation results, the best parameter is obtained through the Zieglar Nichols PID tuning process based on the results of the transient response analysis, namely when the proportional gain value (Kp) is 50. The system performance characteristics produced in the tuning process are 3.994 seconds of settling time at 2.36 seconds research time. resulting in a maximum overshoot value of 3.6% and a peaktime value of 3.994 seconds