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Hybrid PID Control Algorithms for Nonlinear Process Control

This paper presents modifications of the classical PID control algorithm, implemented by an Adaptive Neuro-Fuzzy Architecture (ANFA). The main goal here is to design a fuzzy PID controller with a flexible structure, adaptive tuning of its parameters and algorithm modifications, which leads to improvement of the system performance. Thus the controlling process and system are prevented from the undesired and non expected changes of the system input signals. The antecedent part of the applied fuzzy rules contains a linear function, similar to the modified discrete equation of the corresponding conventional PID controller. The simulations demonstrate satisfactory results of these performances and implementations applied to a nonlinear plant.

, No. 1, pp. 86-92. Clarke D. W., Mohtadi C. and Tuffs P. S. (1987): Generalized predictive control - 1. The basic algorithm. — Automatica , Vol. 23, No. 2, pp. 137-148. Cutler R. and Ramaker B. (1979): Dynamic matrix control - A computer control algorithm. — Proc. AIChE National Meeting , Houston. Dutka A. and Ordys A. W. (2004): The optimal non-linear generalised predictive control by the time-varying approximation. — Proc. 10-th IEEE Int. Conf. Methods and Models in Automation and Robotics , Międzyzdroje, Poland, pp. 299-303. Grimble M.J. and Ordys A


The growing number of vehicles moving on Polish roads equipped with various types of automatic transmissions prompted the authors of this publication to carry out research to assess the impact of the use of this type of transmission on the fuel consumption of these vehicles. The presented article presents a comparative analysis of the fuel consumption of vehicles of different manufacturers equipped with automatic transmissions moving in specially prepared driving cycles for research purposes. In the developed driving cycle, the vehicle speed was gradually increased every 10 km/h, maintaining each speed for a period of at least 30 seconds in order to stabilize the measurement results. The tests were carried out for various load stages of the drive system. Load differentiation was made by simulating driving in the prepared cycle for simulated different slopes of the road. The tests were carried out on the MAHA MSR500 chassis dynamometer, and the obtained results for the vehicle moving in automatic mode, where the transmission controller selected the gear ratio according to the programmed algorithm, were compared with the vehicle's fuel consumption for the vehicle gear selected by the driver in the driving cycle. The control software used to test the chassis dynamometer allows taking into account the increasing resistance of traffic along with the increase of vehicle speed, which greatly approximates the simulated conditions in the laboratory to the conditions on the road. The main purpose of this publication is to check whether using automatic transmissions or other control algorithm of these gears can achieve a reduction in fuel consumption.


The paper aims at researching and developing an adaptive control system algorithm and its implementation and integration in the control system of the existing unmanned aerial vehicle (UAV). The authors describe the mathematical model of UAV and target function for energy consumption minimisation and possible searching algorithms for UAV optimal control from an energy efficiency perspective. There are two main goals: to minimise energy consumption and to develop and investigate an adaptive control algorithm for UAV traction drive in order to increase energy efficiency.

The optimal control algorithm is based on two target function values, when comparing and generating corresponding control signals. The main advantage of the proposed algorithm is its unification and usability in any electrical UAV with a different number of traction drives, different or variable mass and other configuration differences without any initial manual setup. Any electric UAV is able to move with maximal energy efficiency using the proposed algorithm.


The paper reports the design and tests of the planar autopilot navigation system in the three-degree-of-freedom (3-DOF) plane (surge, sway and yaw) for a ship. The aim of the tests was to check the improved maneuverability of the ship in open waters using the improved nonlinear control algorithm, developed based on the sliding mode control theory for the ship-trajectory tracking problem of under-actuated ships with static constraints, actuator saturation, and parametric uncertainties. With the integration of the simple increment feedback control law, the dynamic control strategy was developed to fulfill the under-actuated tracking and stabilization objectives. In addition, the LOS (line of sight) guidance system was applied to control the motion path, whereas the sliding mode controller was used to emulate the rudder angle and propeller rotational speed control. Firstly, simulation tests were performed to verify the validity of the basic model and the tracking control algorithm. Subsequently, full scale maneuverability tests were done with a novel container ship, equipped with trajectory tracking control and sliding mode controller algorithm, to check the dynamic stability performance of the ship. The results of the theoretical and numerical simulation on a training ship verify the invariability and excellent robustness of the proposed controller, which: effectively eliminates system chattering, solves the problem of lateral drift of the ship, and maintains the following of the trajectory while simultaneously achieving global stability and robustness.


The paper presents results of examination of control algorithms for the purpose of controlling chaos in spatially distributed systems like the coupled map lattice (CML). The mathematical definition of the CML, stability analysis as well as some basic results of numerical simulation exposing complex, spatiotemporal and chaotic behavior of the CML were already presented in another paper. The main purpose of this article is to compare the efficiency of controlling chaos by simple classical algorithms in spatially distributed systems like CMLs. This comparison is made based on qualitative and quantitative evaluation methods proposed in the previous paper such as the indirect Lyapunov method, Lyapunov exponents and the net direction phase indicator. As a summary of this paper, some conclusions which can be useful for creating a more efficient algorithm of controlling chaos in spatially distributed systems are made.


The paper covers the main aspects of designing a low voltage three-phase PWM rectifier for bidirectional AC/DC power flow with unity power factor. A model in Matlab/Simulink environment has been built for a 10kW active rectifier with an LCL filter connected to grid side of the rectifier. The primary goal of the model is to achieve low grid current harmonic content for frequency ranges described in worldwide applicable standards and above. LCL filter parameter design procedure is described in the paper and implemented in the rectifier model to achieve a better power quality with limitations in passive element size. A simple “p-q” theory-based voltage oriented control algorithm is used in the model and described in the present paper. Model performance is characterised by dynamic response, stability and grid parameters during simulation. The simulation results demonstrate that the modelled rectifier system is stable and the grid current harmonic content is low both in the low-and high-frequency ranges.


The article presents the possibility of using self-learning control algorithms to manage subassemblies of an internal combustion engine in order to reduce exhaust emissions to the natural environment. In compression ignition (CI) engines, the issue of emissions mainly concerns two components: particulate matter (PM) and nitrogen oxides (NOx). The work focuses mainly on the possibility of reducing the emission of nitrogen oxides. It is assumed that the particularly problematic points when it comes to excessive emission of harmful substances are the dynamic states in which combustion engines operate constantly. In dynamically changing operating points, it is very difficult to choose the right setting of actuators such as the exhaust gas recirculation (EGR) valve to ensure the correct operation of the unit and the minimum emission of these substances. In the light of the above, an attempt was made to develop a self-learning mathematical model, which can predict estimated emission levels of selected substance basing on current measurement signals (e.g. air, pressure, crankshaft rotational speed, etc.). The article presents the results of the estimation of nitrogen oxides by the trained neural network in comparison to the values measured with the use of a sensor installed in the exhaust system. The presented levels of estimated and measured results are very similar to each other and shifted over time in favour of neural networks, where the information about the emission level appears much earlier. On the basis of the estimated level, it shall be possible to make an appropriate decision about specific settings of recirculation system components, such as the EGR valve. It is estimated that by using the chosen control method it is possible significantly to reduce the emission of harmful substances into the natural environment while maintaining dynamic properties of the engine.

. Earthquake Engineering and Engineering Vibration, Vol. 3, No. 2, pp. 181-193. Johnson, E.A. - Ramallo, J.C. - Spencer, B.F. - Sain, M.K. (1999) Intelligent base isolation systems. Proc., Second World Conf. on Structural Control, Kyoto, Japan, Vol. 1, pp. 367-376. Jung, H.J. - Choi, K.M. - Spencer, B.F. - Lee, I.W. (2006) Application of some semi-active control algorithms to a smart base-isolated building employing MR dampers. Structural Control and Health Monitoring, Vol. 13, No. 2-3, pp. 693-704. Jung, H.J. - Spencer, B.F. - Lee, I.W. (2003) Control of seismically excited

References [1] Li Yin (2007). PID control algorithm and its application in wind speed control . Huazhong University of Science and Technology. (in Chinese) [2] Li Ke, Liu Wangkai, Wang Jun. Experts - Fuzzy PID in the low-speed wind tunnel speed control system. (in Chinese) [3] Sorensen, B.R. (2011). An energy efficient control strategy for fan static pressure difference. In International Conference on Consumer Electronics, Communications and Networks , 16-18 April 2011. IEEE, 4736-4739. [4] Radetic, R.M., Milivojevic, D.R., Pavlov, M. (2010). The new bridge