Clutch control of a hybrid electrical vehicle based on neuron-adaptive PID algorithm

Practical and intuitive controller design method for precision positioning of a pneumatic cylinder actuator stage

The flexible manipulators have many industrial applications; but most of the reported works are using electrical or hydraulic actuators. These actuators have a linear behaviour and the control is easier than pneumatic actuators; but the pneumatic position control is a highly non linear problem, due to the air compressibility behaviour and internal friction (Moore & Pu, 1993). Because of these conditions, there are certain difficulties in pneumatics cylinder control design. The main disadvantage of electrical actuators is the low powerweight rate, the high current related with its load and it is heavy. The hydraulic actuators are not ecological, needs hydraulic oil and return lines to the pump is needed. By the other hand, the pneumatic actuators are clean, economy, light, faster, have a great power-weight rate and return lines are not needed. However, pneumatic actuators are not used into flexible manipulators developed due to their highly non linear behaviour. It is important to note that research lines; such as pneumatic control, embedded systems and flexible manipulators, are used in separate way. As a matter of fact, most of manipulators robots use electric or hydraulic actuators; however, the pneumatic actuators are being used in recent years (Ramos at al, 2006a; Ramos et al, 2006b) to control a flexible manipulator arm. This is the beginning of a project which involves the use of a pneumatic cylinder to control a flexible manipulator robot. Our first approach is to use one degree of freedom, but the main goal is to have a two degree of freedom flexible manipulator. Several pneumatic controllers had been developed; for example, the Model Reference Adaptive Control, MRAC (Suarez & Luis, 2005); however, the pneumatic model used for the control design, have the next considerations; a lineal actuator, a lineal valve, without damping systems at the sides, ideal gas, adiabatic changes and constant viscous friction. Other works have been focused in friction parameter identification techniques of cylinder pneumatic (Wang & Wang, 2004), dynamic modelling and simulation (Jozsef & Claude,

Intelligent switching control of pneumatic actuator using on/off solenoid valves

This paper proposed two novel discrete sliding model controllers (DSMC), which based on equivalent control and exponential reaching law respectively. Three different control algorithms, PID, DSMC based on Equivalent control (DSMCEC) and DSMC based on exponential reaching (DSMC-ER) law, are applied to control a pneumatic muscle actuator (PMA) in a position control system. Simulation and experiment methods are used to study characteristics of those control algorithms. Results show that two DSMC control algorithms are both better compared with PID controller in robustness, though they own some strengths and weaknesses.

Pneumatic actuators are attractive for robotic rehabilitation applications because they are lightweight, powerful, and compliant, but their control has historically been difficult, limiting their use. In this paper we present the pneumatic control system developed for Pneu-WREX: a pneumatically actuated, upper extremity orthosis for rehabilitation after stroke. The developed pneumatic control system combines several novel components to make the entire system stable, reliable, and backdrivable. These components, which are described in this paper, include: (1) a unique two-valve force control subsystem that keeps chamber pressure low (to reduce friction and energy consumption) and adaptively compensates for leakage; (2) a new servovalve characterization approach that uses experimentally measured data in a combined non-linear and least-squares regression to obtain a linear relationship between mass flow and valve voltage; and (3) a new approach to state estimation using accelerometers and a Kalman filter to obtain clean signals for use in a non-linear adaptive feedback control law. Experimental testing of the device demonstrates the efficacy of the developed pneumatic control system.

This paper addresses a new methodology for servo pneumatic actuators mathematical modeling and selection from the dynamic behavior study in engineering applications. The pneumatic actuator is very common in industrial application because it has the following advantages: its maintenance is easy and simple, with relatively low cost, self‐cooling properties, good power density (power/dimension rate), fast acting with high accelerations, and installation flexibility. The proposed fifth‐order nonlinear mathematical model represents the main characteristics of this nonlinear dynamic system, as servo valve dead zone, air flow‐pressure relationship through valve orifice, air compressibility, and friction effects between contact surfaces in actuator seals. Simulation results show the dynamic performance for different pneumatic cylinders in order to see which features contribute to a better behavior of the system. The knowledge of this behavior allows an appropriate choice of pneumatic actuator, mainly contributing to the success of their precise control in several applications.

Drawing on the driving mechanism of biological muscles and combining the nonlinear hyperelastic characteristics of silicone rubber, a multi-degree-of-freedom pneumatic soft bionic actuator is designed, which can be used as the executing mechanism for soft robots and robotic arms. Using response surface analysis and numerical simulation algorithms, the optimal combination of structural dimensions parameters is determined with the maximum bending and torsion angles output by the actuator as the optimization objectives. Based on the idea of flexible mechanism Piecewise Constant Curvature (PCC) modeling, the kinematics equation of the actuator is derived. Analyze the equivalent motion structure of the actuator and establish a dynamic model of the actuator based on the Lagrange method. The physical model of the actuator is manufactured using rapid prototyping technology. Finally, experimental testing and analysis are conducted on the physical model to obtain the motion and dynamic characteristics curves and empirical formulas of the actuator, which are compared with theoretical and simulation results to verify that the pneumatic soft bionic actuator is feasible and effective. The above research methods, processes, and results can provide reference and inspiration for the research and implementation of pneumatic and hydraulic soft robots and gripping actuators for soft robotic arms.

This paper proposes a sliding mode law for precise position control with minimal switching activity for a robotic system that uses on/off (solenoid) pneumatic actuators, For a two-chamber pneumatic actuator with four binary solenoid valves, there is a total of sixteen possible input combinations defined directly from the state of the four on/off solenoid valves present in the system. However, only seven of these discrete operating modes are considered both functional and unique. Accordingly, we use a seven-mode sliding controller that minimizes the position error using modes that have both the necessary and sufficient amounts of drive energy and, thus, involve reduced switching activity. An analysis of the closed-loop system stability is carried out. The performance of the proposed control design is experimentally verified on a single pneumatic actuator comprising of two chambers driven by four on/off solenoid valves.

The powertrain performance and comfort of a vehicle are largely dependent on the clutch control strategy for its Automated Manual Transmission (AMT). In most industrial cases, clutch control is managed by clutch pressure control, irrespective of clutch actuation technology. However, clutch pressure control represents a challenge with regard to clutch nonlinearities and time-varying parameters (especially with electromechanical technology). In this paper, the electrohydraulic AMT of a three-wheeled motorcycle is considered. In the context of replacing its electrohydraulic clutch actuator (EHA) by a new electromechanical clutch actuator (EMA) for fuel savings, a dual sliding mode controller (DSMC) is proposed in order to control clutch pressure with the EMA. The clutch and its EMA are modeled through an analytical model including two nonlinear components. The EMA was prototyped and subsequently enclosed in an environmental chamber. Experimental investigations revealed both predicted nonlinearities in the system and allowed identifying several numeric models of the system's dynamics. In keeping with these experimental investigations, the DSMC architecture and algorithm was developed, in which the robustness of the DSMC was proven through a Lyapunov analysis and the original use of the bang-bang component enabled to overcome static friction without introducing chattering. Finally, the test bench setup was used to experimentally highlight controller robustness and performance. Finally, the better control performance and accuracy of the EMA and its DSMC over the current integrated EHA and its associated PI controller were pointed out with respect to four critical clutch operational cases.

In the last decade, robotic systems have penetrated human life more than human can imagine. In particular, the multi-mobile robotic systems have faced a fast growing due to the fact that by deploying a large collection of mobile robots the overall system has a high redundancy and offers the capability of handling more complex tasks. A group of mobile robots increases robustness against failures and provide flexibility to system changes. In order to achieve a fully autonomous operation, the control algorithm to coordinate the robots becomes more important and decisive. The controller has to meet different requirements that sometimes are conflicting. The main goal of this thesis is to evaluate performance of coordination control algorithms for a group of mobile robots. In the first part of this thesis, a modular framework for simulation and experiments of coordination control of mobile robots is presented. All connections between the modules follow the subscriber/publisher paradigm on the exchanged data, i.e. a component publishes data and other components can subscribe on that data. The data is identified by a named magazine, and actual data packets are called issues of the magazine. By means of simulation and experiments, it is demonstrated how the modularity in the framework allows straightforward modification of system configurations, design parameters, and control algorithms. In the second part, hierarchical coordination control of mobile robots is presented. The hierarchy consists of three layers: a high-level control for motion planning of the robots, a low-level control for motion execution, and a flexible layer to accommodate shifting of responsibilities. One advantage of using a hierarchical approach is the isolation of control design in each layer. Changes in the control strategy of a layer do not necessarily require adaptation of other layers. The proposed control algorithms are used to coordinate a group of unicycle mobile robots that realize the transportation system of an automated warehouse. Using the framework, series of simulations with different design parameters are conducted, as well as real-time experiments. Subsequently, performance analysis of the results is carried out. It is shown that the proposed algorithms are flexible to system changes and scalable to variation in the warehouse demands. The algorithms are robust against failures in the transportation systems. It is found that an algorithm, denoted high-level control, with higher throughputs requires more information sharing between the robots. This algorithm is less robust against failures compared to the algorithm, denoted low-level control, that yields lower throughput. In addition, a cost analysis of the proposed control algorithms is given. It is studied that the cost of realizing the transport using a group of mobile robots, coordinated by the proposed control algorithm, has similar performance-to-cost ratio compared to conveyor systems. This conclusion is very promising having in minds that the proposed control algorithm is not necessarily the optimal one. In the third part, the problem of simultaneous tracking of individual references and formation keeping for a group of mobile robots is investigated. The control algorithm is developed using dynamic feedback linearization. The stability proof is analyzed using the theorem on interconnected systems. Using a root-mean-square-like indicator, the trade off between individual tracking and formation keeping, as well as the influences of communication topologies are analyzed. From the analysis of real-time experiment results, it is found that the best formation keeping is obtained when all robots communicate. As a trade off, this requires a larger communication bandwidth and yields large individual tracking errors. Furthermore, the analysis suggests that to achieve optimal individual tracking and formation keeping simultaneously, there is less need to share information between the robots. In the fourth part, as a complement to the hierarchical control approach, a coordination control algorithm based on Model Predictive Control (MPC) is presented. Focusing on the practical aspects, a sequentially decentralized MPC, i.e. a single MPC computes the control signals of all robots where priority rules are used to determine which robots handled earlier in optimization procedure, is implemented. Using the similar automated warehouse environment, the sequentially decentralized MPC is validated in simulations and real-time experiments. For comparison purposes, a centralized MPC, i.e. a single MPC computes the control signals of all robots, is also implemented. Using completion time as indicators, the influences of the MPC parameters are investigated. It is found that the centralized MPC is better than the sequentially decentralizwed MPC, but with the cost of high computation load and limited number of robots in real-time application. Relevance and performance comparison between the MPC and the hierarchical control approach are presented. It is found that regardless of the control algorithm choices, in terms of completion time, a better performance is obtained when the control algorithm uses information from all robots in computing the control signals.

An adaptive controller is presented for a one-degree-of-freedom pneumatic actuator. The control law uses full-state feedback for simultaneous parameter identification and tracking control. For position control, a pneumatic actuator with high bandwidth is difficult to obtain because of the compressibility of air and the nonlinear characteristics of air flowing through a variable area orifice. Most previous controllers for gas powered actuators were relatively limited fixed gain or on-off type controllers with low tracking accuracy. Experimental results demonstrate that tracking performance comparable to electric servomotors can be obtained using the algorithm presented despite the nonlinearities and compressibility of air.

A new concept for pneumatic motion control is discussed that is enabled by magnetorheological (MR) technology. This concept involves placing MR braking devices functionally in parallel with pneumatic actuators. Through closed-loop feedback of a position sensor, accurate and robust motion control is achieved. Furthermore, these systems address many of the problematic issues associated with other pneumatic motion control systems such as: complexity, compliance and sensitivity to air quality. The basic system structure is presented as well as several concepts for MR-pneumatic actuators. A general control structure is proposed that can implicitly accommodate the inherent tradeoff between speed and accuracy in motion control systems. Some laboratory data is presented that explores the behavior of these systems and the nature of this tradeoff. Ideally, this technology will fill a niche between unsophisticated directional control and complex servo control systems.

Soft robots spark a revolution in human–machine interaction. However, developing high‐performance soft actuators remains challenging due to trade‐offs among output force, driving distance, control precision, safety, and compliance. Here, addressing the lack of long‐distance, high‐precision flexible linear actuators, an innovative pneumatic flexible linear actuator (PFLA) is introduced, inspired by the smooth and controlled process observed in snakes ingesting sizable food, such as eggs. This PFLA combines a soft tube, emulating the snake's body cavity, with a pneumatically driven piston. Through the joint modulation of moving resistance and driving force by pneumatic pressure, the PFLA exhibits exceptional motion control capabilities, including self‐holding without pressure supply, smooth low‐speed motion (down to 0.004 m s–1), high‐speed motion (up to 5.6 m s–1) with low air pressure demand, and a self‐protection mechanism. Highlighting its adaptability and versatility, the PFLA finds applications in various settings, including a wearable assistive devices, a manipulator capable of precise path tracking and positioning, and rapid transportation in diverse environments for pipeline inspection and firefighting. This PFLA combines biomimetic principles with sophisticated fluidic actuation to achieve long‐distance, flexible, precise, and safe actuation, offering a more adaptive solution for force/motion transmission, particularly in challenging environments.

This paper describes the application of system identification techniques and robust control strategies to a pneumatic muscle actuator system. Due to the inherent nonlinear and time-varying characteristics of this system, it is difficult to achieve excellent performance using conventional control methods. Therefore, we apply identification techniques to model the system as linear transfer functions and regard the un-modeled dynamics as system uncertainties. Because robust control is well-known for its capability in dealing with system uncertainties, we then apply robust control strategies to guarantee system stability and performance for the system. This work is carried out in three parts. First, the pneumatic muscle actuator system was modeled as linear transfer functions. Second, robust control theorem were utilized to design a Hinf robust controller to deal with system uncertainties and performance requirements. Finally, the designed controller was implemented for experimental verifications and compared with a conventional PID controller. From the experimental results, the proposed Hinf robust controller is deemed effective.

In order to improve the output efficiency of a photovoltaic (PV) energy system, the real-time maximum power point (MPP) of the PV array should be tracked closely. The non-linear and time-variant characteristics of the photovoltaic array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP as in traditional control strategies. A neural fuzzy controller (NFC) in conjunction with the reasoning capability of fuzzy logical systems and the learning capability of neural networks is proposed to track the MPP in this paper. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the NFC. With a derived learning algorithm, the parameters of the NFC are updated adaptively. Experimental results show that, compared with the fuzzy logic control algorithm, the proposed control algorithm provides much better tracking performance.